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149 results on '"Brugnara, Gianluca"'

1. The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

Author

de Verdier, Maria Correia, Saluja, Rachit, Gagnon, Louis, LaBella, Dominic, Baid, Ujjwall, Tahon, Nourel Hoda, Foltyn-Dumitru, Martha, Zhang, Jikai, Alafif, Maram, Baig, Saif, Chang, Ken, D'Anna, Gennaro, Deptula, Lisa, Gupta, Diviya, Haider, Muhammad Ammar, Hussain, Ali, Iv, Michael, Kontzialis, Marinos, Manning, Paul, Moodi, Farzan, Nunes, Teresa, Simon, Aaron, Sollmann, Nico, Vu, David, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Chai, Rongrong, Chung, Verena, Faghani, Shahriar, Farahani, Keyvan, Kazerooni, Anahita Fathi, Iglesias, Eugenio, Kofler, Florian, Li, Hongwei, Linguraru, Marius George, Menze, Bjoern, Moawad, Ahmed W., Velichko, Yury, Wiestler, Benedikt, Altes, Talissa, Basavasagar, Patil, Bendszus, Martin, Brugnara, Gianluca, Cho, Jaeyoung, Dhemesh, Yaseen, Fields, Brandon K. K., Garrett, Filip, Gass, Jaime, Hadjiiski, Lubomir, Hattangadi-Gluth, Jona, Hess, Christopher, Houk, Jessica L., Isufi, Edvin, Layfield, Lester J., Mastorakos, George, Mongan, John, Nedelec, Pierre, Nguyen, Uyen, Oliva, Sebastian, Pease, Matthew W., Rastogi, Aditya, Sinclair, Jason, Smith, Robert X., Sugrue, Leo P., Thacker, Jonathan, Vidic, Igor, Villanueva-Meyer, Javier, White, Nathan S., Aboian, Mariam, Conte, Gian Marco, Dale, Anders, Sabuncu, Mert R., Seibert, Tyler M., Weinberg, Brent, Abayazeed, Aly, Huang, Raymond, Turk, Sevcan, Rauschecker, Andreas M., Farid, Nikdokht, Vollmuth, Philipp, Nada, Ayman, Bakas, Spyridon, Calabrese, Evan, and Rudie, Jeffrey D.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care., Comment: 10 pages, 4 figures, 1 table

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

6. Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study

Author

Vollmuth, Philipp, Foltyn, Martha, Huang, Raymond Y, Galldiks, Norbert, Petersen, Jens, Isensee, Fabian, van den Bent, Martin J, Barkhof, Frederik, Park, Ji Eun, Park, Yae Won, Ahn, Sung Soo, Brugnara, Gianluca, Meredig, Hagen, Jain, Rajan, Smits, Marion, Pope, Whitney B, Maier-Hein, Klaus, Weller, Michael, Wen, Patrick Y, Wick, Wolfgang, and Bendszus, Martin

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Rare Diseases, Neurosciences, Brain Cancer, Clinical Research, Brain Disorders, Cancer, Humans, Glioblastoma, Brain Neoplasms, Artificial Intelligence, Reproducibility of Results, Glioma, Artificial intelligence (AI)-based decision support, RANO, tumor response assessment, tumor volumetry, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract

BackgroundTo assess whether artificial intelligence (AI)-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the Response Assessment in Neuro-Oncology (RANO) criteria.MethodsA series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecutive MRI scans was selected. The time to progression (TTP) on MRI was separately evaluated for each patient by 15 investigators over two rounds. In the first round the TTP was evaluated based on the RANO criteria, whereas in the second round the TTP was evaluated by incorporating additional information from AI-enhanced MRI sequences depicting the longitudinal changes in tumor volumes. The agreement of the TTP measurements between investigators was evaluated using concordance correlation coefficients (CCC) with confidence intervals (CI) and P-values obtained using bootstrap resampling.ResultsThe CCC of TTP-measurements between investigators was 0.77 (95% CI = 0.69,0.88) with RANO alone and increased to 0.91 (95% CI = 0.82,0.95) with AI-based decision support (P = .005). This effect was significantly greater (P = .008) for patients with lower-grade gliomas (CCC = 0.70 [95% CI = 0.56,0.85] without vs. 0.90 [95% CI = 0.76,0.95] with AI-based decision support) as compared to glioblastoma (CCC = 0.83 [95% CI = 0.75,0.92] without vs. 0.86 [95% CI = 0.78,0.93] with AI-based decision support). Investigators with less years of experience judged the AI-based decision as more helpful (P = .02).ConclusionsAI-based decision support has the potential to yield more reproducible and standardized assessment of treatment response in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden, particularly in patients with lower-grade gliomas. A fully-functional version of this AI-based processing pipeline is provided as open-source (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).

Published
2023

7. Abstract: Deep Learning-based Detection of Vessel Occlusions on CT-Angiography in Patients with Suspected Acute Ischemic Stroke

Author

Brugnara, Gianluca, primary, Baumgartner, Michael, additional, Scholze, Edwin D., additional, Deike-Hofmann, Katerina, additional, Kades, Klaus, additional, Scherer, Jonas, additional, Denner, Stefan, additional, Meredig, Hagen, additional, Rastogi, Aditya, additional, Mahmutoglu, Mustafa A., additional, Ulfert, Christian, additional, Neuberger, Ulf, additional, Schönenberger, Silvia, additional, Schlamp, Kai, additional, Bendella, Zeynep, additional, Pinetz, Thomas, additional, Schmeel, Carsten, additional, Wick, Wolfgang, additional, Ringleb, Peter A., additional, Floca, Ralf, additional, Möhlenbruch, Markus, additional, Radbruch, Alexander, additional, Bendszus, Martin, additional, Maier-Hein, Klaus, additional, and Vollmuth, Philipp, additional

Published
2024
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8. Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J, Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y, Chang, Ken, Balana, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S, Lombardo, Joseph, Palmer, Joshua D, Flanders, Adam E, Dicker, Adam P, Sair, Haris I, Jones, Craig K, Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y, Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A, Mitchell, J Ross, Farinhas, Joaquim, Maldjian, Joseph A, Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C, Reddy, Divya, Holcomb, James, Wagner, Benjamin C, Ellingson, Benjamin M, Cloughesy, Timothy F, Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B, Teixeira, Bernardo C A, Sprenger, Flávia, Menotti, David, Lucio, Diego R, LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W, McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E, Vadmal, Vachan, Waite, Kristin, Colen, Rivka R, Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V M, Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I, Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M, Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R, Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten MJ, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Dubbink, Hendrikus J, Vincent, Arnaud JPE, Bent, Martin J van den, French, Pim J, Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P, Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B, Mistry, Akshitkumar, Thompson, Reid C, Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C, Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G H, Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M, Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F, Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M, Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A, Ogbole, Godwin, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu'aibu, Mustapha, Dorcas, Adeleye, Soneye, Mayowa, Dako, Farouk, Simpson, Amber L, Hamghalam, Mohammad, Peoples, Jacob J, Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y, Boss, Michael A, Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S, Martin, Jason, and Bakas, Spyridon

