241 results on '"B. Wiestler"'
Search Results
2. Reconstruction of the Corticospinal Tract in Patients with Motor-Eloquent High-Grade Gliomas Using Multilevel Fiber Tractography Combined with Functional Motor Cortex Mapping
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A. Zhylka, N. Sollmann, F. Kofler, A. Radwan, A. De Luca, J. Gempt, B. Wiestler, B. Menze, A. Schroeder, C. Zimmer, J.S. Kirschke, S. Sunaert, A. Leemans, S.M. Krieg, J. Pluim, Medical Image Analysis, and EAISI Health
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Radiology, Nuclear Medicine and imaging ,Neurology (clinical) - Abstract
BACKGROUND AND PURPOSE: Tractography of the corticospinal tract is paramount to presurgical planning and guidance of intraoperative resection in patients with motor-eloquent gliomas. It is well-known that DTI-based tractography as the most frequently used technique has relevant shortcomings, particularly for resolving complex fiber architecture. The purpose of this study was to evaluate multilevel fiber tractography combined with functional motor cortex mapping in comparison with conventional deterministic tractography algorithms. MATERIALS AND METHODS: Thirty-one patients (mean age, 61.5 [SD, 12.2] years) with motor-eloquent high-grade gliomas underwent MR imaging with DWI (TR/TE = 5000/78 ms, voxel size = 2 × 2 × 2 mm3, 1 volume at b = 0 s/mm2, 32 volumes at b = 1000 s/mm2). DTI, constrained spherical deconvolution, and multilevel fiber tractography-based reconstruction of the corticospinal tract within the tumor-affected hemispheres were performed. The functional motor cortex was enclosed by navigated transcranial magnetic stimulation motor mapping before tumor resection and used for seeding. A range of angular deviation and fractional anisotropy thresholds (for DTI) was tested. RESULTS: For all investigated thresholds, multilevel fiber tractography achieved the highest mean coverage of the motor maps (eg, angular threshold = 60°; multilevel/constrained spherical deconvolution/DTI, 25% anisotropy threshold = 71.8%, 22.6%, and 11.7%) and the most extensive corticospinal tract reconstructions (eg, angular threshold = 60°; multilevel/constrained spherical deconvolution/DTI, 25% anisotropy threshold = 26,485 mm3, 6308 mm3, and 4270 mm3). CONCLUSIONS: Multilevel fiber tractography may improve the coverage of the motor cortex by corticospinal tract fibers compared with conventional deterministic algorithms. Thus, it could provide a more detailed and complete visualization of corticospinal tract architecture, particularly by visualizing fiber trajectories with acute angles that might be of high relevance in patients with gliomas and distorted anatomy. ispartof: AMERICAN JOURNAL OF NEURORADIOLOGY vol:44 issue:3 pages:283-290 ispartof: location:United States status: published
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- 2023
3. Methylation subgroup and molecular heterogeneity is a hallmark of glioblastoma: implications for biopsy targeting, classification and therapy
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J. Gempt, F. Withake, A.K. Aftahy, H.S. Meyer, M. Barz, C. Delbridge, F. Liesche-Starnecker, G. Prokop, N. Pfarr, J. Schlegel, B. Meyer, C. Zimmer, B.H. Menze, and B. Wiestler
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ErbB Receptors ,Cancer Research ,DNA Repair Enzymes ,Oncology ,DNA Copy Number Variations ,Brain Neoplasms ,Biopsy ,Humans ,Prospective Studies ,DNA Methylation ,Glioblastoma ,DNA Modification Methylases ,Phylogeny - Abstract
Intratumoral heterogeneity at the cellular and molecular level is a hallmark of glioblastoma (GB) that contributes to treatment resistance and poor clinical outcome. Little is known regarding epigenetic heterogeneity and intratumoral phylogeny and their implication for molecular classification and targeted therapies.Multiple tissue biopsies (238 in total) were sampled from 56 newly-diagnosed, treatment-naive GB patients from a prospective in-house cohort and publicly available data and profiled for DNA methylation using the Illumina MethylationEPIC array. Methylation-based classification using the glioma classifier developed by Ceccarelli et al. and estimation of the MGMT promoter methylation status via the MGMT-STP27 model were carried out. In addition, copy number variations (CNVs) and phylogeny were analyzed.Almost half of the patients (22/56, 39%) harbored tumors composed of heterogeneous methylation subtypes. We found two predominant subtype combinations: classic-/mesenchymal-like, and mesenchymal-/pilocytic astrocytoma-like. Nine patients (16%) had tumors composed of subvolumes with and without MGMT promoter methylation, whereas 20 patients (36%) were homogeneously methylated, and 27 patients (48%) were homogeneously unmethylated. CNV analysis revealed high variations in many genes, including CDKN2A/B, EGFR, and PTEN. Phylogenetic analysis correspondingly showed a general pattern of CDKN2A/B loss and gain of EGFR, PDGFRA, and CDK4 during early stages of tumor development.(Epi)genetic intratumoral heterogeneity is a hallmark of GB, both at DNA methylation and CNV level. This intratumoral heterogeneity is of utmost importance for molecular classification as well as for defining therapeutic targets in this disease, as single biopsies might underestimate the true molecular diversity in a tumor.
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- 2022
4. P11.22.A Prognostic and predictive relevance of immunohistochemically determined p53 mutation in glioblastoma
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J Kempter, J Gempt, B Wiestler, S E Combs, J Schlegel, F Liesche-Starnecker, and F Schmidt-Graf
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Cancer Research ,Oncology ,Neurology (clinical) - Abstract
Background It can be expected that molecular biomarkers will increasingly affect clinical decisions and lead to the development of more personalized therapies in glioblastoma (GBM) in the future. In several other tumor entities TP53 gene mutation or p53 immunoreactivity (IR) serve as a prognostic marker, significantly affecting overall survival (OS) and progression-free survival (PFS). Such an association has not yet sufficiently been demonstrated in GBM. However, there are known prognostic markers in GBM, notably MGMT promotor methylation (mMGMT) which also serves as an important predictive marker leading to a better response to temozolomide chemotherapy. Our aim was to evaluate retrospectively if p53 mutation determined via immunohistochemistry (IHC) could act as a prognostic or predictive marker in GBM. Material and Methods Tumor samples of 195 treatment-naïve patients with IDHwt GBM that had been stained with the p53 antibody DO-7 were subdivided into 2 different groups by p53 IHC. Samples were considered as p53mut when strong p53 IR was detected in ≥10% of all tumor cells and as p53wt when in Results The frequency of p53mut was 36.4% (71/195). p53mut tumors showed a significantly higher IR with Ki-67 proliferation marker (p=0.005) and p53wt seemed to be associated with multifocal primary tumor localization, though not statistically significant (p=0.107). There was no significant difference between p53wt and p53mut regarding gender, age, extent of resection, adjuvant therapy, occurrence of seizures, mMGMT or ATRX loss. The p53 status was not associated with OS or PFS. Factors that univariately led to significantly longer OS and PFS were younger age, unilateral or unifocal primary tumor localization, gross-total resection, higher Karnofsky Performance Status (KPS), mMGMT and adjuvant treatment via Stupp regimen instead of radiotherapy alone, the latter being significantly better than best supportive care. In multivariate survival analyses only age Conclusion Based on our study, p53 IR has no prognostic or predictive significance in IDHwt GBM. There have been previous studies with similar and others with contradicting results. Remarkable is the discordance of the used IR thresholds between different studies. Further studies should aim to revalidate the staining threshold and improve the concordance between TP53 gene sequencing and p53 IHC in IDHwt GBM.
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- 2022
5. Fully automated analysis combining [
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K J, Paprottka, S, Kleiner, C, Preibisch, F, Kofler, F, Schmidt-Graf, C, Delbridge, D, Bernhardt, S E, Combs, J, Gempt, B, Meyer, C, Zimmer, B H, Menze, I, Yakushev, J S, Kirschke, and B, Wiestler
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Brain Neoplasms ,Multiparametric MRI ,Kirschke ,B. coshared last ,Glioma ,APTw ,Amides ,Magnetic Resonance Imaging ,[18F]-FET-PET ,Glioma progression ,Perfusion ,J. S. and Wiestler ,Positron-Emission Tomography ,Humans ,Tyrosine ,Original Article ,Multiparametric Magnetic Resonance Imaging ,Protons ,Fully automated ,DSC perfusion ,Retrospective Studies - Abstract
Purpose To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma. Material and methods At suspected tumor progression, MRI and [18F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier’s performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. Results In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77–0.93 (sensitivity 0.91, 95% CI 0.81–0.97; specificity 0.71, 95% CI 0.44–0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72–0.9), 0.81 for [18F]-FET-PET (95% CI 0.7–0.89), and 0.81 for expert consensus (95% CI 0.7–0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. Conclusion Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05427-8.
