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1. Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation

Author

LaBella, Dominic, Schumacher, Katherine, Mix, Michael, Leu, Kevin, McBurney-Lin, Shan, Nedelec, Pierre, Villanueva-Meyer, Javier, Shapey, Jonathan, Vercauteren, Tom, Chia, Kazumi, Al-Salihi, Omar, Leu, Justin, Halasz, Lia, Velichko, Yury, Wang, Chunhao, Kirkpatrick, John, Floyd, Scott, Reitman, Zachary J., Mullikin, Trey, Bagci, Ulas, Sachdev, Sean, Hattangadi-Gluth, Jona A., Seibert, Tyler, Farid, Nikdokht, Puett, Connor, Pease, Matthew W., Shiue, Kevin, Anwar, Syed Muhammad, Faghani, Shahriar, Haider, Muhammad Ammar, Warman, Pranav, Albrecht, Jake, Jakab, András, Moassefi, Mana, Chung, Verena, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Huang, Christina, Coley, Aaron, Ghanta, Siddharth, Schneider, Alex, Sharp, Conrad, Saluja, Rachit, Kofler, Florian, Lohmann, Philipp, Vollmuth, Phillipp, Gagnon, Louis, Adewole, Maruf, Li, Hongwei Bran, Kazerooni, Anahita Fathi, Tahon, Nourel Hoda, Anazodo, Udunna, Moawad, Ahmed W., Menze, Bjoern, Linguraru, Marius George, Aboian, Mariam, Wiestler, Benedikt, Baid, Ujjwal, Conte, Gian-Marco, Rauschecker, Andreas M., Nada, Ayman, Abayazeed, Aly H., Huang, Raymond, de Verdier, Maria Correia, Rudie, Jeffrey D., Bakas, Spyridon, and Calabrese, Evan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk postoperative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For preoperative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for postoperative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using an adapted lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and Computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes., Comment: 14 pages, 9 figures, 1 table

Published
2024

2. Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

Author

LaBella, Dominic, Baid, Ujjwal, Khanna, Omaditya, McBurney-Lin, Shan, McLean, Ryan, Nedelec, Pierre, Rashid, Arif, Tahon, Nourel Hoda, Altes, Talissa, Bhalerao, Radhika, Dhemesh, Yaseen, Godfrey, Devon, Hilal, Fathi, Floyd, Scott, Janas, Anastasia, Kazerooni, Anahita Fathi, Kirkpatrick, John, Kent, Collin, Kofler, Florian, Leu, Kevin, Maleki, Nazanin, Menze, Bjoern, Pajot, Maxence, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Velichko, Yury, Wang, Chunhao, Warman, Pranav, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Anwar, Syed Muhammad, Bergquist, Timothy, Chen, Sully Francis, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Khalili, Nastaran, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Liu, Xinyang, Mahtabfar, Aria, Meier, Zeke, Moawad, Ahmed W., Mongan, John, Piraud, Marie, Shinohara, Russell Takeshi, Wiggins, Walter F., Abayazeed, Aly H., Akinola, Rachel, Jakab, András, Bilello, Michel, de Verdier, Maria Correia, Crivellaro, Priscila, Davatzikos, Christos, Farahani, Keyvan, Freymann, John, Hess, Christopher, Huang, Raymond, Lohmann, Philipp, Moassefi, Mana, Pease, Matthew W., Vollmuth, Phillipp, Sollmann, Nico, Diffley, David, Nandolia, Khanak K., Warren, Daniel I., Hussain, Ali, Fehringer, Pascal, Bronstein, Yulia, Deptula, Lisa, Stein, Evan G., Taherzadeh, Mahsa, de Oliveira, Eduardo Portela, Haughey, Aoife, Kontzialis, Marinos, Saba, Luca, Turner, Benjamin, Brüßeler, Melanie M. T., Ansari, Shehbaz, Gkampenis, Athanasios, Weiss, David Maximilian, Mansour, Aya, Shawali, Islam H., Yordanov, Nikolay, Stein, Joel M., Hourani, Roula, Moshebah, Mohammed Yahya, Abouelatta, Ahmed Magdy, Rizvi, Tanvir, Willms, Klara, Martin, Dann C., Okar, Abdullah, D'Anna, Gennaro, Taha, Ahmed, Sharifi, Yasaman, Faghani, Shahriar, Kite, Dominic, Pinho, Marco, Haider, Muhammad Ammar, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Alonso-Basanta, Michelle, Villanueva-Meyer, Javier, Rauschecker, Andreas M., Nada, Ayman, Aboian, Mariam, Flanders, Adam E., Wiestler, Benedikt, Bakas, Spyridon, and Calabrese, Evan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps., Comment: 16 pages, 11 tables, 10 figures, MICCAI

