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2. The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Author

Li, Hongwei Bran, Conte, Gian Marco, Hu, Qingqiao, Anwar, Syed Muhammad, Kofler, Florian, Ezhov, Ivan, van Leemput, Koen, Piraud, Marie, Diaz, Maria, Cole, Byrone, Calabrese, Evan, Rudie, Jeff, Meissen, Felix, Adewole, Maruf, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W., Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M., Wiest, Roland, Kirschke, Jan, Colen, Rivka R., Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva, Javier, Colak, Meyer Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Yu, Thomas, Baid, Ujjwal, Bakas, Spyridon, Linguraru, Marius George, Menze, Bjoern, Iglesias, Juan Eugenio, and Wiestler, Benedikt

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

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions., Comment: Technical report of BraSyn

Published
2023

3. The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

Author

Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Ehrlich, Stefan K, Reinke, Annika, Oswald, Eva, Waldmannstetter, Diana, Hoelzl, Florian, Horvath, Izabela, Turgut, Oezguen, Shit, Suprosanna, Bukas, Christina, Yang, Kaiyuan, Paetzold, Johannes C., de da Rosa, Ezequiel, Mekki, Isra, Vinayahalingam, Shankeeth, Kassem, Hasan, Zhang, Juexin, Chen, Ke, Weng, Ying, Durrer, Alicia, Cattin, Philippe C., Wolleb, Julia, Sadique, M. S., Rahman, M. M., Farzana, W., Temtam, A., Iftekharuddin, K. M., Adewole, Maruf, Anwar, Syed Muhammad, Baid, Ujjwal, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Li, Hongwei Bran, Moawad, Ahmed W, Conte, Gian-Marco, Farahani, Keyvan, Eddy, James, Sheller, Micah, Pati, Sarthak, Karagyris, Alexandros, Aristizabal, Alejandro, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Johanson, Elaine, Meier, Zeke, Familiar, Ariana, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M, Wiest, Roland, Kirschke, Jan, Colen, Rivka R, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Colak, Errol, Crivellaro, Priscila, Jakab, Andras, Fatade, Abiodun, Omidiji, Olubukola, Lagos, Rachel Akinola, Olatunji, O O, Khanna, Goldey, Kirkpatrick, John, Alonso-Basanta, Michelle, Rashid, Arif, Bornhorst, Miriam, Nabavizadeh, Ali, Lepore, Natasha, Palmer, Joshua, Porras, Antonio, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Calabrese, Evan, Rudie, Jeffrey David, Linguraru, Marius George, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, and Menze, Bjoern H

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

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include, but are not limited to, algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge., Comment: 14 pages, 6 figures

Published
2023

4. 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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5. Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements

Author

Villanueva-Meyer, Javier E, Bakas, Spyridon, Tiwari, Pallavi, Lupo, Janine M, Calabrese, Evan, Davatzikos, Christos, Bi, Wenya Linda, Ismail, Marwa, Akbari, Hamed, Lohmann, Philipp, Booth, Thomas C, Wiestler, Benedikt, Aerts, Hugo J W L, Rasool, Ghulam, Tonn, Joerg C, Nowosielski, Martha, Jain, Rajan, Colen, Rivka R, Pati, Sarthak, Baid, Ujjwal, Vollmuth, Philipp, Macdonald, David, Vogelbaum, Michael A, Chang, Susan M, Huang, Raymond Y, and Galldiks, Norbert

Published
2024
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6. The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Author

Baid, Ujjwal, Ghodasara, Satyam, Mohan, Suyash, Bilello, Michel, Calabrese, Evan, Colak, Errol, Farahani, Keyvan, Kalpathy-Cramer, Jayashree, Kitamura, Felipe C., Pati, Sarthak, Prevedello, Luciano M., Rudie, Jeffrey D., Sako, Chiharu, Shinohara, Russell T., Bergquist, Timothy, Chai, Rong, Eddy, James, Elliott, Julia, Reade, Walter, Schaffter, Thomas, Yu, Thomas, Zheng, Jiaxin, Moawad, Ahmed W., Coelho, Luiz Otavio, McDonnell, Olivia, Miller, Elka, Moron, Fanny E., Oswood, Mark C., Shih, Robert Y., Siakallis, Loizos, Bronstein, Yulia, Mason, James R., Miller, Anthony F., Choudhary, Gagandeep, Agarwal, Aanchal, Besada, Cristina H., Derakhshan, Jamal J., Diogo, Mariana C., Do-Dai, Daniel D., Farage, Luciano, Go, John L., Hadi, Mohiuddin, Hill, Virginia B., Iv, Michael, Joyner, David, Lincoln, Christie, Lotan, Eyal, Miyakoshi, Asako, Sanchez-Montano, Mariana, Nath, Jaya, Nguyen, Xuan V., Nicolas-Jilwan, Manal, Jimenez, Johanna Ortiz, Ozturk, Kerem, Petrovic, Bojan D., Shah, Chintan, Shah, Lubdha M., Sharma, Manas, Simsek, Onur, Singh, Achint K., Soman, Salil, Statsevych, Volodymyr, Weinberg, Brent D., Young, Robert J., Ikuta, Ichiro, Agarwal, Amit K., Cambron, Sword C., Silbergleit, Richard, Dusoi, Alexandru, Postma, Alida A., Letourneau-Guillon, Laurent, Perez-Carrillo, Gloria J. Guzman, Saha, Atin, Soni, Neetu, Zaharchuk, Greg, Zohrabian, Vahe M., Chen, Yingming, Cekic, Milos M., Rahman, Akm, Small, Juan E., Sethi, Varun, Davatzikos, Christos, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Freymann, John B., Kirby, Justin S., Wiestler, Benedikt, Crivellaro, Priscila, Colen, Rivka R., Kotrotsou, Aikaterini, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Fathallah-Shaykh, Hassan, Wiest, Roland, Jakab, Andras, Weber, Marc-Andre, Mahajan, Abhishek, Menze, Bjoern, Flanders, Adam E., and Bakas, Spyridon