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

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing., Comment: federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS

Published
2022
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9. Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke

Author

Brugnara, Gianluca, Baumgartner, Michael, Scholze, Edwin David, Deike-Hofmann, Katerina, Kades, Klaus, Scherer, Jonas, Denner, Stefan, Meredig, Hagen, Rastogi, Aditya, Mahmutoglu, Mustafa Ahmed, Ulfert, Christian, Neuberger, Ulf, Schönenberger, Silvia, Schlamp, Kai, Bendella, Zeynep, Pinetz, Thomas, Schmeel, Carsten, Wick, Wolfgang, Ringleb, Peter A., Floca, Ralf, Möhlenbruch, Markus, Radbruch, Alexander, Bendszus, Martin, Maier-Hein, Klaus, and Vollmuth, Philipp

Published
2023
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10. T cell-independent eradication of experimental glioma by intravenous TLR7/8-agonist-loaded nanoparticles

Author

Turco, Verena, Pfleiderer, Kira, Hunger, Jessica, Horvat, Natalie K., Karimian-Jazi, Kianush, Schregel, Katharina, Fischer, Manuel, Brugnara, Gianluca, Jähne, Kristine, Sturm, Volker, Streibel, Yannik, Nguyen, Duy, Altamura, Sandro, Agardy, Dennis A., Soni, Shreya S., Alsasa, Abdulrahman, Bunse, Theresa, Schlesner, Matthias, Muckenthaler, Martina U., Weissleder, Ralph, Wick, Wolfgang, Heiland, Sabine, Vollmuth, Philipp, Bendszus, Martin, Rodell, Christopher B., Breckwoldt, Michael O., and Platten, Michael

Published
2023
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11. Author Correction: Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G. Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J., Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y., Chang, Ken, Balaña, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S., Lombardo, Joseph, Palmer, Joshua D., Flanders, Adam E., Dicker, Adam P., Sair, Haris I., Jones, Craig K., Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y., Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A., Mitchell, J. Ross, Farinhas, Joaquim, Maldjian, Joseph A., Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C., Reddy, Divya, Holcomb, James, Wagner, Benjamin C., Ellingson, Benjamin M., Cloughesy, Timothy F., Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B., Teixeira, Bernardo C. A., Sprenger, Flávia, Menotti, David, Lucio, Diego R., LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W., McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E., Vadmal, Vachan, Waite, Kristin, Colen, Rivka R., Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J. Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V. M., Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I., Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M., Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R., Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten M. J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J. P. E., van den Bent, Martin J., French, Pim J., Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P., Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B., Mistry, Akshitkumar, Thompson, Reid C., Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C., Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G. H., Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M., Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F., Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M., Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A., Ogbole, Godwin, Soneye, Mayowa, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu’aibu, Mustapha, Dorcas, Adeleye, Dako, Farouk, Simpson, Amber L., Hamghalam, Mohammad, Peoples, Jacob J., Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y., Boss, Michael A., Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S., Martin, Jason, and Bakas, Spyridon

Published
2023
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12. Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study

Author

Rastogi, Aditya, Brugnara, Gianluca, Foltyn-Dumitru, Martha, Mahmutoglu, Mustafa Ahmed, Preetha, Chandrakanth J, Kobler, Erich, Pflüger, Irada, Schell, Marianne, Deike-Hofmann, Katerina, Kessler, Tobias, van den Bent, Martin J, Idbaih, Ahmed, Platten, Michael, Brandes, Alba A, Nabors, Burt, Stupp, Roger, Bernhardt, Denise, Debus, Jürgen, Abdollahi, Amir, Gorlia, Thierry, Tonn, Jörg-Christian, Weller, Michael, Maier-Hein, Klaus H, Radbruch, Alexander, Wick, Wolfgang, Bendszus, Martin, Meredig, Hagen, Kurz, Felix T, and Vollmuth, Philipp

Published
2024
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13. Abstract: Deep Learning-based Detection of Vessel Occlusions on CT-Angiography in Patients with Suspected Acute Ischemic Stroke

Author

Brugnara, Gianluca, Baumgartner, Michael, Scholze, Edwin D., Deike-Hofmann, Katerina, Kades, Klaus, Scherer, Jonas, Denner, Stefan, Meredig, Hagen, Rastogi, Aditya, Mahmutoglu, Mustafa A., Ulfert, Christian, Neuberger, Ulf, Schönenberger, Silvia, Schlamp, Kai, Bendella, Zeynep, Pinetz, Thomas, Schmeel, Carsten, Wick, Wolfgang, Ringleb, Peter A., Floca, Ralf, Möhlenbruch, Markus, Radbruch, Alexander, Bendszus, Martin, Maier-Hein, Klaus, Vollmuth, Philipp, Gesellschaft für Informatik e.V. (GI), Maier, Andreas, editor, Deserno, Thomas M., editor, Handels, Heinz, editor, Maier-Hein, Klaus, editor, Palm, Christoph, editor, and Tolxdorff, Thomas, editor

Published
2024
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14. Automated brain extraction of multi-sequence MRI using artificial neural networks

Author

Isensee, Fabian, Schell, Marianne, Tursunova, Irada, Brugnara, Gianluca, Bonekamp, David, Neuberger, Ulf, Wick, Antje, Schlemmer, Heinz-Peter, Heiland, Sabine, Wick, Wolfgang, Bendszus, Martin, Maier-Hein, Klaus Hermann, and Kickingereder, Philipp

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aims to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.11 points for the DICE coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility our HD-BET prediction algorithm is made freely available (http://www.neuroAI-HD.org) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data., Comment: Fabian Isensee, Marianne Schell and Irada Tursunova share the first authorship

Published
2019
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15. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study