Published
2024

3. The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Author

Moawad, Ahmed W., Janas, Anastasia, Baid, Ujjwal, Ramakrishnan, Divya, Saluja, Rachit, Ashraf, Nader, Jekel, Leon, Amiruddin, Raisa, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Aneja, Sanjay, Anwar, Syed Muhammad, Bergquist, Timothy, Calabrese, Evan, Chiang, Veronica, Chung, Verena, Conte, Gian Marco Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Kazerooni, Anahita Fathi, Kofler, Florian, Krantchev, Kiril, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Link, Katherine E., Liu, Xinyang, Maleki, Nazanin, Meier, Zeke, Menze, Bjoern H, Moy, Harrison, Osenberg, Klara, Piraud, Marie, Reitman, Zachary, Shinohara, Russel Takeshi, Tahon, Nourel hoda, Nada, Ayman, Velichko, Yuri S., Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter, Shafique, Umber, Willms, Klara, Avesta, Arman, Bousabarah, Khaled, Chakrabarty, Satrajit, Gennaro, Nicolo, Holler, Wolfgang, Kaur, Manpreet, LaMontagne, Pamela, Lin, MingDe, Lost, Jan, Marcus, Daniel S., Maresca, Ryan, Merkaj, Sarah, Nada, Ayaman, Pedersen, Gabriel Cassinelli, von Reppert, Marc, Sotiras, Aristeidis, Teytelboym, Oleg, Tillmans, Niklas, Westerhoff, Malte, Youssef, Ayda, Godfrey, Devon, Floyd, Scott, Rauschecker, Andreas, Villanueva-Meyer, Javier, Pflüger, Irada, Cho, Jaeyoung, Bendszus, Martin, Brugnara, Gianluca, Cramer, Justin, Perez-Carillo, Gloria J. Guzman, Johnson, Derek R., Kam, Anthony, Kwan, Benjamin Yin Ming, Lai, Lillian, Lall, Neil U., Memon, Fatima, Patro, Satya Narayana, Petrovic, Bojan, So, Tiffany Y., Thompson, Gerard, Wu, Lei, Schrickel, E. Brooke, Bansal, Anu, Barkhof, Frederik, Besada, Cristina, Chu, Sammy, Druzgal, Jason, Dusoi, Alexandru, Farage, Luciano, Feltrin, Fabricio, Fong, Amy, Fung, Steve H., Gray, R. Ian, Ikuta, Ichiro, Iv, Michael, Postma, Alida A., Mahajan, Amit, Joyner, David, Krumpelman, Chase, Letourneau-Guillon, Laurent, Lincoln, Christie M., Maros, Mate E., Miller, Elka, Morón, Fanny, Nimchinsky, Esther A., Ozsarlak, Ozkan, Patel, Uresh, Rohatgi, Saurabh, Saha, Atin, Sayah, Anousheh, Schwartz, Eric D., Shih, Robert, Shiroishi, Mark S., Small, Juan E., Tanwar, Manoj, Valerie, Jewels, Weinberg, Brent D., White, Matthew L., Young, Robert, Zohrabian, Vahe M., Azizova, Aynur, Brüßeler, Melanie Maria Theresa, Fehringer, Pascal, Ghonim, Mohanad, Ghonim, Mohamed, Gkampenis, Athanasios, Okar, Abdullah, Pasquini, Luca, Sharifi, Yasaman, Singh, Gagandeep, Sollmann, Nico, Soumala, Theodora, Taherzadeh, Mahsa, Yordanov, Nikolay, Vollmuth, Philipp, Foltyn-Dumitru, Martha, Malhotra, Ajay, Abayazeed, Aly H., Dellepiane, Francesco, Lohmann, Philipp, Pérez-García, Víctor M., Elhalawani, Hesham, Al-Rubaiey, Sanaria, Armindo, Rui Duarte, Ashraf, Kholod, Asla, Moamen M., Badawy, Mohamed, Bisschop, Jeroen, Lomer, Nima Broomand, Bukatz, Jan, Chen, Jim, Cimflova, Petra, Corr, Felix, Crawley, Alexis, Deptula, Lisa, Elakhdar, Tasneem, Shawali, Islam H., Faghani, Shahriar, Frick, Alexandra, Gulati, Vaibhav, Haider, Muhammad Ammar, Hierro, Fátima, Dahl, Rasmus Holmboe, Jacobs, Sarah Maria, Hsieh, Kuang-chun Jim, Kandemirli, Sedat G., Kersting, Katharina, Kida, Laura, Kollia, Sofia, Koukoulithras, Ioannis, Li, Xiao, Abouelatta, Ahmed, Mansour, Aya, Maria-Zamfirescu, Ruxandra-Catrinel, Marsiglia, Marcela, Mateo-Camacho, Yohana Sarahi, McArthur, Mark, McDonnell, Olivia, McHugh, Maire, Moassefi, Mana, Morsi, Samah Mostafa, Muntenu, Alexander, Nandolia, Khanak K., Naqvi, Syed Raza, Nikanpour, Yalda, Alnoury, Mostafa, Nouh, Abdullah Mohamed Aly, Pappafava, Francesca, Patel, Markand D., Petrucci, Samantha, Rawie, Eric, Raymond, Scott, Roohani, Borna, Sabouhi, Sadeq, Sanchez-Garcia, Laura M., Shaked, Zoe, Suthar, Pokhraj P., Altes, Talissa, Isufi, Edvin, Dhermesh, Yaseen, Gass, Jaime, Thacker, Jonathan, Tarabishy, Abdul Rahman, Turner, Benjamin, Vacca, Sebastiano, Vilanilam, George K., Warren, Daniel, Weiss, David, Worede, Fikadu, Yousry, Sara, Lerebo, Wondwossen, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Bakas, Spyridon, Rudie, Jeffrey D., and Aboian, Mariam