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- 2021
6. PATHOLOGY
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J.-i. Adachi, K. Totake, M. Shirahata, K. Mishima, T. Suzuki, T. Yanagisawa, K. Fukuoka, R. Nishikawa, A. Arimappamagan, N. Manoj, A. Mahadevan, D. Bhat, H. Arvinda, B. Indiradevi, S. Somanna, B. Chandramouli, S. A. Petterson, S. K. Hermansen, R. H. Dahlrot, S. Hansen, B. W. Kristensen, F. Carvalho, S. Jalali, S. Singh, S. Croul, K. Aldape, G. Zadeh, J. Choi, S.-H. Park, S. K. Khang, Y.-L. Suh, S. P. Kim, Y. S. Lee, S. H. Kim, S. Coberly, K. Samayoa, Y. Liu, P. Kiaei, J. Hill, S. Patterson, M. Damore, S. Dahiya, R. Emnett, J. Phillips, D. Haydon, J. Leonard, A. Perry, D. Gutmann, S. Epari, S. Ahmed, M. Gurav, S. Raikar, A. Moiyadi, P. Shetty, T. Gupta, R. Jalali, J. Georges, A. Zehri, E. Carlson, N. Martirosyan, A. Elhadi, J. Nichols, L. Ighaffari, J. Eschbacher, B. Feuerstein, T. Anderson, M. Preul, K. Jensen, P. Nakaji, H. Girardi, F. Monville, S. Carpentier, M. Giry, J. Voss, R. Jenkins, B. Boisselier, V. Frayssinet, C. Poggionovo, A. Catteau, K. Mokhtari, M. Sanson, H. Peyro-Saint-Paul, C. Giannini, T. Hide, H. Nakamura, K. Makino, S. Yano, S. Anai, N. Shinojima, J.-i. Kuroda, T. Takezaki, J.-i. Kuratsu, F. Higuchi, H. Matsuda, K. Iwata, K. Ueki, P. Kim, J. Kong, L. Cooper, F. Wang, J. Gao, G. Teodoro, L. Scarpace, T. Mikkelsen, M. Schniederjan, C. Moreno, J. Saltz, D. Brat, U. Cho, Y.-K. Hong, R. Lober, L. Lu, M. H. Gephart, P. Fisher, M. Miyazaki, H. Nishihara, T. Itoh, M. Kato, S. Fujimoto, T. Kimura, M. Tanino, S. Tanaka, N. Nguyen, G. Moes, J. L. Villano, H. Kanno, Y. Kato, T. Ohnishi, H. Harada, S. Ohue, S. Kouno, A. Inoue, D. Yamashita, S. Okamoto, M. Nitta, Y. Muragaki, T. Maruyama, T. Sawada, T. Komori, T. Saito, Y. Okada, S. B. Omay, J. M. Gunel, V. E. Clark, J. Li, E. Z. E. Omay, A. Serin, L. E. Kolb, R. M. Hebert, K. Bilguvar, K. Ozduman, M. N. Pamir, T. Kilic, J. Baehring, J. M. Piepmeier, C. W. Brennan, J. Huse, P. H. Gutin, K. Yasuno, A. Vortmeyer, M. Gunel, S. Pugh, C. L. Rogers, D. Brachman, W. McMillan, J. Jenrette, I. Barani, D. Shrieve, A. Sloan, M. Mehta, A. Prabowo, A. Iyer, T. Veersema, J. Anink, A. S.-v. Meeteren, W. Spliet, P. van Rijen, T. Ferrier, D. Capper, M. Thom, E. Aronica, T. Chharchhodawala, M. Sable, M. C. Sharma, C. Sarkar, V. Suri, M. Singh, V. Santosh, B. Thota, M. Srividya, K. Sravani, S. Shwetha, A. Arivazhagan, K. Thennarasu, A. Hegde, P. Kondaiah, K. Somasundaram, M. Rao, V. P. Kumar, A. Shastry, R. Narayan, S. Naz, S. Venneti, M. Garimella, L. Sullivan, D. Martinez, A. Heguy, M. Santi, C. Thompson, A. Judkins, Z. Voronovich, L. Chen, K. Clark, M. Walsh, J. Mannas, C. Horbinski, B. Wiestler, T. Holland-Letz, A. Korshunov, A. von Deimling, S. M. Pfister, M. Platten, M. Weller, W. Wick, G. Zieman, C. Dardis, and L. Ashby
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Abstracts ,Cancer Research ,Oncology ,Neurology (clinical) - Published
- 2013
7. OMICS AND PROGNSTIC MARKERS
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K. Adachi, H. Sasaki, S. Nagahisa, K. Yoshida, N. Hattori, Y. Nishiyama, T. Kawase, M. Hasegawa, M. Abe, Y. Hirose, A. Alentorn, Y. Marie, S. Poggioli, H. Alshehhi, B. Boisselier, C. Carpentier, K. Mokhtari, L. Capelle, D. Figarella-Branger, K. Hoang-Xuan, M. Sanson, J.-Y. Delattre, A. Idbaih, S. Yust-Katz, M. Anderson, A. Olar, A. Eterovic, N. Ezzeddine, K. Chen, H. Zhao, G. Fuller, K. Aldape, J. de Groot, N. Andor, J. Harness, S. G. Lopez, T. L. Fung, H. W. Mewes, C. Petritsch, A. Arivazhagan, K. Somasundaram, K. Thennarasu, P. Pandey, B. Anandh, V. Santosh, B. Chandramouli, A. Hegde, P. Kondaiah, M. Rao, R. Bell, R. Kang, C. Hong, J. Song, J. Costello, R. Nagarajan, B. Zhang, A. Diaz, T. Wang, L. Bie, Y. Li, H. Liu, W. F. C. Luyo, M. H. Carnero, M. E. P. Iruegas, A. R. Morell, M. C. Figueiras, R. L. Lopez, C. F. Valverde, A. K.-Y. Chan, J. C.-S. Pang, N. Y.-F. Chung, K. K.-W. Li, W. S. Poon, D. T.-M. Chan, Y. Wang, H.-a. K. Ng, M. Chaumeil, P. Larson, H. Yoshihara, D. Vigneron, S. Nelson, R. Pieper, J. Phillips, S. Ronen, V. Clark, Z. E. Omay, A. Serin, J. Gunel, B. Omay, C. Grady, M. Youngblood, K. Bilguvar, J. Baehring, J. Piepmeier, P. Gutin, A. Vortmeyer, C. Brennan, M. N. Pamir, T. Kilic, B. Krischek, M. Simon, K. Yasuno, M. Gunel, A. L. Cohen, M. Sato, K. D. Aldape, C. Mason, K. Diefes, L. Heathcock, L. Abegglen, D. Shrieve, W. Couldwell, J. D. Schiffman, H. Colman, Q. G. D'Alessandris, T. Cenci, M. Martini, L. Ricci-Vitiani, R. De Maria, L. M. Larocca, R. Pallini, B. Theeler, F. Lang, G. Rao, M. Gilbert, E. Sulman, R. Luthra, K. Eterovic, M. Routbort, R. Verhaak, G. Mills, J. Mendelsohn, F. Meric-Bernstam, A. Yung, K. MacArthur, S. Hahn, G. Kao, R. Lustig, M. Alonso-Basanta, S. Chandrasekaran, E. P. Wileyto, E. Reyes, J. Dorsey, K. Fujii, K. Kurozumi, T. Ichikawa, M. Onishi, J. Ishida, Y. Shimazu, B. Kaur, E. A. Chiocca, I. Date, C. Geisenberger, A. Mock, R. Warta, C. Schwager, C. Hartmann, A. von Deimling, A. Abdollahi, C. Herold-Mende, O. Gevaert, A. Achrol, S. Gholamin, S. Mitra, E. Westbroek, J. Loya, L. Mitchell, S. Chang, G. Steinberg, S. Plevritis, S. Cheshier, J. Xu, S. Napel, G. Zaharchuk, G. Harsh, D. Gutman, C. Holder, R. Colen, W. Dunn, R. Jain, L. Cooper, S. Hwang, A. Flanders, D. Brat, J. Hayes, A. Droop, H. Thygesen, M. Boissinot, D. Westhead, S. Short, S. Lawler, P. Bady, S. Kurscheid, M. Delorenzi, M. E. Hegi, C. Crosby, C. Faulkner, T. Smye-Rumsby, K. Kurian, M. Williams, K. Hopkins, A. Palmer, H. Williams, C. Wragg, H. R. Haynes, K. M. Kurian, P. White, T. Oka, L. Jalbert, A. Elkhaled, R. Jensen, K. Salzman, M. Schabel, D. Gillespie, M. Mumert, B. Johnson, T. Mazor, M. Barnes, S. Yamamoto, H. Ueda, K. Tatsuno, K. Aihara, A. Bollen, M. Hirst, M. Marra, A. Mukasa, N. Saito, H. Aburatani, M. Berger, B. Taylor, S. Popov, A. Mackay, W. Ingram, A. Burford, A. Jury, M. Vinci, C. Jones, D. T. W. Jones, V. Hovestadt, S. Picelli, W. Wang, P. A. Northcott, M. Kool, G. Reifenberger, T. Pietsch, M. Sultan, H. Lehrach, M.-L. Yaspo, A. Borkhardt, P. Landgraf, R. Eils, A. Korshunov, M. Zapatka, B. Radlwimmer, S. M. Pfister, P. Lichter, A. Joy, I. Smirnov, M. Reiser, W. Shapiro, S. Kim, B. Feuerstein, C. Jungk, S. Friauf, A. Unterberg, T. A. Juratli, J. McElroy, W. Meng, A. Huebner, K. D. Geiger, D. Krex, G. Schackert, A. Chakravarti, T. Lautenschlaeger, B. Y. Kim, W. Jiang, J. Beiko, S. Prabhu, F. DeMonte, R. Sawaya, D. Cahill, I. McCutcheon, C. Lau, L. Wang, K. Terashima, S. Yamaguchi, M. Burstein, J. Sun, T. Suzuki, R. Nishikawa, H. Nakamura, A. Natsume, S. Terasaka, H.-K. Ng, D. Muzny, R. Gibbs, D. Wheeler, X.-q. Zhang, S. Sun, K.-f. Lam, K. M. Y. Kiang, J. K. S. Pu, A. S. W. Ho, G. K. K. Leung, F. Loebel, W. T. Curry, F. G. Barker, N. Lelic, A. S. Chi, D. P. Cahill, D. Lu, J. Yin, C. Teo, K. McDonald, A. Madhankumar, C. Weston, B. Slagle-Webb, J. Sheehan, A. Patel, M. Glantz, J. Connor, C. Maire, J. Francis, C.-Z. Zhang, J. Jung, V. Manzo, V. Adalsteinsson, H. Homer, B. Blumenstiel, C. S. Pedamallu, E. Nickerson, A. Ligon, C. Love, M. Meyerson, K. Ligon, L. E. Jalbert, S. J. Nelson, A. W. Bollen, I. V. Smirnov, J. S. Song, A. B. Olshen, M. S. Berger, S. M. Chang, B. S. Taylor, J. F. Costello, S. Mehta, B. Armstrong, S. Peng, A. Bapat, M. Berens, B. Melendez, M. Mollejo, P. Mur, T. Hernandez-Iglesias, C. Fiano, J. Ruiz, J. A. Rey, V. Stadler, A. Schulte, K. Lamszus, C. Schichor, M. Westphal, J.-C. Tonn, O. Morozova, S. Katzman, M. Grifford, S. Salama, D. Haussler, A. Olshen, S. Fouse, S. Nakamizo, T. Sasayama, H. Tanaka, K. Tanaka, K. Mizukawa, M. Yoshida, E. Kohmura, P. Northcott, D. Jones, S. Pfister, R. Otani, S. Takayanagi, K. Saito, S. Tanaka, M. Shin, T. Ozawa, M. Riester, Y.-K. Cheng, J. Huse, K. Helmy, N. Charles, M. Squatrito, F. Michor, E. Holland, M. Perrech, L. Dreher, G. Rohn, R. Goldbrunner, M. Timmer, B. Pollo, V. Palumbo, C. Calatozzolo, M. Patane, R. Nunziata, M. Farinotti, A. Silvani, S. Lodrini, G. Finocchiaro, E. Lopez, A. Rioscovian, R. Ruiz, G. Siordia, A. P. de Leon, C. Rostomily, R. Rostomily, D. Silbergeld, D. Kolstoe, M. Chamberlain, J. Silber, P. Roth, A. Keller, J. Hoheisel, P. Codo, A. Bauer, C. Backes, P. Leidinger, E. Meese, E. Thiel, A. Korfel, M. Weller, G. Nagae, M. Nagane, J. Z. Sanborn, T. Mikkelsen, S. Jhanwar, L. Chin, M. Nishihara, M. Schliesser, C. Grimm, E. Weiss, R. Claus, D. Weichenhan, M. Weiler, T. Hielscher, F. Sahm, B. Wiestler, A.-C. Klein, J. Blaes, C. Plass, W. Wick, G. Stragliotto, A. Rahbar, C. Soderberg-Naucler, M. Won, R. Ezhilarasan, P. Sun, D. Blumenthal, M. Vogelbaum, R. Jenkins, R. Jeraj, P. Brown, K. Jaeckle, D. Schiff, J. Dignam, J. Atkins, D. Brachman, M. Werner-Wasik, M. Mehta, J. Shen, J. Luan, A. Yu, M. Matsutani, Y. Liang, T.-K. Man, A. Trister, M. Tokita, S. Mikheeva, A. Mikheev, S. Friend, M. van den Bent, L. Erdem, T. Gorlia, M. Taphoorn, J. Kros, P. Wesseling, H. Dubbink, A. Ibdaih, P. French, H. van Thuijl, J. Heimans, B. Ylstra, J. Reijneveld, A. Prabowo, I. Scheinin, H. van Essen, W. Spliet, C. Ferrier, P. van Rijen, T. Veersema, M. Thom, A. S.-v. Meeteren, E. Aronica, H. Kim, S. Zheng, D. J. Brat, S. Virk, S. Amini, C. Sougnez, J. Barnholtz-Sloan, R. G. W. Verhaak, C. Watts, A. Sottoriva, I. Spiteri, S. Piccirillo, A. Touloumis, P. Collins, J. Marioni, C. Curtis, S. Tavare, B. Tews, T. P. C. Yeung, B. Al-Khazraji, L. Morrison, L. Hoffman, D. Jackson, T.-Y. Lee, S. Yartsev, G. Bauman, J. Fu, R. Vegesna, Y. Mao, L. E. Heathcock, W. Torres-Garcia, S. Wang, A. McKenna, C. W. Brennan, W. K. A. Yung, J. N. Weinstein, E. P. Sulman, and D. Koul
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Abstracts ,Cancer Research ,Text mining ,Oncology ,business.industry ,Neurology (clinical) ,Computational biology ,Biology ,Omics ,business - Published
- 2013
8. The Death Receptor CD95 Activates Adult Neural Stem Cells for Working Memory Formation and Brain Repair
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Marcin Teodorczyk, Norbert Gretz, B Wiestler, Meinolf Thiemann, Stefan Klussmann, Susanne Kleber, Tansu Celikel, Philipp Koch, Wolf Mueller, Rolf Sprengel, Sabrina Laudenklos, Oliver Brüstle, Ana Martin-Villalba, Elisabeth Letellier, Sachin Kumar, Ignacio Sancho-Martinez, Oliver Hill, Désirée Glagow, Nina S. Corsini, Christian Gieffers, and Matthias Seedorf
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Epigenetic regulation of neurogenesis ,Fas Ligand Protein ,Neurogenesis ,Subventricular zone ,Gene Expression ,Biology ,Biochemistry, biophysics & molecular biology [F05] [Life sciences] ,Brain Ischemia ,Mice ,Memory ,medicine ,Genetics ,Animals ,fas Receptor ,Biochimie, biophysique & biologie moléculaire [F05] [Sciences du vivant] ,PI3K/AKT/mTOR pathway ,Neurons ,Dentate gyrus ,TOR Serine-Threonine Kinases ,Brain ,brain damage ,Cell Biology ,STEMCELL ,Neural stem cell ,Neuroepithelial cell ,Mice, Inbred C57BL ,neurogenesis ,Adult Stem Cells ,medicine.anatomical_structure ,nervous system ,Immunology ,CD95 ,Molecular Medicine ,Female ,Neuroscience ,Protein Kinases ,Adult stem cell ,Signal Transduction ,Stem Cell Transplantation - Abstract
SummaryAdult neurogenesis persists in the subventricular zone and the dentate gyrus and can be induced upon central nervous system injury. However, the final contribution of newborn neurons to neuronal networks is limited. Here we show that in neural stem cells, stimulation of the “death receptor” CD95 does not trigger apoptosis but unexpectedly leads to increased stem cell survival and neuronal specification. These effects are mediated via activation of the Src/PI3K/AKT/mTOR signaling pathway, ultimately leading to a global increase in protein translation. Induction of neurogenesis by CD95 was further confirmed in the ischemic CA1 region, in the naive dentate gyrus, and after forced expression of CD95L in the adult subventricular zone. Lack of hippocampal CD95 resulted in a reduction in neurogenesis and working memory deficits. Following global ischemia, CD95-mediated brain repair rescued behavioral impairment. Thus, we identify the CD95/CD95L system as an instructive signal for ongoing and injury-induced neurogenesis.