Author

Jayachandran Preetha, Chandrakanth, Meredig, Hagen, Brugnara, Gianluca, Mahmutoglu, Mustafa A, Foltyn, Martha, Isensee, Fabian, Kessler, Tobias, Pflüger, Irada, Schell, Marianne, Neuberger, Ulf, Petersen, Jens, Wick, Antje, Heiland, Sabine, Debus, Jürgen, Platten, Michael, Idbaih, Ahmed, Brandes, Alba A, Winkler, Frank, van den Bent, Martin J, Nabors, Burt, Stupp, Roger, Maier-Hein, Klaus H, Gorlia, Thierry, Tonn, Jörg-Christian, Weller, Michael, Wick, Wolfgang, Bendszus, Martin, and Vollmuth, Philipp

Published
2021
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16. Abstract: Automated Detection and Quantification of Brain Metastases on Clinical MRI Data using CNNs

Author

Pflüger, Irada, Wald, Tassilo, Isensee, Fabian, Schell, Marianne, Meredig, Hagen, Schlamp, Kai, Bernhardt, Denise, Brugnara, Gianluca, Heußel, Claus P., Debus, Juergen, Wick, Wolfgang, Bendszus, Martin, Maier-Hein, Klaus, Vollmuth, Philipp, Gesellschaft für Informatik e.V. (GI), Deserno, Thomas M., editor, Handels, Heinz, editor, Maier, Andreas, editor, Maier-Hein, Klaus, editor, Palm, Christoph, editor, and Tolxdorff, Thomas, editor

Published
2023
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17. Deep-learning-based reconstruction of undersampled MRI to reduce scan times:a multicentre, retrospective, cohort study

Author

Rastogi, Aditya, Brugnara, Gianluca, Foltyn-Dumitru, Martha, Mahmutoglu, Mustafa Ahmed, Preetha, Chandrakanth J., Kobler, Erich, Pflüger, Irada, Schell, Marianne, Deike-Hofmann, Katerina, Kessler, Tobias, van den Bent, Martin J., Idbaih, Ahmed, Platten, Michael, Brandes, Alba A., Nabors, Burt, Stupp, Roger, Bernhardt, Denise, Debus, Jürgen, Abdollahi, Amir, Gorlia, Thierry, Tonn, Jörg Christian, Weller, Michael, Maier-Hein, Klaus H., Radbruch, Alexander, Wick, Wolfgang, Bendszus, Martin, Meredig, Hagen, Kurz, Felix T., Vollmuth, Philipp, Rastogi, Aditya, Brugnara, Gianluca, Foltyn-Dumitru, Martha, Mahmutoglu, Mustafa Ahmed, Preetha, Chandrakanth J., Kobler, Erich, Pflüger, Irada, Schell, Marianne, Deike-Hofmann, Katerina, Kessler, Tobias, van den Bent, Martin J., Idbaih, Ahmed, Platten, Michael, Brandes, Alba A., Nabors, Burt, Stupp, Roger, Bernhardt, Denise, Debus, Jürgen, Abdollahi, Amir, Gorlia, Thierry, Tonn, Jörg Christian, Weller, Michael, Maier-Hein, Klaus H., Radbruch, Alexander, Wick, Wolfgang, Bendszus, Martin, Meredig, Hagen, Kurz, Felix T., and Vollmuth, Philipp

Abstract

BACKGROUND: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 acros

Published
2024

21. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study

Author

Kickingereder, Philipp, Isensee, Fabian, Tursunova, Irada, Petersen, Jens, Neuberger, Ulf, Bonekamp, David, Brugnara, Gianluca, Schell, Marianne, Kessler, Tobias, Foltyn, Martha, Harting, Inga, Sahm, Felix, Prager, Marcel, Nowosielski, Martha, Wick, Antje, Nolden, Marco, Radbruch, Alexander, Debus, Jürgen, Schlemmer, Heinz-Peter, Heiland, Sabine, Platten, Michael, von Deimling, Andreas, van den Bent, Martin J, Gorlia, Thierry, Wick, Wolfgang, Bendszus, Martin, and Maier-Hein, Klaus H

Published
2019
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25. The proneural gene ASCL1 governs the transcriptional subgroup affiliation in glioblastoma stem cells by directly repressing the mesenchymal gene NDRG1

Author

Narayanan, Ashwin, Gagliardi, Filippo, Gallotti, Alberto L., Mazzoleni, Stefania, Cominelli, Manuela, Fagnocchi, Luca, Pala, Mauro, Piras, Ignazio S., Zordan, Paola, Moretta, Nicole, Tratta, Elisa, Brugnara, Gianluca, Altabella, Luisa, Bozzuto, Giuseppina, Gorombei, Petra, Molinari, Agnese, Padua, Rose-Ann, Bulfone, Alessandro, Politi, Letterio S., Falini, Andrea, Castellano, Antonella, Mortini, Pietro, Zippo, Alessio, Poliani, Pietro L., and Galli, Rossella

Published
2019
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28. Heterogeneity of response to immune checkpoint blockade in hypermutated experimental gliomas

Author

Aslan, Katrin, Turco, Verena, Blobner, Jens, Sonner, Jana K., Liuzzi, Anna Rita, Núñez, Nicolás Gonzalo, De Feo, Donatella, Kickingereder, Philipp, Fischer, Manuel, Green, Ed, Sadik, Ahmed, Friedrich, Mirco, Sanghvi, Khwab, Kilian, Michael, Cichon, Frederik, Wolf, Lara, Jähne, Kristine, von Landenberg, Anna, Bunse, Lukas, Sahm, Felix, Schrimpf, Daniel, Meyer, Jochen, Alexander, Allen, Brugnara, Gianluca, Röth, Ralph, Pfleiderer, Kira, Niesler, Beate, von Deimling, Andreas, Opitz, Christiane, Breckwoldt, Michael O., Heiland, Sabine, Bendszus, Martin, Wick, Wolfgang, Becher, Burkhard, and Platten, Michael

Published
2020
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29. Clinical value of automated volumetric quantification of early ischemic tissue changes on non-contrast CT.