Subjects
Quantitative Biology - Other Quantitative Biology, Electrical Engineering and Systems Science - Image and Video Processing
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. 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. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.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.

Published
2023

4. Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics

Author

Floca, Ralf, Bohn, Jonas, Haux, Christian, Wiestler, Benedikt, Zöllner, Frank G., Reinke, Annika, Weiß, Jakob, Nolden, Marco, Albert, Steffen, Persigehl, Thorsten, Norajitra, Tobias, Baeßler, Bettina, Dewey, Marc, Braren, Rickmer, Büchert, Martin, Fallenberg, Eva Maria, Galldiks, Norbert, Gerken, Annika, Götz, Michael, Hahn, Horst K., Haubold, Johannes, Haueise, Tobias, Große Hokamp, Nils, Ingrisch, Michael, Iuga, Andra-Iza, Janoschke, Marco, Jung, Matthias, Kiefer, Lena Sophie, Lohmann, Philipp, Machann, Jürgen, Moltz, Jan Hendrik, Nattenmüller, Johanna, Nonnenmacher, Tobias, Oerther, Benedict, Othman, Ahmed E., Peisen, Felix, Schick, Fritz, Umutlu, Lale, Wichtmann, Barbara D., Zhao, Wenzhao, Caspers, Svenja, Schlemmer, Heinz-Peter, Schlett, Christopher L., Maier-Hein, Klaus, and Bamberg, Fabian

Published
2024
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6. Advances in PET imaging for meningioma patients.