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- 2009
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9. Basal Caspase Activity Promotes Migration and Invasiveness in Glioblastoma Cells
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Sebastian Sieber, Kerstin Grund, Barbara C. Böck, Anika Eckert, B Wiestler, Otmar D. Wiestler, Benjamin Funke, Wilfried Roth, Georg Gdynia, Stephan Macher-Goeppinger, and Christel Herold-Mende
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Cancer Research ,Small interfering RNA ,MAP Kinase Signaling System ,Motility ,Cell Movement ,Cell Line, Tumor ,Glioma ,medicine ,Humans ,Neoplasm Invasiveness ,fas Receptor ,Enzyme Inhibitors ,RNA, Small Interfering ,Molecular Biology ,Gelsolin ,Caspase ,Caspase 8 ,biology ,Brain Neoplasms ,Caspase 3 ,Kinase ,Fas receptor ,medicine.disease ,Caspase Inhibitors ,Cell biology ,Oncology ,Apoptosis ,biology.protein ,Cancer research ,Glioblastoma - Abstract
Glioblastomas, the most malignant of all brain tumors, are characterized by cellular resistance to apoptosis and a highly invasive growth pattern. These factors contribute to the poor response of glioblastomas to radiochemotherapy and prevent their complete neurosurgical resection. However, the driving force behind the distinct motility of glioma cells is only partly understood. Here, we report that in the absence of cellular stress and proapoptotic stimuli, human glioblastoma cells exhibit a constitutive activation of caspases in vivo and in vitro. The inhibition of caspases by various peptide inhibitors decreases the migration of cells in scrape motility assays and the invasiveness of cells in spheroid assays. Similarly, specific small interfering RNA– or antisense-mediated down-regulation of caspase-3 and caspase-8 results in an inhibition of the migratory potential of glioma cells. The constitutive caspase-dependent motility of glioblastoma cells is independent of CD95 activation and it is not mediated by mitogen-activated protein/extracellular signal-regulated kinase kinase signaling. The basal caspase activity is accompanied by a constant cleavage of the motility-associated gelsolin protein, which may contribute to the caspase-mediated promotion of migration and invasiveness in glioblastoma cells. Our results suggest that the administration of low doses of caspase inhibitors that block glioma cell motility without affecting the execution of apoptotic cell death may be exploited as a novel strategy for the treatment of glioblastomas. (Mol Cancer Res 2007;5(12):1232–40)
- Published
- 2007
10. Wachstumsmuster von Glioblastomen: Prognostischer Nutzen kombinierter ADC und CBV Maps
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Andreas Radbruch, Martin Bendszus, RO Floca, B Wiestler, W Wick, K Deike, Sabine Heiland, and M Graf
- Subjects
Radiology, Nuclear Medicine and imaging - Published
- 2014
11. Identification of an epigenetic biomarker predicting the response to therapy with APG101 in glioblastoma
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J. Sykora, C. Kunz, Wolfgang Wick, C. Gieffers, H. Fricke, B. Wiestler, M. Thiemann, and C. Merz
- Subjects
APG101 ,Oncology ,Response to therapy ,business.industry ,medicine ,Cancer research ,Biomarker (medicine) ,Identification (biology) ,Hematology ,Epigenetics ,medicine.disease ,business ,Glioblastoma - Published
- 2016
12. MR-Perfusionsbildgebung zur Differenzialdiagnose von Glioblastom und PCNSL geeignet
- Author
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Philipp Kickingereder, B Wiestler, and F Sahm
- Published
- 2015
13. Time-of-Flight Angiography at 7 Tesla visualizes Tumor Vessels in Patients with Newly Diagnosed Glioblastoma
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M Roehtke, Andreas Radbruch, Martin Bendszus, Heinz Peter Schlemmer, B Wiestler, and Wolfhard Semmler
- Subjects
medicine.medical_specialty ,medicine.diagnostic_test ,business.industry ,Glioma ,Angiography ,medicine ,Radiology, Nuclear Medicine and imaging ,In patient ,Radiology ,Newly diagnosed ,medicine.disease ,business ,Glioblastoma - Published
- 2013
14. Differential methylation of a CpG site in the CD95-ligand promoter predicts the response to therapy with APG101 in glioblastoma
- Author
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C. Kunz, M. Thiemann, C. Gieffers, H. Fricke, Wolfgang Wick, B. Wiestler, J. Sykora, and C. Merz
- Subjects
Cancer Research ,APG101 ,Cd95 ligand ,Oncology ,Response to therapy ,CpG site ,Chemistry ,medicine ,Cancer research ,Differential Methylation ,medicine.disease ,Glioblastoma - Published
- 2016
15. Abgrenzung von höher- und niedrigmalignen Astrozytomen mittels Suszeptibilitäts-gewichteter-Bildgebung
- Author
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L Kramp, C. Hartmann, Philipp Bäumer, Bram Stieltjes, Martin Bendszus, B Wiestler, Andreas Radbruch, Sabine Heiland, Kira Lutz, W Wick, and Tobias Boppel
- Subjects
Radiology, Nuclear Medicine and imaging - Published
- 2011
16. Yes and PI3K bind CD95 to signal invasion of glioblastoma
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Tom M. Ganten, Cecilia Zuliani, Stefan Klussmann, Elisabeth Letellier, Alexandra Beisel, Christian Gieffers, Anne Régnier-Vigouroux, Andreas Kuhn, Wolf Mueller, Ana Martin-Villalba, Marcin Teodorczyk, Christel Herold-Mende, Jaromir Sykora, Andreas von Deimling, Meinolf Thiemann, Ignacio Sancho-Martinez, Oliver Hill, Jochen Tüttenberg, Hermann Josef Gröne, B Wiestler, Nina Schreglmann, Holger Sültmann, and Susanne Kleber
- Subjects
Cancer Research ,Death Domain Receptor Signaling Adaptor Proteins ,Fas Ligand Protein ,Recombinant Fusion Proteins ,Apoptosis ,Biology ,Biochemistry, biophysics & molecular biology [F05] [Life sciences] ,Transfection ,Glycogen Synthase Kinase 3 ,Mice ,Phosphatidylinositol 3-Kinases ,GSK-3 ,Cell Movement ,Glioma ,Cell Line, Tumor ,hemic and lymphatic diseases ,medicine ,Tumor Cells, Cultured ,Animals ,Humans ,Neoplasm Invasiveness ,fas Receptor ,RNA, Small Interfering ,CD95 glioblastome ,Biochimie, biophysique & biologie moléculaire [F05] [Sciences du vivant] ,GSK3B ,PI3K/AKT/mTOR pathway ,Proto-Oncogene Proteins c-yes ,Glycogen Synthase Kinase 3 beta ,Activator (genetics) ,Brain Neoplasms ,hemic and immune systems ,Cell Biology ,medicine.disease ,Matrix Metalloproteinases ,Cell biology ,Isolated Tumor Cells ,Transplantation, Isogeneic ,src-Family Kinases ,Oncology ,Cell culture ,CELLBIO ,RNA Interference ,Signal transduction ,biological phenomena, cell phenomena, and immunity ,Glioblastoma ,Neoplasm Transplantation ,Signal Transduction - Abstract
SummaryInvasion of surrounding brain tissue by isolated tumor cells represents one of the main obstacles to a curative therapy of glioblastoma multiforme. Here we unravel a mechanism regulating glioma infiltration. Tumor interaction with the surrounding brain tissue induces CD95 Ligand expression. Binding of CD95 Ligand to CD95 on glioblastoma cells recruits the Src family member Yes and the p85 subunit of phosphatidylinositol 3-kinase to CD95, which signal invasion via the glycogen synthase kinase 3-β pathway and subsequent expression of matrix metalloproteinases. In a murine syngeneic model of intracranial GBM, neutralization of CD95 activity dramatically reduced the number of invading cells. Our results uncover CD95 as an activator of PI3K and, most importantly, as a crucial trigger of basal invasion of glioblastoma in vivo.
- Published
- 2008
17. A Framework for Multimodal Medical Image Interaction.
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Schutz L, Matinfar S, Schafroth G, Navab N, Fairhurst M, Wagner A, Wiestler B, Eck U, and Navab N
- Abstract
Medical doctors rely on images of the human anatomy, such as magnetic resonance imaging (MRI), to localize regions of interest in the patient during diagnosis and treatment. Despite advances in medical imaging technology, the information conveyance remains unimodal. This visual representation fails to capture the complexity of the real, multisensory interaction with human tissue. However, perceiving multimodal information about the patient's anatomy and disease in real-time is critical for the success of medical procedures and patient outcome. We introduce a Multimodal Medical Image Interaction (MMII) framework to allow medical experts a dynamic, audiovisual interaction with human tissue in three-dimensional space. In a virtual reality environment, the user receives physically informed audiovisual feedback to improve the spatial perception of anatomical structures. MMII uses a model-based sonification approach to generate sounds derived from the geometry and physical properties of tissue, thereby eliminating the need for hand-crafted sound design. Two user studies involving 34 general and nine clinical experts were conducted to evaluate the proposed interaction framework's learnability, usability, and accuracy. Our results showed excellent learnability of audiovisual correspondence as the rate of correct associations significantly improved (p < 0.001) over the course of the study. MMII resulted in superior brain tumor localization accuracy (p < 0.05) compared to conventional medical image interaction. Our findings substantiate the potential of this novel framework to enhance interaction with medical images, for example, during surgical procedures where immediate and precise feedback is needed.
- Published
- 2024
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18. Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy.
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Buchner JA, Kofler F, Mayinger M, Christ SM, Brunner TB, Wittig A, Menze B, Zimmer C, Meyer B, Guckenberger M, Andratschke N, El Shafie RA, Debus J, Rogers S, Riesterer O, Schulze K, Feldmann HJ, Blanck O, Zamboglou C, Ferentinos K, Bilger-Zähringer A, Grosu AL, Wolff R, Piraud M, Eitz KA, Combs SE, Bernhardt D, Rueckert D, Wiestler B, and Peeken JC
- Subjects
- Humans, Male, Female, Retrospective Studies, Middle Aged, Aged, Prognosis, Follow-Up Studies, Adult, Radiomics, Brain Neoplasms secondary, Brain Neoplasms surgery, Brain Neoplasms diagnostic imaging, Brain Neoplasms radiotherapy, Radiosurgery methods, Magnetic Resonance Imaging methods
- Abstract
Background: Surgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the risk of local failure (LF) persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk., Methods: Data were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of BMs (AURORA) retrospective study (training cohort: 253 patients from 2 centers; external test cohort: 99 patients from 5 centers). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (T2-FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameter set previously determined by internal 5-fold cross-validation and tested on the external test set., Results: The best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (P < .001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively., Conclusions: A combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)
- Published
- 2024
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19. Generating synthetic high-resolution spinal STIR and T1w images from T2w FSE and low-resolution axial Dixon.