Author

Brugnara, Gianluca, Mihalicz, Peter, Herweh, Christian, Schönenberger, Silvia, Purrucker, Jan, Nagel, Simon, Ringleb, Peter Arthur, Bendszus, Martin, Möhlenbruch, Markus A., and Neuberger, Ulf

Subjects
STATISTICS, CONFIDENCE intervals, ISCHEMIC stroke, MULTIPLE regression analysis, RETROSPECTIVE studies, DESCRIPTIVE statistics, COMPUTED tomography, PREDICTIVE validity, DATA analysis, RECEIVER operating characteristic curves, ODDS ratio
Abstract

Background Quantitative and automated volumetric evaluation of early ischemic changes on non-contrast CT (NCCT) has recently been proposed as a new tool to improve prognostic performance in patients undergoing endovascular therapy (EVT) for acute ischemic stroke (AIS). We aimed to test its clinical value compared with the Alberta Stroke Program Early CT Score (ASPECTS) in a large single-institutional patient cohort. Methods A total of 1103 patients with AIS due to large vessel occlusion in the M1 or proximal M2 segments who underwent NCCT and EVT between January 2013 and November 2019 were retrospectively enrolled. Acute ischemic volumes (AIV) and ASPECTS were generated from the baseline NCCT through e-ASPECTS (Brainomix). Correlations were tested using Spearman's coefficient. The predictive capabilities of AIV for a favorable outcome (modified Rankin Scale score at 90 days ≤2) were tested using multivariable logistic regression as well as machine-learning models. Performance of the models was assessed using receiver operating characteristic (ROC) curves and differences were tested using DeLong's test. results Patients with a favorable outcome had a significantly lower AIV (median 12.0 mL (IQR 5.7-21.7) vs 18.8 mL (IQR 9.4-33.9), p<0.001). AIV was highly correlated with ASPECTS (rho=0.78, p<0.001) and weakly correlated with the National Institutes of Health Stroke Scale score at baseline (rho=0.22, p<0.001), and was an independent predictor of an unfavorable clinical outcome (adjusted OR 0.97, 95% CI 0.96 to 0.98). No significant difference was found between machinelearning models using either AIV or ASPECTS or both metrics for predicting a good clinical outcome (p>0.05). Conclusion AIV is an independent predictor of clinical outcome and presented a non-inferior performance compared with ASPECTS, without clear advantages for prognostic modelling. [ABSTRACT FROM AUTHOR]

Published
2023
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31. Author Correction: Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak; https://orcid.org/0000-0003-2243-8487, Baid, Ujjwal; https://orcid.org/0000-0001-5246-2088, Edwards, Brandon; https://orcid.org/0000-0002-0957-9149, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick; https://orcid.org/0000-0001-9401-3088, Gruzdev, Alexey, Karkada, Deepthi; https://orcid.org/0000-0002-0623-548X, Davatzikos, Christos; https://orcid.org/0000-0002-1025-8561, Sako, Chiharu; https://orcid.org/0000-0003-3243-3954, Ghodasara, Satyam; https://orcid.org/0000-0002-5332-3132, Bilello, Michel, Mohan, Suyash; https://orcid.org/0000-0002-4025-115X, Vollmuth, Philipp; https://orcid.org/0000-0002-6224-0064, Brugnara, Gianluca; https://orcid.org/0000-0003-2461-1407, Preetha, Chandrakanth J; https://orcid.org/0000-0002-9875-1982, Sahm, Felix; https://orcid.org/0000-0001-5441-1962, Maier-Hein, Klaus; https://orcid.org/0000-0002-6626-2463, Zenk, Maximilian; https://orcid.org/0000-0002-8933-5995, Bendszus, Martin, Wick, Wolfgang; https://orcid.org/0000-0002-6171-634X, Calabrese, Evan; https://orcid.org/0000-0002-1464-0354, Rudie, Jeffrey; https://orcid.org/0000-0001-8609-8421, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali; https://orcid.org/0000-0002-0561-7144, Pandey, Umang; https://orcid.org/0000-0003-3289-0901, Saini, Jitender, et al, Pati, Sarthak; https://orcid.org/0000-0003-2243-8487, Baid, Ujjwal; https://orcid.org/0000-0001-5246-2088, Edwards, Brandon; https://orcid.org/0000-0002-0957-9149, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick; https://orcid.org/0000-0001-9401-3088, Gruzdev, Alexey, Karkada, Deepthi; https://orcid.org/0000-0002-0623-548X, Davatzikos, Christos; https://orcid.org/0000-0002-1025-8561, Sako, Chiharu; https://orcid.org/0000-0003-3243-3954, Ghodasara, Satyam; https://orcid.org/0000-0002-5332-3132, Bilello, Michel, Mohan, Suyash; https://orcid.org/0000-0002-4025-115X, Vollmuth, Philipp; https://orcid.org/0000-0002-6224-0064, Brugnara, Gianluca; https://orcid.org/0000-0003-2461-1407, Preetha, Chandrakanth J; https://orcid.org/0000-0002-9875-1982, Sahm, Felix; https://orcid.org/0000-0001-5441-1962, Maier-Hein, Klaus; https://orcid.org/0000-0002-6626-2463, Zenk, Maximilian; https://orcid.org/0000-0002-8933-5995, Bendszus, Martin, Wick, Wolfgang; https://orcid.org/0000-0002-6171-634X, Calabrese, Evan; https://orcid.org/0000-0002-1464-0354, Rudie, Jeffrey; https://orcid.org/0000-0001-8609-8421, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali; https://orcid.org/0000-0002-0561-7144, Pandey, Umang; https://orcid.org/0000-0003-3289-0901, Saini, Jitender, and et al

Published
2023

32. Assessing the added value of apparent diffusion coefficient, cerebral blood volume, and radiomic magnetic resonance features for differentiation of pseudoprogression versus true tumor progression in patients with glioblastoma

Author

Leone, Riccardo, primary, Meredig, Hagen, additional, Foltyn-Dumitru, Martha, additional, Sahm, Felix, additional, Hamelmann, Stefan, additional, Kurz, Felix, additional, Kessler, Tobias, additional, Bonekamp, David, additional, Schlemmer, Heinz-Peter, additional, Bo Hansen, Mikkel, additional, Wick, Wolfgang, additional, Bendszus, Martin, additional, Vollmuth, Philipp, additional, and Brugnara, Gianluca, additional