Author

Galldiks, Norbert, Albert, Nathalie L, Wollring, Michael, Werner, Jan-Michael, Lohmann, Philipp, Villanueva-Meyer, Javier E, Fink, Gereon R, Langen, Karl-Josef, and Tonn, Joerg-Christian

Subjects
DOTATATE, FDG, FLT, SiTATE, amino acid PET, somatostatin receptor-expression, Brain Cancer, Brain Disorders, Biomedical Imaging, Cancer, Rare Diseases, Neurosciences, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies
Abstract

In patients with meningioma, diagnosis and treatment planning are predominantly based on anatomical imaging using MRI or CT. Constraints of these imaging modalities include precise meningioma delineation-especially at the skull base, in the case of trans-osseus growth, and in tumors with complex geometry-and the differentiation of post-therapeutic reactive changes from meningioma relapse. Advanced metabolic imaging using PET may help to characterize specific metabolic and cellular features providing additional information beyond the information derived from anatomical imaging alone. Accordingly, the use of PET in meningioma patients is steadily increasing. This review summarizes recent advances in PET imaging helpful for improving the clinical management of patients with meningioma.

Published
2023

14. MRI and18F-FET PET for Multimodal Treatment Monitoring in Patients with Brain Metastases: A Cost-Effectiveness Analysis

Author

Rosen, Jurij, primary, Werner, Jan-Michael, additional, Ceccon, Garry S., additional, Rosen, Elena K., additional, Wollring, Michael M., additional, Stetter, Isabelle, additional, Lohmann, Philipp, additional, Mottaghy, Felix M., additional, Fink, Gereon R., additional, Langen, Karl-Josef, additional, and Galldiks, Norbert, additional

Published
2024
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20. Treatment-associated imaging changes in newly diagnosed MGMT promoter-methylated glioblastoma undergoing chemoradiation with or without cilengitide

Author

Flies, Christina Maria, Friedrich, Michel, Lohmann, Philipp, van Garderen, Karin Alida, Smits, Marion, Tonn, Joerg-Christian, Weller, Michael, Galldiks, Norbert, Snijders, Tom Jan, Flies, Christina Maria, Friedrich, Michel, Lohmann, Philipp, van Garderen, Karin Alida, Smits, Marion, Tonn, Joerg-Christian, Weller, Michael, Galldiks, Norbert, and Snijders, Tom Jan

Abstract

Background Radiological progression may originate from progressive disease (PD) or pseudoprogression/treatment-associated changes. We assessed radiological progression in O6-methylguanine-DNA methyltransferase (MGMT) promoter-methylated glioblastoma treated with standard-of-care chemoradiotherapy with or without the integrin inhibitor cilengitide according to the modified response assessment in neuro-oncology (RANO) criteria of 2017.Methods Patients with >= 3 follow-up MRIs were included. Preliminary PD was defined as a >= 25% increase of the sum of products of perpendicular diameters (SPD) of a new or increasing lesion compared to baseline. PD required a second >= 25% increase of the SPD. Treatment-associated changes require stable or regressing disease after preliminary PD.Results Of the 424 evaluable patients, 221 patients (52%) were randomized into the cilengitide and 203 patients (48%) into the control arm. After chemoradiation with or without cilengitide, preliminary PD occurred in 274 patients (65%) during available follow-up, and 88 of these patients (32%) had treatment-associated changes, whereas 67 patients (25%) had PD. The remaining 119 patients (43%) had no further follow-up after preliminary PD. Treatment-associated changes were more common in the cilengitide arm than in the standard-of-care arm (24% vs. 17%; relative risk, 1.3; 95% CI, 1.004-1.795; P = .047). Treatment-associated changes occurred mainly during the first 6 months after RT (54% after 3 months vs. 13% after 6 months).Conclusions With the modified RANO criteria, the rate of treatment-associated changes was low compared to previous studies in MGMT promoter-methylated glioblastoma. This rate was higher after cilengitide compared to standard-of-care treatment. Confirmatory scans, as recommended in the modified RANO criteria, were not always available reflecting current clinical practice.