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Graf R, Platzek PS, Riedel EO, Kim SH, Lenhart N, Ramschütz C, Paprottka KJ, Kertels OR, Möller HK, Atad M, Bülow R, Werner N, Völzke H, Schmidt CO, Wiestler B, Paetzold JC, Rueckert D, and Kirschke JS
- Abstract
Objectives: To generate sagittal T1-weighted fast spin echo (T1w FSE) and short tau inversion recovery (STIR) images from sagittal T2-weighted (T2w) FSE and axial T1w gradient echo Dixon technique (T1w-Dixon) sequences., Materials and Methods: This retrospective study used three existing datasets: "Study of Health in Pomerania" (SHIP, 3142 subjects, 1.5 Tesla), "German National Cohort" (NAKO, 2000 subjects, 3 Tesla), and an internal dataset (157 patients 1.5/3 Tesla). We generated synthetic sagittal T1w FSE and STIR images from sagittal T2w FSE and low-resolution axial T1w-Dixon sequences based on two successively applied 3D Pix2Pix deep learning models. "Peak signal-to-noise ratio" (PSNR) and "structural similarity index metric" (SSIM) were used to evaluate the generated image quality on an ablations test. A Turing test, where seven radiologists rated 240 images as either natively acquired or generated, was evaluated using misclassification rate and Fleiss kappa interrater agreement., Results: Including axial T1w-Dixon or T1w FSE images resulted in higher image quality in generated T1w FSE (PSNR = 26.942, SSIM = 0.965) and STIR (PSNR = 28.86, SSIM = 0.948) images compared to using only single T2w images as input (PSNR = 23.076/24.677 SSIM = 0.952/0.928). Radiologists had difficulty identifying generated images (misclassification rate: 0.39 ± 0.09 for T1w FSE, 0.42 ± 0.18 for STIR) and showed low interrater agreement on suspicious images (Fleiss kappa: 0.09 for T1w/STIR)., Conclusions: Axial T1w-Dixon and sagittal T2w FSE images contain sufficient information to generate sagittal T1w FSE and STIR images., Clinical Relevance Statement: T1w fast spin echo and short tau inversion recovery can be retroactively added to existing datasets, saving MRI time and enabling retrospective analysis, such as evaluating bone marrow pathologies., Key Points: Sagittal T2-weighted images alone were insufficient for differentiating fat and water and to generate T1-weighted images. Axial T1w Dixon technique, together with a T2-weighted sequence, produced realistic sagittal T1-weighted images. Our approach can be used to retrospectively generate STIR and T1-weighted fast spin echo sequences., (© 2024. The Author(s).)
- Published
- 2024
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20. Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.
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Erdur AC, Rusche D, Scholz D, Kiechle J, Fischer S, Llorián-Salvador Ó, Buchner JA, Nguyen MQ, Etzel L, Weidner J, Metz MC, Wiestler B, Schnabel J, Rueckert D, Combs SE, and Peeken JC
- Abstract
The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization., (© 2024. The Author(s).)
- Published
- 2024
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21. A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors.
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Gong Z, Xu T, Peng N, Cheng X, Niu C, Wiestler B, Hong F, and Li HB
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- Humans, Female, Breast Neoplasms diagnostic imaging, Breast Neoplasms pathology, Lung Neoplasms diagnostic imaging, Lung Neoplasms pathology, Ovarian Neoplasms diagnostic imaging, Ovarian Neoplasms pathology, Melanoma diagnostic imaging, Melanoma pathology, Brain Neoplasms diagnostic imaging, Glioma diagnostic imaging, Glioma pathology, Magnetic Resonance Imaging
- Abstract
Brain metastases (BMs) and high-grade gliomas (HGGs) are the most common and aggressive types of malignant brain tumors in adults, with often poor prognosis and short survival. As their clinical symptoms and image appearances on conventional magnetic resonance imaging (MRI) can be astonishingly similar, their accurate differentiation based solely on clinical and radiological information can be very challenging, particularly for "cancer of unknown primary", where no systemic malignancy is known or found. Non-invasive multiparametric MRI and radiomics offer the potential to identify these distinct biological properties, aiding in the characterization and differentiation of HGGs and BMs. However, there is a scarcity of publicly available multi-origin brain tumor imaging data for tumor characterization. In this paper, we introduce a multi-center, multi-origin brain tumor MRI (MOTUM) imaging dataset obtained from 67 patients: 29 with high-grade gliomas, 20 with lung metastases, 10 with breast metastases, 2 with gastric metastasis, 4 with ovarian metastasis, and 2 with melanoma metastasis. This dataset includes anonymized DICOM files alongside processed FLAIR, T1-weighted, contrast-enhanced T1-weighted, T2-weighted sequences images, segmentation masks of two tumor regions, and clinical data. Our data-sharing initiative is to support the benchmarking of automated tumor segmentation, multi-modal machine learning, and disease differentiation of multi-origin brain tumors in a multi-center setting., (© 2024. The Author(s).)
- Published
- 2024
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22. Exploring molecular glioblastoma: Insights from advanced imaging for a nuanced understanding of the molecularly defined malignant biology.
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Griessmair M, Delbridge C, Ziegenfeuter J, Jung K, Mueller T, Schramm S, Bernhardt D, Schmidt-Graf F, Kertels O, Thomas M, Zimmer C, Meyer B, Combs SE, Yakushev I, Wiestler B, and Metz MC
- Abstract
Background: Molecular glioblastoma (molGB) does not exhibit the histologic hallmarks of a grade 4 glioma but is nevertheless diagnosed as glioblastoma when harboring specific molecular markers. MolGB can easily be mistaken for similar-appearing lower-grade astrocytomas. Here, we investigated how advanced imaging could reflect the underlying tumor biology., Methods: Clinical and imaging data were collected for 7 molGB grade 4, 9 astrocytomas grade 2, and 12 astrocytomas grade 3. Four neuroradiologists performed VASARI-scoring of conventional imaging, and their inter-reader agreement was assessed using Fleiss κ coefficient. To evaluate the potential of advanced imaging, 2-sample t test, 1-way ANOVA, Mann-Whitney U, and Kruskal-Wallis test were performed to test for significant differences between apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) that were extracted fully automatically from the whole tumor volume., Results: While conventional VASARI imaging features did not allow for reliable differentiation between glioma entities, rCBV was significantly higher in molGB compared to astrocytomas for the 5th and 95th percentile, mean, and median values ( P < .05). ADC values were significantly lower in molGB than in astrocytomas for mean, median, and the 95th percentile ( P < .05). Although no molGB showed contrast enhancement initially, we observed enhancement in the short-term follow-up of 1 patient., Discussion: Quantitative analysis of diffusion and perfusion parameters shows potential in reflecting the malignant tumor biology of molGB. It may increase awareness of molGB in a nonenhancing, "benign" appearing tumor. Our results support the emerging hypothesis that molGB might present glioblastoma captured at an early stage of gliomagenesis., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)
- Published
- 2024
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23. Enhancing predictability of IDH mutation status in glioma patients at initial diagnosis: a comparative analysis of radiomics from MRI, [ 18 F]FET PET, and TSPO PET.
- Author
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Kaiser L, Quach S, Zounek AJ, Wiestler B, Zatcepin A, Holzgreve A, Bollenbacher A, Bartos LM, Ruf VC, Böning G, Thon N, Herms J, Riemenschneider MJ, Stöcklein S, Brendel M, Rupprecht R, Tonn JC, Bartenstein P, von Baumgarten L, Ziegler S, and Albert NL
- Subjects
- Humans, Female, Male, Middle Aged, Adult, Brain Neoplasms diagnostic imaging, Brain Neoplasms genetics, Aged, Tyrosine analogs & derivatives, Image Processing, Computer-Assisted, Radiomics, Receptors, GABA genetics, Receptors, GABA metabolism, Isocitrate Dehydrogenase genetics, Mutation, Positron-Emission Tomography methods, Glioma diagnostic imaging, Glioma genetics, Magnetic Resonance Imaging
- Abstract
Purpose: According to the World Health Organization classification for tumors of the central nervous system, mutation status of the isocitrate dehydrogenase (IDH) genes has become a major diagnostic discriminator for gliomas. Therefore, imaging-based prediction of IDH mutation status is of high interest for individual patient management. We compared and evaluated the diagnostic value of radiomics derived from dual positron emission tomography (PET) and magnetic resonance imaging (MRI) data to predict the IDH mutation status non-invasively., Methods: Eighty-seven glioma patients at initial diagnosis who underwent PET targeting the translocator protein (TSPO) using [
18 F]GE-180, dynamic amino acid PET using [18 F]FET, and T1-/T2-weighted MRI scans were examined. In addition to calculating tumor-to-background ratio (TBR) images for all modalities, parametric images quantifying dynamic [18 F]FET PET information were generated. Radiomic features were extracted from TBR and parametric images. The area under the receiver operating characteristic curve (AUC) was employed to assess the performance of logistic regression (LR) classifiers. To report robust estimates, nested cross-validation with five folds and 50 repeats was applied., Results: TBRGE-180 features extracted from TSPO-positive volumes had the highest predictive power among TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic [18 F]FET PET reached a similarly high performance (0.94, with age 0.96). The highest LR coefficients in multimodal analyses included TBRGE-180 features, parameters from kinetic and early static [18 F]FET PET images, age, and the features from TBRT2 images such as the kurtosis (0.97)., Conclusion: The findings suggest that incorporating TBRGE-180 features along with kinetic information from dynamic [18 F]FET PET, kurtosis from TBRT2 , and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management., (© 2024. The Author(s).)- Published
- 2024
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24. Virtual reality-empowered deep-learning analysis of brain cells.
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Kaltenecker D, Al-Maskari R, Negwer M, Hoeher L, Kofler F, Zhao S, Todorov M, Rong Z, Paetzold JC, Wiestler B, Piraud M, Rueckert D, Geppert J, Morigny P, Rohm M, Menze BH, Herzig S, Berriel Diaz M, and Ertürk A
- Subjects
- Animals, Mice, Neurons, Software, Image Processing, Computer-Assisted methods, Proto-Oncogene Proteins c-fos metabolism, Humans, Deep Learning, Brain diagnostic imaging, Virtual Reality
- Abstract
Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos
+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease., (© 2024. The Author(s).)- Published
- 2024
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25. The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.
- Author
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Moawad AW, Janas A, Baid U, Ramakrishnan D, Saluja R, Ashraf N, Jekel L, Amiruddin R, Adewole M, Albrecht J, Anazodo U, Aneja S, Anwar SM, Bergquist T, Calabrese E, Chiang V, Chung V, Conte GMM, Dako F, Eddy J, Ezhov I, Familiar A, Farahani K, Iglesias JE, Jiang Z, Johanson E, Kazerooni AF, Kofler F, Krantchev K, LaBella D, Van Leemput K, Li HB, Linguraru MG, Link KE, Liu X, Maleki N, Meier Z, Menze BH, Moy H, Osenberg K, Piraud M, Reitman Z, Shinohara RT, Tahon NH, Nada A, Velichko YS, Wang C, Wiestler B, Wiggins W, Shafique U, Willms K, Avesta A, Bousabarah K, Chakrabarty S, Gennaro N, Holler W, Kaur M, LaMontagne P, Lin M, Lost J, Marcus DS, Maresca R, Merkaj S, Nada A, Pedersen GC, von Reppert M, Sotiras A, Teytelboym O, Tillmans N, Westerhoff M, Youssef A, Godfrey D, Floyd S, Rauschecker A, Villanueva-Meyer J, Pflüger I, Cho J, Bendszus M, Brugnara G, Cramer J, Perez-Carillo GJG, Johnson DR, Kam A, Kwan BYM, Lai L, Lall NU, Memon F, Patro SN, Petrovic B, So TY, Thompson G, Wu L, Schrickel EB, Bansal A, Barkhof F, Besada C, Chu S, Druzgal J, Dusoi A, Farage L, Feltrin F, Fong A, Fung SH, Gray RI, Ikuta I, Iv M, Postma AA, Mahajan A, Joyner D, Krumpelman C, Letourneau-Guillon L, Lincoln CM, Maros ME, Miller E, Morón F, Nimchinsky EA, Ozsarlak O, Patel U, Rohatgi S, Saha A, Sayah A, Schwartz ED, Shih R, Shiroishi MS, Small JE, Tanwar M, Valerie J, Weinberg BD, White ML, Young R, Zohrabian VM, Azizova A, Brüßeler MMT, Fehringer P, Ghonim M, Ghonim M, Gkampenis A, Okar A, Pasquini L, Sharifi Y, Singh G, Sollmann N, Soumala T, Taherzadeh M, Yordanov N, Vollmuth P, Foltyn-Dumitru M, Malhotra A, Abayazeed AH, Dellepiane F, Lohmann P, Pérez-García VM, Elhalawani H, Al-Rubaiey S, Armindo RD, Ashraf K, Asla MM, Badawy M, Bisschop J, Lomer NB, Bukatz J, Chen J, Cimflova P, Corr F, Crawley A, Deptula L, Elakhdar T, Shawali IH, Faghani S, Frick A, Gulati V, Haider MA, Hierro F, Dahl RH, Jacobs SM, Hsieh KJ, Kandemirli SG, Kersting K, Kida L, Kollia S, Koukoulithras I, Li X, Abouelatta A, Mansour A, Maria-Zamfirescu RC, Marsiglia M, Mateo-Camacho YS, McArthur M, McDonnell O, McHugh M, Moassefi M, Morsi SM, Muntenu A, Nandolia KK, Naqvi SR, Nikanpour Y, Alnoury M, Nouh AMA, Pappafava F, Patel MD, Petrucci S, Rawie E, Raymond S, Roohani B, Sabouhi S, Sanchez-Garcia LM, Shaked Z, Suthar PP, Altes T, Isufi E, Dhermesh Y, Gass J, Thacker J, Tarabishy AR, Turner B, Vacca S, Vilanilam GK, Warren D, Weiss D, Willms K, Worede F, Yousry S, Lerebo W, Aristizabal A, Karargyris A, Kassem H, Pati S, Sheller M, Bakas S, Rudie JD, and Aboian M
- Abstract
The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment., Competing Interests: Conflicts of Interest No conflicts of interest to disclose.