Published
2023
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33. Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak, primary, Baid, Ujjwal, additional, Edwards, Brandon, additional, Sheller, Micah, additional, Wang, Shih-Han, additional, Reina, G. Anthony, additional, Foley, Patrick, additional, Gruzdev, Alexey, additional, Karkada, Deepthi, additional, Davatzikos, Christos, additional, Sako, Chiharu, additional, Ghodasara, Satyam, additional, Bilello, Michel, additional, Mohan, Suyash, additional, Vollmuth, Philipp, additional, Brugnara, Gianluca, additional, Preetha, Chandrakanth J., additional, Sahm, Felix, additional, Maier-Hein, Klaus, additional, Zenk, Maximilian, additional, Bendszus, Martin, additional, Wick, Wolfgang, additional, Calabrese, Evan, additional, Rudie, Jeffrey, additional, Villanueva-Meyer, Javier, additional, Cha, Soonmee, additional, Ingalhalikar, Madhura, additional, Jadhav, Manali, additional, Pandey, Umang, additional, Saini, Jitender, additional, Garrett, John, additional, Larson, Matthew, additional, Jeraj, Robert, additional, Currie, Stuart, additional, Frood, Russell, additional, Fatania, Kavi, additional, Huang, Raymond Y., additional, Chang, Ken, additional, Balaña, Carmen, additional, Capellades, Jaume, additional, Puig, Josep, additional, Trenkler, Johannes, additional, Pichler, Josef, additional, Necker, Georg, additional, Haunschmidt, Andreas, additional, Meckel, Stephan, additional, Shukla, Gaurav, additional, Liem, Spencer, additional, Alexander, Gregory S., additional, Lombardo, Joseph, additional, Palmer, Joshua D., additional, Flanders, Adam E., additional, Dicker, Adam P., additional, Sair, Haris I., additional, Jones, Craig K., additional, Venkataraman, Archana, additional, Jiang, Meirui, additional, So, Tiffany Y., additional, Chen, Cheng, additional, Heng, Pheng Ann, additional, Dou, Qi, additional, Kozubek, Michal, additional, Lux, Filip, additional, Michálek, Jan, additional, Matula, Petr, additional, Keřkovský, Miloš, additional, Kopřivová, Tereza, additional, Dostál, Marek, additional, Vybíhal, Václav, additional, Vogelbaum, Michael A., additional, Mitchell, J. Ross, additional, Farinhas, Joaquim, additional, Maldjian, Joseph A., additional, Yogananda, Chandan Ganesh Bangalore, additional, Pinho, Marco C., additional, Reddy, Divya, additional, Holcomb, James, additional, Wagner, Benjamin C., additional, Ellingson, Benjamin M., additional, Cloughesy, Timothy F., additional, Raymond, Catalina, additional, Oughourlian, Talia, additional, Hagiwara, Akifumi, additional, Wang, Chencai, additional, To, Minh-Son, additional, Bhardwaj, Sargam, additional, Chong, Chee, additional, Agzarian, Marc, additional, Falcão, Alexandre Xavier, additional, Martins, Samuel B., additional, Teixeira, Bernardo C. A., additional, Sprenger, Flávia, additional, Menotti, David, additional, Lucio, Diego R., additional, LaMontagne, Pamela, additional, Marcus, Daniel, additional, Wiestler, Benedikt, additional, Kofler, Florian, additional, Ezhov, Ivan, additional, Metz, Marie, additional, Jain, Rajan, additional, Lee, Matthew, additional, Lui, Yvonne W., additional, McKinley, Richard, additional, Slotboom, Johannes, additional, Radojewski, Piotr, additional, Meier, Raphael, additional, Wiest, Roland, additional, Murcia, Derrick, additional, Fu, Eric, additional, Haas, Rourke, additional, Thompson, John, additional, Ormond, David Ryan, additional, Badve, Chaitra, additional, Sloan, Andrew E., additional, Vadmal, Vachan, additional, Waite, Kristin, additional, Colen, Rivka R., additional, Pei, Linmin, additional, Ak, Murat, additional, Srinivasan, Ashok, additional, Bapuraj, J. Rajiv, additional, Rao, Arvind, additional, Wang, Nicholas, additional, Yoshiaki, Ota, additional, Moritani, Toshio, additional, Turk, Sevcan, additional, Lee, Joonsang, additional, Prabhudesai, Snehal, additional, Morón, Fanny, additional, Mandel, Jacob, additional, Kamnitsas, Konstantinos, additional, Glocker, Ben, additional, Dixon, Luke V. M., additional, Williams, Matthew, additional, Zampakis, Peter, additional, Panagiotopoulos, Vasileios, additional, Tsiganos, Panagiotis, additional, Alexiou, Sotiris, additional, Haliassos, Ilias, additional, Zacharaki, Evangelia I., additional, Moustakas, Konstantinos, additional, Kalogeropoulou, Christina, additional, Kardamakis, Dimitrios M., additional, Choi, Yoon Seong, additional, Lee, Seung-Koo, additional, Chang, Jong Hee, additional, Ahn, Sung Soo, additional, Luo, Bing, additional, Poisson, Laila, additional, Wen, Ning, additional, Tiwari, Pallavi, additional, Verma, Ruchika, additional, Bareja, Rohan, additional, Yadav, Ipsa, additional, Chen, Jonathan, additional, Kumar, Neeraj, additional, Smits, Marion, additional, van der Voort, Sebastian R., additional, Alafandi, Ahmed, additional, Incekara, Fatih, additional, Wijnenga, Maarten M. J., additional, Kapsas, Georgios, additional, Gahrmann, Renske, additional, Schouten, Joost W., additional, Dubbink, Hendrikus J., additional, Vincent, Arnaud J. P. E., additional, van den Bent, Martin J., additional, French, Pim J., additional, Klein, Stefan, additional, Yuan, Yading, additional, Sharma, Sonam, additional, Tseng, Tzu-Chi, additional, Adabi, Saba, additional, Niclou, Simone P., additional, Keunen, Olivier, additional, Hau, Ann-Christin, additional, Vallières, Martin, additional, Fortin, David, additional, Lepage, Martin, additional, Landman, Bennett, additional, Ramadass, Karthik, additional, Xu, Kaiwen, additional, Chotai, Silky, additional, Chambless, Lola B., additional, Mistry, Akshitkumar, additional, Thompson, Reid C., additional, Gusev, Yuriy, additional, Bhuvaneshwar, Krithika, additional, Sayah, Anousheh, additional, Bencheqroun, Camelia, additional, Belouali, Anas, additional, Madhavan, Subha, additional, Booth, Thomas C., additional, Chelliah, Alysha, additional, Modat, Marc, additional, Shuaib, Haris, additional, Dragos, Carmen, additional, Abayazeed, Aly, additional, Kolodziej, Kenneth, additional, Hill, Michael, additional, Abbassy, Ahmed, additional, Gamal, Shady, additional, Mekhaimar, Mahmoud, additional, Qayati, Mohamed, additional, Reyes, Mauricio, additional, Park, Ji Eun, additional, Yun, Jihye, additional, Kim, Ho Sung, additional, Mahajan, Abhishek, additional, Muzi, Mark, additional, Benson, Sean, additional, Beets-Tan, Regina G. H., additional, Teuwen, Jonas, additional, Herrera-Trujillo, Alejandro, additional, Trujillo, Maria, additional, Escobar, William, additional, Abello, Ana, additional, Bernal, Jose, additional, Gómez, Jhon, additional, Choi, Joseph, additional, Baek, Stephen, additional, Kim, Yusung, additional, Ismael, Heba, additional, Allen, Bryan, additional, Buatti, John M., additional, Kotrotsou, Aikaterini, additional, Li, Hongwei, additional, Weiss, Tobias, additional, Weller, Michael, additional, Bink, Andrea, additional, Pouymayou, Bertrand, additional, Shaykh, Hassan F., additional, Saltz, Joel, additional, Prasanna, Prateek, additional, Shrestha, Sampurna, additional, Mani, Kartik M., additional, Payne, David, additional, Kurc, Tahsin, additional, Pelaez, Enrique, additional, Franco-Maldonado, Heydy, additional, Loayza, Francis, additional, Quevedo, Sebastian, additional, Guevara, Pamela, additional, Torche, Esteban, additional, Mendoza, Cristobal, additional, Vera, Franco, additional, Ríos, Elvis, additional, López, Eduardo, additional, Velastin, Sergio A., additional, Ogbole, Godwin, additional, Soneye, Mayowa, additional, Oyekunle, Dotun, additional, Odafe-Oyibotha, Olubunmi, additional, Osobu, Babatunde, additional, Shu’aibu, Mustapha, additional, Dorcas, Adeleye, additional, Dako, Farouk, additional, Simpson, Amber L., additional, Hamghalam, Mohammad, additional, Peoples, Jacob J., additional, Hu, Ricky, additional, Tran, Anh, additional, Cutler, Danielle, additional, Moraes, Fabio Y., additional, Boss, Michael A., additional, Gimpel, James, additional, Veettil, Deepak Kattil, additional, Schmidt, Kendall, additional, Bialecki, Brian, additional, Marella, Sailaja, additional, Price, Cynthia, additional, Cimino, Lisa, additional, Apgar, Charles, additional, Shah, Prashant, additional, Menze, Bjoern, additional, Barnholtz-Sloan, Jill S., additional, Martin, Jason, additional, and Bakas, Spyridon, additional