Published
2024

21. Treatment-associated imaging changes in newly diagnosed $MGMT$ promoter-methylated glioblastoma undergoing chemoradiation with or without cilengitide

Author

Flies, Christina Maria; https://orcid.org/0000-0002-9160-4058, Friedrich, Michel, Lohmann, Philipp; https://orcid.org/0000-0002-5360-046X, van Garderen, Karin Alida, Smits, Marion; https://orcid.org/0000-0001-5563-2871, Tonn, Joerg-Christian; https://orcid.org/0009-0004-1756-423X, Weller, Michael; https://orcid.org/0000-0002-1748-174X, Galldiks, Norbert; https://orcid.org/0000-0002-2485-1796, Snijders, Tom Jan; https://orcid.org/0000-0003-0857-081X, Flies, Christina Maria; https://orcid.org/0000-0002-9160-4058, Friedrich, Michel, Lohmann, Philipp; https://orcid.org/0000-0002-5360-046X, van Garderen, Karin Alida, Smits, Marion; https://orcid.org/0000-0001-5563-2871, Tonn, Joerg-Christian; https://orcid.org/0009-0004-1756-423X, Weller, Michael; https://orcid.org/0000-0002-1748-174X, Galldiks, Norbert; https://orcid.org/0000-0002-2485-1796, and Snijders, Tom Jan; https://orcid.org/0000-0003-0857-081X

Abstract

Background: Radiological progression may originate from progressive disease (PD) or pseudoprogression/treatment-associated changes. We assessed radiological progression in O6-methylguanine-DNA methyltransferase (MGMT) promoter-methylated glioblastoma treated with standard-of-care chemoradiotherapy with or without the integrin inhibitor cilengitide according to the modified response assessment in neuro-oncology (RANO) criteria of 2017. Methods: Patients with ≥ 3 follow-up MRIs were included. Preliminary PD was defined as a ≥ 25% increase of the sum of products of perpendicular diameters (SPD) of a new or increasing lesion compared to baseline. PD required a second ≥25% increase of the SPD. Treatment-associated changes require stable or regressing disease after preliminary PD. Results: Of the 424 evaluable patients, 221 patients (52%) were randomized into the cilengitide and 203 patients (48%) into the control arm. After chemoradiation with or without cilengitide, preliminary PD occurred in 274 patients (65%) during available follow-up, and 88 of these patients (32%) had treatment-associated changes, whereas 67 patients (25%) had PD. The remaining 119 patients (43%) had no further follow-up after preliminary PD. Treatment-associated changes were more common in the cilengitide arm than in the standard-of-care arm (24% vs. 17%; relative risk, 1.3; 95% CI, 1.004–1.795; P = .047). Treatment-associated changes occurred mainly during the first 6 months after RT (54% after 3 months vs. 13% after 6 months). Conclusions: With the modified RANO criteria, the rate of treatment-associated changes was low compared to previous studies in MGMT promoter-methylated glioblastoma. This rate was higher after cilengitide compared to standard-of-care treatment. Confirmatory scans, as recommended in the modified RANO criteria, were not always available reflecting current clinical practice.

Published
2024

23. Multicentric 68Ga-PSMA PET radiomics for treatment response assessment of 177Lu-PSMA-617 radioligand therapy in patients with metastatic castrationresistant prostate cancer.

Author

Gutsche, Robin, Gülmüs, Gizem, Mottaghy, Felix M., Gärtner, Florian, Essler, Markus, Mallek, Dirk von, Ahmadzadehfar, Hojjat, Lohmann, Philipp, and Heinzel, Alexander

Subjects
CASTRATION-resistant prostate cancer, RANDOM forest algorithms, PEARSON correlation (Statistics), RADIOPHARMACEUTICALS, ACADEMIC medical centers, RESEARCH funding, RECEIVER operating characteristic curves, RADIOMICS, POSITRON emission tomography, CANCER patients, RETROSPECTIVE studies, AGE distribution, DESCRIPTIVE statistics, METASTASIS, KAPLAN-Meier estimator, LOG-rank test, PROSTATE-specific membrane antigen, MEDICAL records, ACQUISITION of data, MACHINE learning, OVERALL survival, PROPORTIONAL hazards models
Abstract