- Published
- 2024
26. Resolving spatial response heterogeneity in glioblastoma.
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Ziegenfeuter J, Delbridge C, Bernhardt D, Gempt J, Schmidt-Graf F, Hedderich D, Griessmair M, Thomas M, Meyer HS, Zimmer C, Meyer B, Combs SE, Yakushev I, Metz MC, and Wiestler B
- Abstract
Purpose: Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity., Methods: Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated., Results: Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance., Conclusion: Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future., (© 2024. The Author(s).)
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- 2024
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27. Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.
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Floca R, Bohn J, Haux C, Wiestler B, Zöllner FG, Reinke A, Weiß J, Nolden M, Albert S, Persigehl T, Norajitra T, Baeßler B, Dewey M, Braren R, Büchert M, Fallenberg EM, Galldiks N, Gerken A, Götz M, Hahn HK, Haubold J, Haueise T, Große Hokamp N, Ingrisch M, Iuga AI, Janoschke M, Jung M, Kiefer LS, Lohmann P, Machann J, Moltz JH, Nattenmüller J, Nonnenmacher T, Oerther B, Othman AE, Peisen F, Schick F, Umutlu L, Wichtmann BD, Zhao W, Caspers S, Schlemmer HP, Schlett CL, Maier-Hein K, and Bamberg F
- Abstract
Objectives: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation., Materials and Methods: The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges., Results: Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows., Conclusion: To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized., Critical Relevance Statement: Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics., Key Points: Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community., (© 2024. The Author(s).)
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- 2024
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28. A prognostic neural epigenetic signature in high-grade glioma.
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Drexler R, Khatri R, Sauvigny T, Mohme M, Maire CL, Ryba A, Zghaibeh Y, Dührsen L, Salviano-Silva A, Lamszus K, Westphal M, Gempt J, Wefers AK, Neumann JE, Bode H, Hausmann F, Huber TB, Bonn S, Jütten K, Delev D, Weber KJ, Harter PN, Onken J, Vajkoczy P, Capper D, Wiestler B, Weller M, Snijder B, Buck A, Weiss T, Göller PC, Sahm F, Menstel JA, Zimmer DN, Keough MB, Ni L, Monje M, Silverbush D, Hovestadt V, Suvà ML, Krishna S, Hervey-Jumper SL, Schüller U, Heiland DH, Hänzelmann S, and Ricklefs FL
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- Humans, Prognosis, DNA Methylation genetics, Animals, Mice, Male, Female, Gene Expression Regulation, Neoplastic, Glioblastoma genetics, Glioblastoma pathology, Middle Aged, Neurons pathology, Neurons metabolism, Adult, Single-Cell Analysis, Cell Line, Tumor, Transcriptome, Neoplasm Grading, Epigenesis, Genetic, Glioma genetics, Glioma pathology, Brain Neoplasms genetics, Brain Neoplasms pathology
- Abstract
Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is limited. We present an epigenetically defined neural signature of glioblastoma that independently predicts patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals a high abundance of malignant stemcell-like cells in high-neural glioblastoma, primarily of the neural lineage. These cells are further classified as neural-progenitor-cell-like, astrocyte-like and oligodendrocyte-progenitor-like, alongside oligodendrocytes and excitatory neurons. In line with these findings, high-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature is associated with decreased overall and progression-free survival. High-neural tumors also exhibit increased functional connectivity in magnetencephalography and resting-state magnet resonance imaging and can be detected via DNA analytes and brain-derived neurotrophic factor in patients' plasma. The prognostic importance of the neural signature was further validated in patients diagnosed with diffuse midline glioma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant. High-neural gliomas likely require a maximized surgical resection approach for improved outcomes., (© 2024. The Author(s).)
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- 2024
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29. Imaging meningioma biology: Machine learning predicts integrated risk score in WHO grade 2/3 meningioma.
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Kertels O, Delbridge C, Sahm F, Ehret F, Acker G, Capper D, Peeken JC, Diehl C, Griessmair M, Metz MC, Negwer C, Krieg SM, Onken J, Yakushev I, Vajkoczy P, Meyer B, Zips D, Combs SE, Zimmer C, Kaul D, Bernhardt D, and Wiestler B
- Abstract
Background: Meningiomas are the most common primary brain tumors. While most are benign (WHO grade 1) and have a favorable prognosis, up to one-fourth are classified as higher-grade, falling into WHO grade 2 or 3 categories. Recently, an integrated risk score (IRS) pertaining to tumor biology was developed and its prognostic relevance was validated in a large, multicenter study. We hypothesized imaging data to be reflective of the IRS. Thus, we assessed the potential of a machine learning classifier for its noninvasive prediction using preoperative magnetic resonance imaging (MRI)., Methods: In total, 160 WHO grade 2 and 3 meningioma patients from 2 university centers were included in this study. All patients underwent surgery with histopathological workup including methylation analysis. Preoperative MRI scans were automatically segmented, and radiomic parameters were extracted. Using a random forest classifier, 3 machine learning classifiers (1 multiclass classifier for IRS and 2 binary classifiers for low-risk and high-risk prediction, respectively) were developed in a training set (120 patients) and independently tested in a hold-out test set (40 patients)., Results: Multiclass IRS classification had a test set area under the curve (AUC) of 0.7, mostly driven by the difficulties in clearly separating medium-risk from high-risk patients. Consequently, a classifier predicting low-risk IRS versus medium-/high-risk showed a very high test accuracy of 90% (AUC 0.88). In particular, "sphericity" was associated with low-risk IRS classification., Conclusion: The IRS, in particular molecular low-risk, can be predicted from imaging data with high accuracy, making this important prognostic classification accessible by imaging., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)
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- 2024
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30. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) .
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Kazerooni AF, Khalili N, Liu X, Haldar D, Jiang Z, Anwar SM, Albrecht J, Adewole M, Anazodo U, Anderson H, Bagheri S, Baid U, Bergquist T, Borja AJ, Calabrese E, Chung V, Conte GM, Dako F, Eddy J, Ezhov I, Familiar A, Farahani K, Haldar S, Iglesias JE, Janas A, Johansen E, Jones BV, Kofler F, LaBella D, Lai HA, Van Leemput K, Li HB, Maleki N, McAllister AS, Meier Z, Menze B, Moawad AW, Nandolia KK, Pavaine J, Piraud M, Poussaint T, Prabhu SP, Reitman Z, Rodriguez A, Rudie JD, Shaikh IS, Shah LM, Sheth N, Shinohara RT, Tu W, Viswanathan K, Wang C, Ware JB, Wiestler B, Wiggins W, Zapaishchykova A, Aboian M, Bornhorst M, de Blank P, Deutsch M, Fouladi M, Hoffman L, Kann B, Lazow M, Mikael L, Nabavizadeh A, Packer R, Resnick A, Rood B, Vossough A, Bakas S, and Linguraru MG
- Abstract
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.
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- 2024
31. Toward a systematic grading for the selection of patients to undergo awake surgery: identifying suitable predictor variables.
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Kram L, Neu B, Schroeder A, Wiestler B, Meyer B, Krieg SM, and Ille S
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Background: Awake craniotomy is the standard of care for treating language eloquent gliomas. However, depending on preoperative functionality, it is not feasible in each patient and selection criteria are highly heterogeneous. Thus, this study aimed to identify broadly applicable predictor variables allowing for a more systematic and objective patient selection., Methods: We performed post-hoc analyses of preoperative language status, patient and tumor characteristics including language eloquence of 96 glioma patients treated in a single neurosurgical center between 05/2018 and 01/2021. Multinomial logistic regression and stepwise variable selection were applied to identify significant predictors of awake surgery feasibility., Results: Stepwise backward selection confirmed that a higher number of paraphasias, lower age, and high language eloquence level were suitable indicators for an awake surgery in our cohort. Subsequent descriptive and ROC-analyses indicated a cut-off at ≤54 years and a language eloquence level of at least 6 for awake surgeries, which require further validation. A high language eloquence, lower age, preexisting semantic and phonological aphasic symptoms have shown to be suitable predictors., Conclusion: The combination of these factors may act as a basis for a systematic and standardized grading of patients' suitability for an awake craniotomy which is easily integrable into the preoperative workflow across neurosurgical centers., Competing Interests: BM received honoraria, consulting fees, and research grants from Medtronic (Meerbusch, Germany), Icotec AG (Altstätten, Switzerland), Nexstim Plc (Helsinki, Finland), and Relievant Medsystems Inc., (Sunnyvale, CA, USA), honoraria, and research grants from Ulrich Medical (Ulm, Germany), honoraria and consulting fees from Spineart Deutschland GmbH (Frankfurt, Germany) and DePuy Synthes (West Chester, PA, USA), and royalties from Spineart Deutschland GmbH (Frankfurt, Germany). SK is consultant for Ulrich Medical (Ulm, Germany), and Need Inc. (Santa Monica, CA, USA), and received honoraria from Nexstim Plc (Helsinki, Finland), Spineart Deutschland GmbH (Frankfurt, Germany), Medtronic (Meerbusch, Germany), and Carl Zeiss Meditec (Oberkochen, Germany). SK and BM received research grants and are consultants for Brainlab AG (Munich, Germany). SI is consultant for Brainlab AG (Munich, Germany) and received honoraria and consulting fees from Icotec AG (Altstätten, Switzerland), Carl Zeiss Meditec (Oberkochen, Germany), and Nexstim Plc (Helsinki, Finland). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Kram, Neu, Schroeder, Wiestler, Meyer, Krieg and Ille.)
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- 2024
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32. Noninvasive- and invasive mapping reveals similar language network centralities - A function-based connectome analysis.