Published
2022
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35. Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study

Author

Vollmuth, Philipp, primary, Foltyn, Martha, additional, Huang, Raymond Y, additional, Galldiks, Norbert, additional, Petersen, Jens, additional, Isensee, Fabian, additional, van den Bent, Martin J, additional, Barkhof, Frederik, additional, Park, Ji Eun, additional, Park, Yae Won, additional, Ahn, Sung Soo, additional, Brugnara, Gianluca, additional, Meredig, Hagen, additional, Jain, Rajan, additional, Smits, Marion, additional, Pope, Whitney B, additional, Maier-Hein, Klaus, additional, Weller, Michael, additional, Wen, Patrick Y, additional, Wick, Wolfgang, additional, and Bendszus, Martin, additional

Published
2022
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36. Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks

Author

Pflüger, Irada, primary, Wald, Tassilo, additional, Isensee, Fabian, additional, Schell, Marianne, additional, Meredig, Hagen, additional, Schlamp, Kai, additional, Bernhardt, Denise, additional, Brugnara, Gianluca, additional, Heußel, Claus Peter, additional, Debus, Juergen, additional, Wick, Wolfgang, additional, Bendszus, Martin, additional, Maier-Hein, Klaus H, additional, and Vollmuth, Philipp, additional

Published
2022
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37. NIMG-48. TLR7/8-AGONIST-LOADED NANOPARTICLES REPROGRAM TUMOR-ASSOCIATED MYELOID CELLS FOR EFFECTIVE IMMUNOTHERAPY OF EXPERIMENTAL GLIOMA AND MRI-BASED TREATMENT MONITORING

Author

Pfleiderer, Kira, primary, Turco, Verena, additional, Horvat, Natalie K, additional, Hunger, Jessica, additional, Karimian-Jazi, Kianush, additional, Schregel, Katharina, additional, Brugnara, Gianluca, additional, Nguyen, Duy, additional, Jähne, Kristine, additional, Fischer, Manuel, additional, Alsasa, Abdulrahman, additional, Bunse, Theresa, additional, Schlesner, Matthias, additional, Muckenthaler, Martina, additional, Weissleder, Ralph, additional, Wick, Wolfgang, additional, Heiland, Sabine, additional, Vollmuth, Philipp, additional, Bendszus, Martin, additional, Rodell, Christopher B, additional, Breckwoldt, Michael O, additional, and Platten, Michael, additional

Published
2021
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39. Diagnostic value of gadolinium contrast administration for spinal cord magnetic resonance imaging in multiple sclerosis patients and correlative markers of lesion enhancement

Author

Karimian-Jazi, Kianush, primary, Neuberger, Ulf, additional, Schregel, Katharina, additional, Brugnara, Gianluca, additional, Bendszus, Martin, additional, Breckwoldt, Michael O., additional, Schwarz, Daniel, additional, Jäger, Laura Bettina, additional, and Wick, Wolfgang, additional

Published
2021
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40. sj-doc-1-mso-10.1177_20552173211047978 - Supplemental material for Diagnostic value of gadolinium contrast administration for spinal cord magnetic resonance imaging in multiple sclerosis patients and correlative markers of lesion enhancement

Author

Karimian-Jazi, Kianush, Neuberger, Ulf, Schregel, Katharina, Brugnara, Gianluca, Bendszus, Martin, Breckwoldt, Michael O., Schwarz, Daniel, J��ger, Laura Bettina, and Wick, Wolfgang

Subjects
FOS: Clinical medicine, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, sj-doc-1-mso-10.1177_20552173211047978 for Diagnostic value of gadolinium contrast administration for spinal cord magnetic resonance imaging in multiple sclerosis patients and correlative markers of lesion enhancement by Kianush Karimian-Jazi, Ulf Neuberger, Katharina Schregel, Gianluca Brugnara, Martin Bendszus, Michael O. Breckwoldt, Daniel Schwarz, Laura Bettina J��ger and Wolfgang Wick in Multiple Sclerosis Journal ��� Experimental, Translational and Clinical

Published
2021
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41. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology:a multicentre, retrospective cohort study