Objective: The treatment with 177Lutetium PSMA (177Lu-PSMA) in patients with metastatic castration-resistant prostate cancer (mCRPC) has recently been approved by the FDA and EMA. Since treatment success is highly variable between patients, the prediction of treatment response and identification of short- and long-term survivors after treatment could help tailor mCRPC diagnosis and treatment accordingly. The aim of this study is to investigate the value of radiomic parameters extracted from pretreatment 68Ga-PSMA PET images for the prediction of treatment response. Methods: A total of 45 mCRPC patients treated with 177Lu-PSMA-617 from two university hospital centers were retrospectively reviewed for this study. Radiomic features were extracted from the volumetric segmentations of metastases in the bone. A random forest model was trained and validated to predict treatment response based on age and conventionally used PET parameters, radiomic features and combinations thereof. Further, overall survival was predicted by using the identified radiomic signature and compared to a Cox regression model based on age and PET parameters. Results: The machine learning model based on a combined radiomic signature of three features and patient age achieved an AUC of 0.82 in 5-fold cross-validation and outperformed models based on age and PET parameters or radiomic features (AUC, 0.75 and 0.76, respectively). A Cox regression model based on this radiomic signature showed the best performance to predict overall survival (C-index, 0.67). Conclusion: Our results demonstrate that a machine learning model to predict response to 177Lu-PSMA treatment based on a combination of radiomics and patient age outperforms a model based on age and PET parameters. Moreover, the identified radiomic signature based on pretreatment 68Ga-PSMA PET images might be able to identify patients with an improved outcome and serve as a supportive tool in clinical decision making. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Structural connectome-based predictive modeling of cognitive deficits in treated glioma patients

Author

Friedrich, Michel, primary, Filss, Christian P, additional, Lohmann, Philipp, additional, Mottaghy, Felix M, additional, Stoffels, Gabriele, additional, Lucas, Carolin Weiss, additional, Ruge, Maximilian I, additional, Shah, N Jon, additional, Caspers, Svenja, additional, Langen, Karl-Josef, additional, Fink, Gereon R, additional, Galldiks, Norbert, additional, and Kocher, Martin, additional

Published
2023
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27. NIMG-05. CHARACTERIZATION OF LONG-TERM METABOLIC CHANGES OF BRAIN METASTASES AFTER TREATMENT WITH STEREOTACTIC RADIOSURGERY USING SERIAL FET PET IMAGING

Author

Wollring, Michael, primary, Lowis, Carsten, additional, Stoffels, Gabriele, additional, Werner, Jan-Michael, additional, Celik, Eren, additional, Baues, Christian, additional, Ruge, Maximilian, additional, Ruess, Daniel, additional, Fink, Gereon, additional, Langen, Karl-Josef, additional, Lohmann, Philipp, additional, and Galldiks, Norbert, additional

Published
2023
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29. NCOG-04. WHOLE-BRAIN STRUCTURAL CONNECTIVITY PREDICTS COGNITIVE DEFICITS IN PRETREATED PATIENTS WITH CNS WHO GRADE 3 OR 4 GLIOMAS

Author

Friedrich, Michel, primary, Stoffels, Gabriele, additional, Filss, Christian, additional, Lohmann, Philipp, additional, Mottaghy, Felix, additional, Lucas, Carolin Weiss, additional, Ruge, Maximilian, additional, Shah, Nadim, additional, Caspers, Svenja, additional, Langen, Karl-Josef, additional, Fink, Gereon, additional, Galldiks, Norbert, additional, and Kocher, Martin, additional

Published
2023
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35. Impact of improved dead time correction on the quantification accuracy of a dedicated BrainPET scanner.