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Ille S, Zhang H, Stassen N, Schwendner M, Schröder A, Wiestler B, Meyer B, and Krieg SM
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- Humans, Diffusion Tensor Imaging methods, Brain Mapping methods, Brain, Transcranial Magnetic Stimulation methods, Language, Brain Neoplasms surgery, Connectome
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Background: Former comparisons between direct cortical stimulation (DCS) and navigated transcranial magnetic stimulation (nTMS) only focused on cortical mapping. While both can be combined with diffusion tensor imaging, their differences in the visualization of subcortical and even network levels remain unclear. Network centrality is an essential parameter in network analysis to measure the importance of nodes identified by mapping. Those include Degree centrality, Eigenvector centrality, Closeness centrality, Betweenness centrality, and PageRank centrality. While DCS and nTMS have repeatedly been compared on the cortical level, the underlying network identified by both has not been investigated yet., Method: 27 patients with brain lesions necessitating preoperative nTMS and intraoperative DCS language mapping during awake craniotomy were enrolled. Function-based connectome analysis was performed based on the cortical nodes obtained through the two mapping methods, and language-related network centralities were compared., Results: Compared with DCS language mapping, the positive predictive value of cortical nTMS language mapping is 74.1%, with good consistency of tractography for the arcuate fascicle and superior longitudinal fascicle. Moreover, network centralities did not differ between the two mapping methods. However, ventral stream tracts can be better traced based on nTMS mappings, demonstrating its strengths in acquiring language-related networks. In addition, it showed lower centralities than other brain areas, with decentralization as an indicator of language function loss., Conclusion: This study deepens the understanding of language-related functional anatomy and proves that non-invasive mapping-based network analysis is comparable to the language network identified via invasive cortical mapping., Competing Interests: Declaration of competing interest BM received honoraria, consulting fees, and research grants from Medtronic (Meerbusch, Germany), Icotec AG (Altstätten, Switzerland), and Relievant Medsystems Inc. (Sunnyvale, CA, USA), honoraria, and research grants from Ulrich Medical (Ulm, Germany), honoraria and consulting fees from Spineart Deutschland GmbH (Frankfurt, Germany) and DePuy Synthes (West Chester, PA, USA), and royalties from Spineart Deutschland GmbH (Frankfurt, Germany). SK is a consultant for Ulrich Medical (Ulm, Germany), and Need Inc. (Santa Monica, CA, USA), and received honoraria from Nexstim Plc (Helsinki, Finland), Spineart Deutschland GmbH (Frankfurt, Germany), Medtronic (Meerbusch, Germany) and Carl Zeiss Meditec (Oberkochen, Germany). SK, SI, and BM are consultants for Brainlab AG (Munich, Germany). However, all authors declare that they have no conflict of interest regarding the materials used or the results presented in this study., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
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- 2024
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33. Amino acid PET vs. RANO MRI for prediction of overall survival in patients with recurrent high grade glioma under bevacizumab therapy.
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Chaban A, Waschulzik B, Bernhardt D, Delbridge C, Schmidt-Graf F, Wagner A, Wiestler B, Weber W, and Yakushev I
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- Humans, Amino Acids therapeutic use, Recurrence, Female, Neoplasm Grading, Male, Survival Analysis, Middle Aged, Bevacizumab therapeutic use, Glioma diagnostic imaging, Glioma drug therapy, Glioma pathology, Magnetic Resonance Imaging, Positron-Emission Tomography, Brain Neoplasms diagnostic imaging, Brain Neoplasms drug therapy
- Abstract
Purpose: To summarize evidence on the comparative value of amino acid (AA) PET and conventional MRI for prediction of overall survival (OS) in patients with recurrent high grade glioma (rHGG) under bevacizumab therapy., Methods: Medical databases were screened for studies with individual data on OS, follow-up MRI, and PET findings in the same patient. MRI images were assessed according to the RANO criteria. A receiver operating characteristic curve analysis was used to predict OS at 9 months., Results: Five studies with a total of 72 patients were included. Median OS was significantly lower in the PET-positive than in the PET-negative group. PET findings predicted OS with a pooled sensitivity and specificity of 76% and 71%, respectively. Corresponding values for MRI were 32% and 82%. Area under the curve and sensitivity were significantly higher for PET than for MRI., Conclusion: For monitoring of patients with rHGG under bevacizumab therapy, AA-PET should be preferred over RANO MRI., (© 2024. The Author(s).)
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- 2024
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34. MRI-detected intraosseous bone marrow edema recedes after effective therapy of periodontitis.
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Schwarting J, Probst FA, Griesbauer M, Robl T, Burian E, Wiestler B, Brunner T, Malenova Y, Bumm C, Folwaczny M, and Probst M
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- Humans, Male, Female, Prospective Studies, Middle Aged, Adult, Bone Marrow diagnostic imaging, Bone Marrow pathology, Bone Marrow Diseases diagnostic imaging, Bone Marrow Diseases therapy, Treatment Outcome, Periodontitis diagnostic imaging, Periodontitis therapy, Magnetic Resonance Imaging methods, Edema diagnostic imaging
- Abstract
Objectives: T2 STIR MRI sequences can detect preclinical changes associated with periodontal inflammation, i.e. intraosseous edema in the tooth-supporting bone. In this study, we assessed whether MRI can be used for monitoring periodontal disease., Material and Methods: In a prospective cohort study, we examined 35 patients with periodontitis between 10/2018 and 04/2019 by using 3D isotropic T2-weighted short tau inversion recovery (STIR) and Fast Field Echo T1-weighted Black bone sequences. All patients received standardized clinical exams before and three months after non-surgical periodontal therapy. Bone marrow edema extent was quantified in the STIR sequence at 922 sites before and after treatment. Results were compared with standard clinical findings. Non-parametric statistical analysis was performed., Results: Non-surgical periodontal treatment caused significant improvement in mean probing depth (p < 0.001) and frequency of bleeding on probing (p < 0.001). The mean depth of osseous edema per site was reduced from a median [IQR] of 2 [1, 3] mm at baseline to 1 [0, 3] mm, (p < 0.001). Periodontal treatment reduced the frequency of sites with edema from 35 to 24% (p < 0.01)., Conclusion: The decrease of periodontal bone marrow edema, as observed with T2 STIR MR imaging, is indicative of successful periodontal healing., Clinical Relevance Statement: T2 STIR hyperintense bone marrow edema in the periodontal bone decreases after treatment and can therefore be used to evaluate treatment success. Furthermore, MRI reveals new options to depict hidden aspects of periodontitis., Key Points: • T2 STIR hyperintense periodontal intraosseous edema was prospectively investigated in 35 patients with periodontitis before and after treatment and compared to clinical outcomes. • The frequency of affected sites was reduced from 35 to 24% (p < 0.001), and mean edema depth was reduced from a median [IQR] of 2 [1, 3] mm at baseline to 1 [0, 3] mm 3 months after treatment. (p < 0.001). • T2 STIR sequences can be used to monitor the posttreatment course of periodontitis., (© 2023. The Author(s).)
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- 2024
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35. Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA.
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Yang K, Musio F, Ma Y, Juchler N, Paetzold JC, Al-Maskari R, Höher L, Li HB, Hamamci IE, Sekuboyina A, Shit S, Huang H, Prabhakar C, de la Rosa E, Waldmannstetter D, Kofler F, Navarro F, Menten M, Ezhov I, Rueckert D, Vos I, Ruigrok Y, Velthuis B, Kuijf H, Hämmerli J, Wurster C, Bijlenga P, Westphal L, Bisschop J, Colombo E, Baazaoui H, Makmur A, Hallinan J, Wiestler B, Kirschke JS, Wiest R, Montagnon E, Letourneau-Guillon L, Galdran A, Galati F, Falcetta D, Zuluaga MA, Lin C, Zhao H, Zhang Z, Ra S, Hwang J, Park H, Chen J, Wodzinski M, Müller H, Shi P, Liu W, Ma T, Yalçin C, Hamadache RE, Salvi J, Llado X, Lal-Trehan Estrada UM, Abramova V, Giancardo L, Oliver A, Liu J, Huang H, Cui Y, Lin Z, Liu Y, Zhu S, Patel TR, Tutino VM, Orouskhani M, Wang H, Mossa-Basha M, Zhu C, Rokuss MR, Kirchhoff Y, Disch N, Holzschuh J, Isensee F, Maier-Hein K, Sato Y, Hirsch S, Wegener S, and Menze B
- Abstract
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.
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- 2024
36. Quantitative Assessment of Tumor Contact with Neurogenic Zones and Its Effects on Survival: Insights beyond Traditional Predictors.
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Jung K, Kempter J, Prokop G, Herrmann T, Griessmair M, Kim SH, Delbridge C, Meyer B, Bernhardt D, Combs SE, Zimmer C, Wiestler B, Schmidt-Graf F, and Metz MC
- Abstract
So far, the cellular origin of glioblastoma (GBM) needs to be determined, with prevalent theories suggesting emergence from transformed endogenous stem cells. Adult neurogenesis primarily occurs in two brain regions: the subventricular zone (SVZ) and the subgranular zone (SGZ) of the hippocampal dentate gyrus. Whether the proximity of GBM to these neurogenic niches affects patient outcome remains uncertain. Previous studies often rely on subjective assessments, limiting the reliability of those results. In this study, we assessed the impact of GBM's relationship with the cortex, SVZ and SGZ on clinical variables using fully automated segmentation methods. In 177 glioblastoma patients, we calculated optimal cutpoints of minimal distances to the SVZ and SGZ to distinguish poor from favorable survival. The impact of tumor contact with neurogenic zones on clinical parameters, such as overall survival, multifocality, MGMT promotor methylation, Ki-67 and KPS score was also examined by multivariable regression analysis, chi-square test and Mann-Whitney-U. The analysis confirmed shorter survival in tumors contacting the SVZ with an optimal cutpoint of 14 mm distance to the SVZ, separating poor from more favorable survival. In contrast, tumor contact with the SGZ did not negatively affect survival. We did not find significant correlations with multifocality or MGMT promotor methylation in tumors contacting the SVZ, as previous studies discussed. These findings suggest that the spatial relationship between GBM and neurogenic niches needs to be assessed differently. Objective measurements disprove prior assumptions, warranting further research on this topic., Competing Interests: The authors declare no conflicts of interest.
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- 2024
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37. Human-Level Differentiation of Medulloblastoma from Pilocytic Astrocytoma: A Real-World Multicenter Pilot Study.
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Wiestler B, Bison B, Behrens L, Tüchert S, Metz M, Griessmair M, Jakob M, Schlegel PG, Binder V, von Luettichau I, Metzler M, Johann P, Hau P, and Frühwald M
- Abstract
Medulloblastoma and pilocytic astrocytoma are the two most common pediatric brain tumors with overlapping imaging features. In this proof-of-concept study, we investigated using a deep learning classifier trained on a multicenter data set to differentiate these tumor types. We developed a patch-based 3D-DenseNet classifier, utilizing automated tumor segmentation. Given the heterogeneity of imaging data (and available sequences), we used all individually available preoperative imaging sequences to make the model robust to varying input. We compared the classifier to diagnostic assessments by five readers with varying experience in pediatric brain tumors. Overall, we included 195 preoperative MRIs from children with medulloblastoma ( n = 69) or pilocytic astrocytoma ( n = 126) across six university hospitals. In the 64-patient test set, the DenseNet classifier achieved a high AUC of 0.986, correctly predicting 62/64 (97%) diagnoses. It misclassified one case of each tumor type. Human reader accuracy ranged from 100% (expert neuroradiologist) to 80% (resident). The classifier performed significantly better than relatively inexperienced readers ( p < 0.05) and was on par with pediatric neuro-oncology experts. Our proof-of-concept study demonstrates a deep learning model based on automated tumor segmentation that can reliably preoperatively differentiate between medulloblastoma and pilocytic astrocytoma, even in heterogeneous data.
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- 2024
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38. Single brain metastases - prognostic factors and impact of residual tumor burden on overall survival.