Author

Jayachandran Preetha, Chandrakanth, Meredig, Hagen, Brugnara, Gianluca, Mahmutoglu, Mustafa A., Foltyn, Martha, Isensee, Fabian, Kessler, Tobias, Pflüger, Irada, Schell, Marianne, Neuberger, Ulf, Petersen, Jens, Wick, Antje, Heiland, Sabine, Debus, Jürgen, Platten, Michael, Idbaih, Ahmed, Brandes, Alba A., Winkler, Frank, van den Bent, Martin J., Nabors, Burt, Stupp, Roger, Maier-Hein, Klaus H., Gorlia, Thierry, Tonn, Jörg Christian, Weller, Michael, Wick, Wolfgang, Bendszus, Martin, Vollmuth, Philipp, Jayachandran Preetha, Chandrakanth, Meredig, Hagen, Brugnara, Gianluca, Mahmutoglu, Mustafa A., Foltyn, Martha, Isensee, Fabian, Kessler, Tobias, Pflüger, Irada, Schell, Marianne, Neuberger, Ulf, Petersen, Jens, Wick, Antje, Heiland, Sabine, Debus, Jürgen, Platten, Michael, Idbaih, Ahmed, Brandes, Alba A., Winkler, Frank, van den Bent, Martin J., Nabors, Burt, Stupp, Roger, Maier-Hein, Klaus H., Gorlia, Thierry, Tonn, Jörg Christian, Weller, Michael, Wick, Wolfgang, Bendszus, Martin, and Vollmuth, Philipp

Abstract

Background: Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology. Methods: In this multicentre, retrospective cohort study, we used MRI examinations to train and validate a dCNN for synthesising post-contrast T1-weighted sequences from pre-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery sequences. We used MRI scans with availability of these sequences from 775 patients with glioblastoma treated at Heidelberg University Hospital, Heidelberg, Germany (775 MRI examinations); 260 patients who participated in the phase 2 CORE trial (1083 MRI examinations, 59 institutions); and 505 patients who participated in the phase 3 CENTRIC trial (3147 MRI examinations, 149 institutions). Separate training runs to rank the importance of individual sequences and (for a subset) diffusion-weighted imaging were conducted. Independent testing was performed on MRI data from the phase 2 and phase 3 EORTC-26101 trial (521 patients, 1924 MRI examinations, 32 institutions). The similarity between synthetic and true contrast enhancement on post-contrast T1-weighted MRI was quantified using the structural similarity index measure (SSIM). Automated tumour segmentation and volumetric tumour response assessment based on synthetic versus true post-contrast T1-weighted sequences was performed in the EORTC-26101 trial and agreement was assessed with Kaplan-Meier plots. Findings: The median SSIM score for predicting contrast enhancement on synthetic post-contrast T1-weighted sequences in

Published
2021

42. Dynamics of cerebral perfusion and oxygenation parameters following endovascular treatment of acute ischemic stroke

Author

Brugnara, Gianluca, primary, Herweh, Christian, additional, Neuberger, Ulf, additional, Bo Hansen, Mikkel, additional, Ulfert, Christian, additional, Mahmutoglu, Mustafa Ahmed, additional, Foltyn, Martha, additional, Nagel, Simon, additional, Schönenberger, Silvia, additional, Heiland, Sabine, additional, Ringleb, Peter Arthur, additional, Bendszus, Martin, additional, Möhlenbruch, Markus, additional, Pfaff, Johannes Alex Rolf, additional, and Vollmuth, Philipp, additional

Published
2021
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43. Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial.

Author

Schell, Marianne, Pflüger, Irada, Brugnara, Gianluca, Isensee, Fabian, Neuberger, Ulf, Foltyn, Martha, Kessler, Tobias, Sahm, Felix, Wick, Antje, Nowosielski, Martha, Heiland, Sabine, Weller, Michael, Platten, Michael, Maier-Hein, Klaus H, von Deimling, Andreas, van den Bent, Martin J, Gorlia, Thierry, Wick, Wolfgang, Bendszus, Martin, Kickingereder, Philipp, Schell, Marianne, Pflüger, Irada, Brugnara, Gianluca, Isensee, Fabian, Neuberger, Ulf, Foltyn, Martha, Kessler, Tobias, Sahm, Felix, Wick, Antje, Nowosielski, Martha, Heiland, Sabine, Weller, Michael, Platten, Michael, Maier-Hein, Klaus H, von Deimling, Andreas, van den Bent, Martin J, Gorlia, Thierry, Wick, Wolfgang, Bendszus, Martin, and Kickingereder, Philipp

Abstract

BACKGROUND: This study validated a previously described diffusion-MRI phenotype as a potential predictive imaging biomarker in patients with recurrent glioblastoma receiving bevacizumab (BEV). METHODS: A total of 396/596 patients (66%) from the prospective randomized phase II/III EORTC-26101 trial (with n=242 in the BEV and n=154 in the non-BEV arm) met the inclusion criteria with availability of anatomical and diffusion MRI-sequences at baseline prior treatment. Apparent diffusion coefficient (ADC) histograms from the contrast-enhancing tumor volume were fitted to a double Gaussian distribution and the mean of the lower curve (ADClow) was used for further analysis. The predictive ability of ADClow was assessed with biomarker threshold models and multivariable Cox-regression for overall and progression-free survival (OS, PFS). RESULTS: ADClow was associated with PFS (HR=0.625,p=0.007) and OS (HR=0.656,p=0.031). However, no (predictive) interaction between ADClow and the treatment arm was present (p=0.865 for PFS, p=0.722 for OS). Independent (prognostic) significance of ADClow was retained after adjusting for epidemiological, clinical and molecular characteristics (p≤0.02 for OS, p≤0.01 PFS). The biomarker threshold model revealed an optimal ADClow cutoff of 1241*10-6mm²/s for OS. Thereby, median OS for BEV-patients with ADClow≥1241 was 10.39 months vs. 8.09 months for those with ADClow<1241 (p=0.004). Similarly, median OS for non-BEV patients with ADClow≥1241 was 9.80 months vs. 7.79 months for those with ADClow<1241 (p=0.054). CONCLUSIONS: ADClow is an independent prognostic parameter for stratifying OS and PFS in patients with recurrent glioblastoma. Consequently, the previously suggested role of ADClow as predictive imaging biomarker could not be confirmed within this phase II/III trial.