Author

Issa, Ahlam Said Mohamad, Scheins, Jürgen, Tellmann, Lutz, Brambilla, Cláudia Régio, Lohmann, Philipp, Rota-Kops, Elena, Herzog, Hans, Neuner, Irene, Shah, N. Jon, and Lerche, Christoph

Subjects
IMAGING phantoms, POSITRON emission tomography, RADIOACTIVE tracers, CEREBRAL circulation, OPTICAL scanners, SCANNING systems, WHITE matter (Nerve tissue), CONTRAST-enhanced magnetic resonance imaging, TRANSCRANIAL Doppler ultrasonography
Abstract

Objective: Quantitative values derived from PET brain images are of high interest for neuroscientific applications. Insufficient DT correction (DTC) can lead to a systematic bias of the output parameters obtained by a detailed analysis of the time activity curves (TACs). The DTC method currently used for the Siemens 3T MR BrainPET insert is global, i.e., differences in DT losses between detector blocks are not considered, leading to inaccurate DTC and, consequently, to inaccurate measurements masked by a bias. However, following careful evaluation with phantom measurements, a new block-pairwise DTC method has demonstrated a higher degree of accuracy compared to the global DTC method. Approach: Differences between the global and the block-pairwise DTC method were studied in this work by applying several radioactive tracers. We evaluated the impact on [11C]ABP688, O-(2-[18F]fluoroethyl)-L-tyrosine (FET), and [15O]H2O TACs. Results: For [11C]ABP688, a relevant bias of between -0.0034 and -0.0053 ml/ (cm3 • min) was found in all studied brain regions for the volume of distribution (VT) when using the current global DTC method. For [18F]FET-PET, differences of up to 10% were observed in the tumor-to-brain ratio (TBRmax), these differences depend on the radial distance of the maximum from the PET isocenter. For [15O]H2O, differences between +4% and -7% were observed in the GM region. Average biases of -4.58%, -3.2%, and -1.2% for the regional cerebral blood flow (CBF (K1)), the rate constant k2, and the volume of distribution VT were observed, respectively. Conversely, in the white matter region, average biases of -4.9%, -7.0%, and 3.8% were observed for CBF (K1), k2, and VT, respectively. Conclusion: The bias introduced by the global DTC method leads to an overestimation in the studied quantitative parameters for all applications compared to the block-pairwise method. Significance: The observed differences between the two DTC methods are particularly relevant for research applications in neuroscientific studies as they affect the accuracy of quantitative Brain PET images. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Assessment of Brain Tumour Perfusion Using Early-Phase 18F-FET PET: Comparison with Perfusion-Weighted MRI.

Author

Filss, Christian P., Cramer, Julian, Löher, Saskia, Lohmann, Philipp, Stoffels, Gabriele, Stegmayr, Carina, Kocher, Martin, Heinzel, Alexander, Galldiks, Norbert, Wittsack, Hans J., Sabel, Michael, Neumaier, Bernd, Scheins, Jürgen, Shah, N. Jon, Meyer, Philipp T., Mottaghy, Felix M., and Langen, Karl-Josef

Subjects
BRAIN tumors, PERFUSION, BLOOD volume, MAGNETIC resonance imaging, FOUR-dimensional imaging, AMINO acids, MENINGIOMA
Abstract

Purpose: Morphological imaging using MRI is essential for brain tumour diagnostics. Dynamic susceptibility contrast (DSC) perfusion-weighted MRI (PWI), as well as amino acid PET, may provide additional information in ambiguous cases. Since PWI is often unavailable in patients referred for amino acid PET, we explored whether maps of relative cerebral blood volume (rCBV) in brain tumours can be extracted from the early phase of PET using O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET). Procedure: Using a hybrid brain PET/MRI scanner, PWI and dynamic 18F-FET PET were performed in 33 patients with cerebral glioma and four patients with highly vascularized meningioma. The time interval from 0 to 2 min p.i. was selected to best reflect the blood pool phase in 18F-FET PET. For each patient, maps of MR-rCBV, early 18F-FET PET (0–2 min p.i.) and late 18F-FET PET (20–40 min p.i.) were generated and coregistered. Volumes of interest were placed on the tumour (VOI-TU) and normal-appearing brain (VOI-REF). The correlation between tumour-to-brain ratios (TBR) of the different parameters was analysed. In addition, three independent observers evaluated MR-rCBV and early 18F-FET maps (18F-FET-rCBV) for concordance in signal intensity, tumour extent and intratumoural distribution. Results: TBRs calculated from MR-rCBV and 18F-FET-rCBV showed a significant correlation (r = 0.89, p < 0.001), while there was no correlation between late 18F-FET PET and MR-rCBV (r = 0.24, p = 0.16) and 18F-FET-rCBV (r = 0.27, p = 0.11). Visual rating yielded widely agreeing findings or only minor differences between MR-rCBV maps and 18F-FET-rCBV maps in 93 % of the tumours (range of three independent raters 91–94%, kappa among raters 0.78–1.0). Conclusion: Early 18F-FET maps (0–2 min p.i.) in gliomas provide similar information to MR-rCBV maps and may be helpful when PWI is not possible or available. Further studies in gliomas are needed to evaluate whether 18F-FET-rCBV provides the same clinical information as MR-rCBV. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Diagnostic Accuracy of MR Spectroscopic Imaging and18F-FET PET for Identifying Glioma: A Biopsy-Controlled Hybrid PET/MRI Study