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Baumgart L, Anetsberger A, Aftahy AK, Wiestler B, Bernhardt D, Combs SE, Meyer HS, Schneider G, Meyer B, and Gempt J
- Abstract
Background: Brain metastases (BM) are a common and challenging issue, with their incidence on the rise due to advancements in systemic therapies and increased patient survival. Most patients present with single BM, some of them without any further extracranial metastasis (i.e., solitary BM). The significance of postoperative intracranial tumor volume in the treatment of singular and solitary BM is still debated., Objective: This study aimed to determine the impact of resection and postoperative tumor burden on overall survival (OS) in patients with single BM., Methods: Patients with surgically treated single BM between 04/2007-01/2020 were retrospectively included. Residual tumor burden (RTB) was determined by manual segmentation of early postoperative brain MRI (72 h). Survival analyses were performed using Kaplan-Meier estimates for univariate analysis and Cox regression proportional hazards model for multivariate analysis, using preoperative Karnofsky performance status scale (KPSS), age, sex, RTB, incomplete resection and singular/solitary BM as covariates., Results: 340 patients were included, median age 64 years (54-71). 119 patients (35%) had solitary BM, 221 (65%) singular BM. Complete resection (RTB=0) was achieved in 73%, median preoperative tumor burden was 11.2 cm3 (5-25), and RTB 0 cm3 (0-0.2). Median OS of patients with singular BM was 13 months (4-33) vs 20 months (5-92) for solitary BM; p=0.062. Multivariate analysis revealed singular BM as independent risk factor for poorer OS: HR 1.840 (1.202-2.817), p=0.005. Complete vs. incomplete resection showed no significant OS difference (13 vs. 13 months, p=0.737). When focusing on solitary BM, complete resection led to a longer OS than incomplete resection (21 vs. 8 months), without statistical significance(p=0.250). Achieving RTB=0 resulted in higher OS for patients with solitary BM compared to singular BM (21 vs. 12 months, p=0.027). Patients who received postoperative radiotherapy (RT) had significantly longer OS compared to those without it (14 vs. 4 months, p<0.001), with favorable OS in those receiving stereotactic radiosurgery (SRS) (15 months (3-42), p<0.001) or hypofractionated stereotactic radiotherapy (HSRT)., Conclusion: When complete intracranial tumor resection RTB=0 is achieved, patients with solitary BM have a favorable outcome compared to singular BM. Singular BM was confirmed as independent risk factor. There is a strong presumption that complete resection leads to an improved oncological prognosis. Patients with solitary BM tend to benefit with a favorable outcome following complete resection. Hence, surgical resection should be considered as a treatment option for patients presenting with either no or minimal extracranial disease. Furthermore, the highly favorable impact of postoperative RT on OS was demonstrated and confirmed, especially with SRS or HSRT., Competing Interests: BM works as consultants for Brainlab Brainlab AG, Feldkirchen. In addition, BM works as a consultant for Medtronic, Spineart, Icotec, Relievant and Depuy/Synthes, as a member of their advisory boards. Furthermore, BM reports financial relationships with Medtronic, Ulrich Medical, Brainlab, Spineart, Icotec, Relievant and Depuy/Synthes. He received personal fees and research grants for clinical studies from Medtronic, Ulrich Medical, Brainlab, Icotec and Relievant. All of this happened independently of the submitted work. BM receives royalties from and holds a patent with Spineart. All of the named potential conflicts of interest are unrelated to this study. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Baumgart, Anetsberger, Aftahy, Wiestler, Bernhardt, Combs, Meyer, Schneider, Meyer and Gempt.)
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- 2024
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39. Intensity scaling of conventional brain magnetic resonance images avoiding cerebral reference regions: A systematic review.
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Wiltgen T, Voon C, Van Leemput K, Wiestler B, and Mühlau M
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Background: Conventional brain magnetic resonance imaging (MRI) produces image intensities that have an arbitrary scale, hampering quantification. Intensity scaling aims to overcome this shortfall. As neurodegenerative and inflammatory disorders may affect all brain compartments, reference regions within the brain may be misleading. Here we summarize approaches for intensity scaling of conventional T1-weighted (w) and T2w brain MRI avoiding reference regions within the brain., Methods: Literature was searched in the databases of Scopus, PubMed, and Web of Science. We included only studies that avoided reference regions within the brain for intensity scaling and provided validating evidence, which we divided into four categories: 1) comparative variance reduction, 2) comparative correlation with clinical parameters, 3) relation to quantitative imaging, or 4) relation to histology., Results: Of the 3825 studies screened, 24 fulfilled the inclusion criteria. Three studies used scaled T1w images, 2 scaled T2w images, and 21 T1w/T2w-ratio calculation (with double counts). A robust reduction in variance was reported. Twenty studies investigated the relation of scaled intensities to different types of quantitative imaging. Statistically significant correlations with clinical or demographic data were reported in 8 studies. Four studies reporting the relation to histology gave no clear picture of the main signal driver of conventional T1w and T2w MRI sequences., Conclusions: T1w/T2w-ratio calculation was applied most often. Variance reduction and correlations with other measures suggest a biologically meaningful signal harmonization. However, there are open methodological questions and uncertainty on its biological underpinning. Validation evidence on other scaling methods is even sparser., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wiltgen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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- 2024
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40. LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation.
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Wiltgen T, McGinnis J, Schlaeger S, Kofler F, Voon C, Berthele A, Bischl D, Grundl L, Will N, Metz M, Schinz D, Sepp D, Prucker P, Schmitz-Koep B, Zimmer C, Menze B, Rueckert D, Hemmer B, Kirschke J, Mühlau M, and Wiestler B
- Abstract
Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D-UNets. LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1w and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI additionally includes a lesion location annotation tool, labeling lesion location according to the 2017 McDonald criteria (periventricular, infratentorial, juxtacortical, subcortical). We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models. Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63-surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10mm
3 and 100mm3 . Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms., Competing Interests: Declaration of Competing Interests: The authors declare no competing interests.- Published
- 2024
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41. A Learnable Prior Improves Inverse Tumor Growth Modeling.
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Weidner J, Ezhov I, Balcerak M, Metz MC, Litvinov S, Kaltenbach S, Feiner L, Lux L, Kofler F, Lipkova J, Latz J, Rueckert D, Menze B, and Wiestler B
- Abstract
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95.
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- 2024
42. Deep Learning to Differentiate Benign and Malignant Vertebral Fractures at Multidetector CT.
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Foreman SC, Schinz D, El Husseini M, Goller SS, Weißinger J, Dietrich AS, Renz M, Metz MC, Feuerriegel GC, Wiestler B, Stahl R, Schwaiger BJ, Makowski MR, Kirschke JS, and Gersing AS
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- Humans, Female, Male, Aged, Reproducibility of Results, Retrospective Studies, Multidetector Computed Tomography, Hospitals, University, Deep Learning, Spinal Fractures diagnostic imaging
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Background Differentiating between benign and malignant vertebral fractures poses diagnostic challenges. Purpose To investigate the reliability of CT-based deep learning models to differentiate between benign and malignant vertebral fractures. Materials and Methods CT scans acquired in patients with benign or malignant vertebral fractures from June 2005 to December 2022 at two university hospitals were retrospectively identified based on a composite reference standard that included histopathologic and radiologic information. An internal test set was randomly selected, and an external test set was obtained from an additional hospital. Models used a three-dimensional U-Net encoder-classifier architecture and applied data augmentation during training. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with that of two residents and one fellowship-trained radiologist using the DeLong test. Results The training set included 381 patients (mean age, 69.9 years ± 11.4 [SD]; 193 male) with 1307 vertebrae (378 benign fractures, 447 malignant fractures, 482 malignant lesions). Internal and external test sets included 86 (mean age, 66.9 years ± 12; 45 male) and 65 (mean age, 68.8 years ± 12.5; 39 female) patients, respectively. The better-performing model of two training approaches achieved AUCs of 0.85 (95% CI: 0.77, 0.92) in the internal and 0.75 (95% CI: 0.64, 0.85) in the external test sets. Including an uncertainty category further improved performance to AUCs of 0.91 (95% CI: 0.83, 0.97) in the internal test set and 0.76 (95% CI: 0.64, 0.88) in the external test set. The AUC values of residents were lower than that of the best-performing model in the internal test set (AUC, 0.69 [95% CI: 0.59, 0.78] and 0.71 [95% CI: 0.61, 0.80]) and external test set (AUC, 0.70 [95% CI: 0.58, 0.80] and 0.71 [95% CI: 0.60, 0.82]), with significant differences only for the internal test set ( P < .001). The AUCs of the fellowship-trained radiologist were similar to those of the best-performing model (internal test set, 0.86 [95% CI: 0.78, 0.93; P = .39]; external test set, 0.71 [95% CI: 0.60, 0.82; P = .46]). Conclusion Developed models showed a high discriminatory power to differentiate between benign and malignant vertebral fractures, surpassing or matching the performance of radiology residents and matching that of a fellowship-trained radiologist. © RSNA, 2024 See also the editorial by Booz and D'Angelo in this issue.
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- 2024
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43. (Predictable) performance bias in unsupervised anomaly detection.
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Meissen F, Breuer S, Knolle M, Buyx A, Müller R, Kaissis G, Wiestler B, and Rückert D
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- Humans, Machine Learning, Algorithms, Hydrolases
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Background: With the ever-increasing amount of medical imaging data, the demand for algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models promise to aid in the crucial first step of disease detection. While previous studies have thoroughly explored fairness in supervised models in healthcare, for UAD, this has so far been unexplored., Methods: In this study, we evaluated how dataset composition regarding subgroups manifests in disparate performance of UAD models along multiple protected variables on three large-scale publicly available chest X-ray datasets. Our experiments were validated using two state-of-the-art UAD models for medical images. Finally, we introduced subgroup-AUROC (sAUROC), which aids in quantifying fairness in machine learning., Findings: Our experiments revealed empirical "fairness laws" (similar to "scaling laws" for Transformers) for training-dataset composition: Linear relationships between anomaly detection performance within a subpopulation and its representation in the training data. Our study further revealed performance disparities, even in the case of balanced training data, and compound effects that exacerbate the drop in performance for subjects associated with multiple adversely affected groups., Interpretation: Our study quantified the disparate performance of UAD models against certain demographic subgroups. Importantly, we showed that this unfairness cannot be mitigated by balanced representation alone. Instead, the representation of some subgroups seems harder to learn by UAD models than that of others. The empirical "fairness laws" discovered in our study make disparate performance in UAD models easier to estimate and aid in determining the most desirable dataset composition., Funding: European Research Council Deep4MI., Competing Interests: Declaration of interests B.W. has received speaker honoraria from Novartis, Bayer and Philips. He holds a patent related to Apogenix APG101 for glioblastoma treatment and is a stockholder of the company “Need”., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
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- 2024
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44. Personalized Predictions of Glioblastoma Infiltration: Mathematical Models, Physics-Informed Neural Networks and Multimodal Scans.
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Zhang RZ, Ezhov I, Balcerak M, Zhu A, Wiestler B, Menze B, and Lowengrub J
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Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized radiotherapy treatment plans.Mathematical models of GBM growth can complement the data in the prediction of spatial distributions of tumor cells. However, this requires estimating patient-specific parameters of the model from clinical data, which is a challenging inverse problem due to limited temporal data and the limited time between imaging and diagnosis. This work proposes a method that uses Physics-Informed Neural Networks (PINNs) to estimate patient-specific parameters of a reaction-diffusion PDE model of GBM growth from a single 3D structural MRI snapshot. PINNs embed both the data and the PDE into a loss function, thus integrating theory and data. Key innovations include the identification and estimation of characteristic non-dimensional parameters, a pre-training step that utilizes the non-dimensional parameters and a fine-tuning step to determine the patient specific parameters. Additionally, the diffuse domain method is employed to handle the complex brain geometry within the PINN framework. Our method is validated both on synthetic and patient datasets, and shows promise for real-time parametric inference in the clinical setting for personalized GBM treatment.
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- 2024
45. Navigating Post-Operative Outcomes: A Comprehensive Reframing of an Original Graded Prognostic Assessment in Patients with Brain Metastases.
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Goldberg M, Mondragon-Soto MG, Dieringer L, Altawalbeh G, Pöser P, Baumgart L, Wiestler B, Gempt J, Meyer B, and Aftahy AK
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Background: Graded Prognostic Assessment (GPA) has been proposed for various brain metastases (BMs) tailored to the primary histology and molecular profiles. However, it does not consider whether patients have been operated on or not and does not include surgical outcomes as prognostic factors. The residual tumor burden (RTB) is a strong predictor of overall survival. We validated the GPA score and introduced "volumetric GPA" in the largest cohort of operated patients and further explored the role of RTB as an additional prognostic factor., Methods: A total of 630 patients with BMs between 2007 and 2020 were included. The four GPA components were analyzed. The validity of the original score was assessed using Cox regression, and a modified index incorporating RTB was developed by comparing the accuracy, sensitivity, specificity, F1-score, and AUC parameters., Results: GPA categories showed an association with survival: age ( p < 0.001, hazard ratio (HR) 2.9, 95% confidence interval (CI) 2.5-3.3), Karnofsky performance status (KPS) ( p < 0.001, HR 1.3, 95% CI 1.2-1.5), number of BMs ( p = 0.019, HR 1.4, 95% CI 1.1-1.8), and the presence of extracranial manifestation ( p < 0.001, HR 3, 95% CI 1.6-2.5). The median survival for GPA 0-1 was 4 months; for GPA 1.5-2, it was 12 months; for GPA 2.5-3, it was 21 months; and for GPA 3.5-4, it was 38 months ( p < 0.001). RTB was identified as an independent prognostic factor. A cut-off of 2 cm
3 was used for further analysis, which showed a median survival of 6 months (95% CI 4-8) vs. 13 months (95% CI 11-14, p < 0.001) for patients with RTB > 2 cm3 and <2 cm3 , respectively. RTB was added as an additional component for a modified volumetric GPA score. The survival rates with the modified GPA score were: GPA 0-1: 4 months, GPA 1.5-2: 7 months, GPA 2.5-3: 18 months, and GPA 3.5-4: 34 months. Both scores showed good stratification, with the new score showed a trend towards better discrimination in patients with more favorable prognoses., Conclusion: The prognostic value of the original GPA was confirmed in our cohort of patients who underwent surgery for BM. The RTB was identified as a parameter of high prognostic significance and was incorporated into an updated "volumetric GPA". This score provides a novel tool for prognosis and clinical decision making in patients undergoing surgery. This method may be useful for stratification and patient selection for further treatment and in future clinical trials.- Published
- 2024
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46. Impact of function-guided glioma treatment on oncological outcome in the elderly.