Published
2020

44. Noninvasive Characterization of Tumor Angiogenesis and Oxygenation in Bevacizumab-treated Recurrent Glioblastoma by Using Dynamic Susceptibility MRI: Secondary Analysis of the European Organization for Research and Treatment of Cancer 26101 Trial

Author

Kickingereder, Philipp, primary, Brugnara, Gianluca, additional, Hansen, Mikkel Bo, additional, Nowosielski, Martha, additional, Pflüger, Irada, additional, Schell, Marianne, additional, Isensee, Fabian, additional, Foltyn, Martha, additional, Neuberger, Ulf, additional, Kessler, Tobias, additional, Sahm, Felix, additional, Wick, Antje, additional, Heiland, Sabine, additional, Weller, Michael, additional, Platten, Michael, additional, von Deimling, Andreas, additional, Maier-Hein, Klaus H., additional, Østergaard, Leif, additional, van den Bent, Martin J., additional, Gorlia, Thierry, additional, Wick, Wolfgang, additional, and Bendszus, Martin, additional

Published
2020
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45. Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial

Author

Schell, Marianne, primary, Pflüger, Irada, additional, Brugnara, Gianluca, additional, Isensee, Fabian, additional, Neuberger, Ulf, additional, Foltyn, Martha, additional, Kessler, Tobias, additional, Sahm, Felix, additional, Wick, Antje, additional, Nowosielski, Martha, additional, Heiland, Sabine, additional, Weller, Michael, additional, Platten, Michael, additional, Maier-Hein, Klaus H, additional, Von Deimling, Andreas, additional, Van Den Bent, Martin J, additional, Gorlia, Thierry, additional, Wick, Wolfgang, additional, Bendszus, Martin, additional, and Kickingereder, Philipp, additional

Published
2020
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46. Enhanced SPARCL1 expression in cancer stem cells improves preclinical modeling of glioblastoma by promoting both tumor infiltration and angiogenesis

Author

Gagliardi, Filippo, primary, Narayanan, Ashwin, additional, Gallotti, Alberto Luigi, additional, Pieri, Valentina, additional, Mazzoleni, Stefania, additional, Cominelli, Manuela, additional, Rezzola, Sara, additional, Corsini, Michela, additional, Brugnara, Gianluca, additional, Altabella, Luisa, additional, Politi, Letterio Salvatore, additional, Bacigaluppi, Marco, additional, Falini, Andrea, additional, Castellano, Antonella, additional, Ronca, Roberto, additional, Poliani, Pietro Luigi, additional, Mortini, Pietro, additional, and Galli, Rossella, additional

Published
2020
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47. T2/FLAIR-mismatch sign for noninvasive detection of IDH-mutant 1p/19q non-codeleted gliomas: validity and pathophysiology

Author

Foltyn, Martha, primary, Nieto Taborda, Karen Natalia, primary, Neuberger, Ulf, primary, Brugnara, Gianluca, primary, Reinhardt, Annekathrin, primary, Stichel, Damian, primary, Heiland, Sabine, primary, Herold-Mende, Christel, primary, Unterberg, Andreas, primary, Debus, Jürgen, primary, Deimling, Andreas von, primary, Wick, Wolfgang, primary, Bendszus, Martin, primary, and Kickingereder, Philipp, primary

Published
2020
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48. NIMG-02. NON-INVASIVE DETECTION OF IDH MUTANT 1p19q NON-CODELETED GLIOMAS USING THE T2-FLAIR MISMATCH SIGN

Author

Foltyn, Martha, primary, Natalia Nieto Taborda, Karen, additional, Neuberger, Ulf, additional, Reinhardt, Annekathrin, additional, Brugnara, Gianluca, additional, Unterberg, Andreas, additional, Debus, Jürgen, additional, Herold-Mende, Christel, additional, von Deimling, Andreas, additional, Wick, Wolfgang, additional, Bendszus, Martin, additional, and Kickingereder, Philipp, additional

Published
2019
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49. Dynamics of cerebral perfusion and oxygenation parameters following endovascular treatment of acute ischemic stroke.

Author

Brugnara, Gianluca, Herweh, Christian, Neuberger, Ulf, Hansen, Mikkel Bo, Ulfert, Christian, Mahmutoglu, Mustafa Ahmed, Foltyn, Martha, Nagel, Simon, Schönenberger, Silvia, Heiland, Sabine, Ringleb, Peter Arthur, Bendszus, Martin, Möhlenbruch, Markus, Rolf Pfaff, Johannes Alex, and Vollmuth, Philipp

Subjects
CONFIDENCE intervals, CEREBRAL circulation, ISCHEMIC stroke, CEREBRAL infarction, MANN Whitney U Test, DESCRIPTIVE statistics, REACTIVE oxygen species, ENDOVASCULAR surgery, COMPUTED tomography, STATISTICAL correlation, OXYGEN in the body
Abstract

Background We studied the effects of endovascular treatment (EVT) and the impact of the extent of recanalization on cerebral perfusion and oxygenation parameters in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO). Methods Forty-seven patients with anterior LVO underwent computed tomography perfusion (CTP) before and immediately after EVT. The entire ischemic region (Tmax >6 s) was segmented before intervention, and tissue perfusion (time-to-maximum (Tmax), time-to-peak (TTP), mean transit time (MTT), cerebral blood volume (CBV), cerebral blood flow (CBF)) and oxygenation (coefficient of variation (COV), capillary transit time heterogeneity (CTH), metabolic rate of oxygen (CMRO2), oxygen extraction fraction (OEF)) parameters were quantified from the segmented area at baseline and the corresponding area immediately after intervention, as well as within the ischemic core and penumbra. The impact of the extent of recanalization (modified Treatment in Cerebral Infarction (mTICI)) on CTP parameters was assessed with the Wilcoxon test and Pearson's correlation coefficients. Results The Tmax, MTT, OEF and CTH values immediately after EVT were lower in patients with complete (as compared with incomplete) recanalization, whereas CBF and COV values were higher (P<0.05) and no differences were found in other parameters. The ischemic penumbra immediately after EVT was lower in patients with complete recanalization as compared with those with incomplete recanalization (P=0.002), whereas no difference was found for the ischemic core (P=0.12). Specifically, higher mTICI scores were associated with a greater reduction of ischemic penumbra volumes (R²=-0.48 (95% CI -0.67 to -0.22), P=0.001) but not of ischemic core volumes (P=0.098). Conclusions Our study demonstrates that the ischemic penumbra is the key target of successful EVT in patients with AIS and largely determines its efficacy on a tissue level. Furthermore, we confirm the validity of the mTICI score as a surrogate parameter of interventional success on a tissue perfusion level. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Automated brain extraction of multisequence MRI using artificial neural networks

Author

Isensee, Fabian, primary, Schell, Marianne, additional, Pflueger, Irada, additional, Brugnara, Gianluca, additional, Bonekamp, David, additional, Neuberger, Ulf, additional, Wick, Antje, additional, Schlemmer, Heinz‐Peter, additional, Heiland, Sabine, additional, Wick, Wolfgang, additional, Bendszus, Martin, additional, Maier‐Hein, Klaus H., additional, and Kickingereder, Philipp, additional

Published
2019
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