Author

Mauler, Jörg, primary, Lohmann, Philipp, additional, Maudsley, Andrew A., additional, Sheriff, Sulaiman, additional, Hoevels, Moritz, additional, Meissner, Anna-Katharina, additional, Hamisch, Christina, additional, Brunn, Anna, additional, Deckert, Martina, additional, Filss, Christian P., additional, Stoffels, Gabriele, additional, Dammers, Jürgen, additional, Ruge, Maximillian I., additional, Galldiks, Norbert, additional, Mottaghy, Felix M., additional, Langen, Karl-Josef, additional, and Shah, N. Jon, additional

Published
2023
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38. Assessment of Brain Tumour Perfusion Using Early-Phase 18F-FET PET: Comparison with Perfusion-Weighted MRI

Author

Filss, Christian P., primary, Cramer, Julian, additional, Löher, Saskia, additional, Lohmann, Philipp, additional, Stoffels, Gabriele, additional, Stegmayr, Carina, additional, Kocher, Martin, additional, Heinzel, Alexander, additional, Galldiks, Norbert, additional, Wittsack, Hans J., additional, Sabel, Michael, additional, Neumaier, Bernd, additional, Scheins, Jürgen, additional, Shah, N. Jon, additional, Meyer, Philipp T., additional, Mottaghy, Felix M., additional, and Langen, Karl-Josef, additional

Published
2023
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39. Multicentric 68Ga-PSMA PET radiomics for treatment response assessment of 177Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer

Author

Gutsche, Robin, primary, Gülmüs, Gizem, additional, Mottaghy, Felix M., additional, Gärtner, Florian, additional, Essler, Markus, additional, von Mallek, Dirk, additional, Ahmadzadehfar, Hojjat, additional, Lohmann, Philipp, additional, and Heinzel, Alexander, additional

Published
2023
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40. Evolution and Implementation of Radiographic Response Criteria in Neuro-oncology

Author

Ramakrishnan, Divya, primary, von Reppert, Marc, additional, Krycia, Mark, additional, Sala, Matthew, additional, Mueller, Sabine, additional, Aneja, Sanjay, additional, Nabavizadeh, Ali, additional, Galldiks, Norbert, additional, Lohmann, Philipp, additional, Raji, Cyrus, additional, Ikuta, Ichiro, additional, Memon, Fatima, additional, Weinberg, Brent D, additional, and Aboian, Mariam S, additional

Published
2023
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43. Amino Acid PET in Neurooncology

Author

Galldiks, Norbert, Lohmann, Philipp, Fink, Gereon R., and Langen, Karl-Josef

Subjects
Radiology, Nuclear Medicine and imaging, ddc:610
Published
2023

46. Comparison of O-(2-18F-Fluoroethyl)-L-Tyrosine Positron Emission Tomography and Perfusion-Weighted Magnetic Resonance Imaging in the Diagnosis of Patients with Progressive and Recurrent Glioma: A Hybrid Positron Emission Tomography/Magnetic Resonance Study

Author

Verger, Antoine, Filss, Christian P., Lohmann, Philipp, Stoffels, Gabriele, Sabel, Michael, Wittsack, Hans-J., Kops, Elena Rota, Galldiks, Norbert, Fink, Gereon R., Shah, Nadim J., and Langen, Karl-Josef

Published
2018
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