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Albrecht C, Baumgart L, Schroeder A, Wiestler B, Meyer B, Krieg SM, and Ille S
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Introduction: Many patients with high-grade gliomas (HGG) are of older age., Research Question: We hypothesize that pre- and intraoperative mapping and monitoring preserve functional status in elderly patients while gross total resection (GTR) is the aim, resulting in overall survival (OS) rates comparable to the general population with HGG., Material and Methods: We subdivided a prospective cohort of 168 patients above 65 years with eloquent high-grade gliomas into four groups ([years/cases] 1: 65-69/58; 2: 70-74/47; 3: 75-79/43; 4: >79/20). All patients underwent preoperative noninvasive mapping, which was also used for decision-making, intraoperative neuromonitoring in 138 cases, direct cortical and/or subcortical motor mapping in 66 and 50 cases, and awake language mapping in 11 cases., Results: GTR and subtotal resection (STR) could be achieved in 65% and 28%, respectively. Stereotactic biopsy was performed in 8% of cases. Postoperatively, we found transient and permanent functional deficits in 13% and 11% of cases. Postoperative Karnofsky Performance Scale (KPS) did not differ between subgroups. Patients with long-term follow-up (51%) had a progression-free survival of 5.5 (1-47) months and an overall survival of 10.5 (0-86) months., Discussion and Conclusion: The interdisciplinary glioma treatment in the elderly is less age-dependent but must be adjusted to the functional status. Function-guided surgical resections could be performed as usual, with maximal tumor resection being the primary goal. However, less network capacity in the elderly to compensate for deficits might cause higher rates of permanent deficits in this group of patients with more fast-growing malignant gliomas., (© 2024 The Authors.)
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- 2024
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47. Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021.
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Sudre CH, Van Wijnen K, Dubost F, Adams H, Atkinson D, Barkhof F, Birhanu MA, Bron EE, Camarasa R, Chaturvedi N, Chen Y, Chen Z, Chen S, Dou Q, Evans T, Ezhov I, Gao H, Girones Sanguesa M, Gispert JD, Gomez Anson B, Hughes AD, Ikram MA, Ingala S, Jaeger HR, Kofler F, Kuijf HJ, Kutnar D, Lee M, Li B, Lorenzini L, Menze B, Molinuevo JL, Pan Y, Puybareau E, Rehwald R, Su R, Shi P, Smith L, Tillin T, Tochon G, Urien H, van der Velden BHM, van der Velpen IF, Wiestler B, Wolters FJ, Yilmaz P, de Groot M, Vernooij MW, and de Bruijne M
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- Humans, Reproducibility of Results, Cerebral Hemorrhage, Computers, Magnetic Resonance Imaging methods, Cerebral Small Vessel Diseases diagnostic imaging
- Abstract
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Carole H Sudre reports financial support was provided by Alzheimer’s Society. Marleen de Bruijne reports financial support was provided by Netherlands Organisation for Scientific Research. Kimberlin van Wijnen reports financial support was provided by Netherlands Organisation for Scientific Research. Shuai Chen reports financial support was provided by Netherlands Organisation for Scientific Research. Silvia Ingala, Luigi Lorenzini, Frederik Barkhof reports financial support was provided by EU Framework Horizon 2020 Research and Innovation. Florian Kofler, Bjoern Menze, Benedikt Wiestler reports financial support was provided by Deutsche Forschung Gemeinschaft. Hugo J Kuijf reports financial support was provided by Galen and Hilary Weston Foundation. Juan Domingo Gispert reports financial support was provided by Spanish Ministry of Science and Innovation. Bjoern Menze reports financial support was provided by Helmut Horten Foundation. Florian Dubost reports financial support was provided by Netherlands Organisation for Health Research and Development. Ivan Ezhov reports financial support was provided by DComEx. Marius de Groot reports a relationship with GlaxoSmithKline that includes: employment and equity or stocks. Jose Luis Molinuevo reports a relationship with Lundbeck that includes: employment. Jose Luis Molinuevo reports a relationship with Roche Diagnostics that includes: board membership, consulting or advisory, and speaking and lecture fees. Jose Luis Molinuevo reports a relationship with Genentech Inc that includes: board membership, consulting or advisory, and speaking and lecture fees. Jose Luis Molinuevo reports a relationship with Novartis that includes: board membership, consulting or advisory, and speaking and lecture fees. Jose Luis Molinuevo reports a relationship with Oryzon Genomics, S.A. that includes: board membership, consulting or advisory, and speaking and lecture fees. Juan Domingo Gispert reports a relationship with GE Healthcare that includes: funding grants. Juan Domingo Gispert reports a relationship with Roche Diagnostics that includes: funding grants. Juan Domingo Gispert reports a relationship with Hoffmann-La Roche Limited that includes: funding grants. Juan Domingo Gispert reports a relationship with Biogen that includes: speaking and lecture fees. Juan Domingo Gispert reports a relationship with Philips that includes: speaking and lecture fees. Jose Luis Molinuevo reports a relationship with Lilly that includes: board membership, consulting or advisory, and speaking and lecture fees. Jose Luis Molinuevo reports a relationship with Green Valley that includes: board membership, consulting or advisory, and speaking and lecture fees. Jose Luis Molinuevo reports a relationship with Janssen that includes: board membership, consulting or advisory, and speaking and lecture fees. Jose Luis Molinuevo reports a relationship with MSD that includes: board membership, consulting or advisory, and speaking and lecture fees. Jose Luis Molinuevo reports a relationship with Alector that includes: board membership, consulting or advisory, and speaking and lecture fees. Jose Luis Molinuevo reports a relationship with BioCross that includes: board membership, consulting or advisory, and speaking and lecture fees. Jose Luis Molinuevo reports a relationship with Eisai that includes: board membership, consulting or advisory, and speaking and lecture fees. Jose Luis Molinuevo reports a relationship with ProMIS Neurosciences Inc that includes: board membership, consulting or advisory, and speaking and lecture fees. NVIDIA and Icometrix provided the challenge prizes., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)
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- 2024
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48. Quantitative susceptibility mapping in multiple sclerosis: A systematic review and meta-analysis.
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Voon CC, Wiltgen T, Wiestler B, Schlaeger S, and Mühlau M
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- Humans, Gray Matter diagnostic imaging, Gray Matter pathology, Brain diagnostic imaging, Brain pathology, Multiple Sclerosis diagnostic imaging, Multiple Sclerosis pathology, Magnetic Resonance Imaging methods
- Abstract
Background: Quantitative susceptibility mapping (QSM) is a quantitative measure based on magnetic resonance imaging sensitive to iron and myelin content. This makes QSM a promising non-invasive tool for multiple sclerosis (MS) in research and clinical practice., Objective: We performed a systematic review and meta-analysis on the use of QSM in MS., Methods: Our review was prospectively registered on PROSPERO (CRD42022309563). We searched five databases for studies published between inception and 30th April 2023. We identified 83 English peer-reviewed studies that applied QSM images on MS cohorts. Fifty-five included studies had at least one of the following outcome measures: deep grey matter QSM values in MS, either compared to healthy controls (HC) (k = 13) or correlated with the score on the Expanded Disability Status Scale (EDSS) (k = 7), QSM lesion characteristics (k = 22) and their clinical correlates (k = 17), longitudinal correlates (k = 11), histological correlates (k = 7), or correlates with other imaging techniques (k = 12). Two meta-analyses on deep grey matter (DGM) susceptibility data were performed, while the remaining findings could only be analyzed descriptively., Results: After outlier removal, meta-analyses demonstrated a significant increase in the basal ganglia susceptibility (QSM values) in MS compared to HC, caudate (k = 9, standardized mean difference (SDM) = 0.54, 95 % CI = 0.39-0.70, I
2 = 46 %), putamen (k = 9, SDM = 0.38, 95 % CI = 0.19-0.57, I2 = 59 %), and globus pallidus (k = 9, SDM = 0.48, 95 % CI = 0.28-0.67, I2 = 60 %), whereas thalamic QSM values exhibited a significant reduction (k = 12, SDM = -0.39, 95 % CI = -0.66--0.12, I2 = 84 %); these susceptibility differences in MS were independent of age. Further, putamen QSM values positively correlated with EDSS (k = 4, r = 0.36, 95 % CI = 0.16-0.53, I2 = 0 %). Regarding rim lesions, four out of seven studies, representing 73 % of all patients, reported rim lesions to be associated with more severe disability. Moreover, lesion development from initial detection to the inactive stage is paralleled by increasing, plateauing (after about two years), and gradually decreasing QSM values, respectively. Only one longitudinal study provided clinical outcome measures and found no association. Histological data suggest iron content to be the primary source of QSM values in DGM and at the edges of rim lesions; further, when also considering data from myelin water imaging, the decrease of myelin is likely to drive the increase of QSM values within WM lesions., Conclusions: We could provide meta-analytic evidence for DGM susceptibility changes in MS compared to HC; basal ganglia susceptibility is increased and, in the putamen, associated with disability, while thalamic susceptibility is decreased. Beyond these findings, further investigations are necessary to establish the role of QSM in MS for research or even clinical routine., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)- Published
- 2024
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49. Cortical Thin Patch Fraction Reflects Disease Burden in MS: The Mosaic Approach.
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Tahedl M, Wiltgen T, Voon CC, Berthele A, Kirschke JS, Hemmer B, Mühlau M, Zimmer C, and Wiestler B
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- Humans, Artificial Intelligence, Magnetic Resonance Imaging methods, Biomarkers, Atrophy pathology, Brain pathology, Disease Progression, Multiple Sclerosis pathology
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Background and Purpose: GM pathology plays an essential role in MS disability progression, emphasizing the importance of neuroradiologic biomarkers to capture the heterogeneity of cortical disease burden. This study aimed to assess the validity of a patch-wise, individual interpretation of cortical thickness data to identify GM pathology, the "mosaic approach," which was previously suggested as a biomarker for assessing and localizing atrophy., Materials and Methods: We investigated the mosaic approach in a cohort of 501 patients with MS with respect to 89 internal and 651 external controls. The resulting metric of the mosaic approach is the so-called thin patch fraction, which is an estimate of overall cortical disease burden per patient. We evaluated the mosaic approach with respect to the following: 1) discrimination between patients with MS and controls, 2) classification between different MS phenotypes, and 3) association with established biomarkers reflecting MS disease burden, using general linear modeling., Results: The thin patch fraction varied significantly between patients with MS and healthy controls and discriminated among MS phenotypes. Furthermore, the thin patch fraction was associated with disease burden, including the Expanded Disability Status Scale, cognitive and fatigue scores, and lesion volume., Conclusions: This study demonstrates the validity of the mosaic approach as a neuroradiologic biomarker in MS. The output of the mosaic approach, namely the thin patch fraction, is a candidate biomarker for assessing and localizing cortical GM pathology. The mosaic approach can furthermore enhance the development of a personalized cortical MS biomarker, given that the thin patch fraction provides a feature on which artificial intelligence methods can be trained. Most important, we showed the validity of the mosaic approach when referencing data with respect to external control MR imaging repositories., (© 2024 by American Journal of Neuroradiology.)
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- 2023
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50. Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model.
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Metz MC, Ezhov I, Peeken JC, Buchner JA, Lipkova J, Kofler F, Waldmannstetter D, Delbridge C, Diehl C, Bernhardt D, Schmidt-Graf F, Gempt J, Combs SE, Zimmer C, Menze B, and Wiestler B
- Abstract
Background: The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation., Methods: One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration ( D
w ) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans., Results: The parameter ratio Dw /ρ ( P < .05 in TCGA) as well as the simulated tumor volume ( P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans., Conclusions: Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future., Competing Interests: M.M. and F.S. serve as part-time consultants for Novocure GmbH., (© The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)- Published
- 2023
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