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. Since its inception, BraTS has been focusing on being a common benchmarking venue for brain glioma segmentation algorithms, with well-curated multi-institutional multi-parametric magnetic resonance imaging (mpMRI) data. Gliomas are the most common primary malignancies of the central nervous system, with varying degrees of aggressiveness and prognosis. The RSNA-ASNR-MICCAI BraTS 2021 challenge targets the evaluation of computational algorithms assessing the same tumor compartmentalization, as well as the underlying tumor's molecular characterization, in pre-operative baseline mpMRI data from 2,040 patients. Specifically, the two tasks that BraTS 2021 focuses on are: a) the segmentation of the histologically distinct brain tumor sub-regions, and b) the classification of the tumor's O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. The performance evaluation of all participating algorithms in BraTS 2021 will be conducted through the Sage Bionetworks Synapse platform (Task 1) and Kaggle (Task 2), concluding in distributing to the top ranked participants monetary awards of $60,000 collectively., Comment: 19 pages, 2 figures, 1 table

Published
2021

7. Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium

Author

Lee, Matthew D., Patel, Sohil H., Mohan, Suyash, Akbari, Hamed, Bakas, Spyridon, Nasrallah, MacLean P., Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, LaMontagne, Pamela, Marcus, Daniel S., Colen, Rivka R., Balana, Carmen, Choi, Yoon Seong, Badve, Chaitra, Barnholtz-Sloan, Jill S., Sloan, Andrew E., Booth, Thomas C., Palmer, Joshua D., Dicker, Adam P., Flanders, Adam E., Shi, Wenyin, Griffith, Brent, Poisson, Laila M., Chakravarti, Arnab, Mahajan, Abhishek, Chang, Susan, Orringer, Daniel, Davatzikos, Christos, and Jain, Rajan

Published
2023
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8. Associating T1-Weighted and T2-Weighted Magnetic Resonance Imaging Radiomic Signatures With Preoperative Symptom Severity in Patients With Cervical Spondylotic Myelopathy

Author

Alan, Nima, Zenkin, Serafettin, Lavadi, Raj Swaroop, Legarreta, Andrew D., Hudson, Joseph S., Fields, Daryl P., Agarwal, Nitin, Mamindla, Priyadarshini, Ak, Murat, Peddagangireddy, Vishal, Puccio, Lauren, Buell, Thomas J., Hamilton, D. Kojo, Kanter, Adam S., Okonkwo, David O., Zinn, Pascal O., and Colen, Rivka R.

Published
2024
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9. The Federated Tumor Segmentation (FeTS) Challenge

Author

Pati, Sarthak, Baid, Ujjwal, Zenk, Maximilian, Edwards, Brandon, Sheller, Micah, Reina, G. Anthony, Foley, Patrick, Gruzdev, Alexey, Martin, Jason, Albarqouni, Shadi, Chen, Yong, Shinohara, Russell Taki, Reinke, Annika, Zimmerer, David, Freymann, John B., Kirby, Justin S., Davatzikos, Christos, Colen, Rivka R., Kotrotsou, Aikaterini, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Fathallah-Shaykh, Hassan, Wiest, Roland, Jakab, Andras, Weber, Marc-Andre, Mahajan, Abhishek, Maier-Hein, Lena, Kleesiek, Jens, Menze, Bjoern, Maier-Hein, Klaus, and Bakas, Spyridon

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

This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standard for validation of biomedical image analysis methods. However, the actual performance of participating (even the winning) algorithms on "real-world" clinical data often remains unclear, as the data included in challenges are usually acquired in very controlled settings at few institutions. The seemingly obvious solution of just collecting increasingly more data from more institutions in such challenges does not scale well due to privacy and ownership hurdles. Towards alleviating these concerns, we are proposing the FeTS challenge 2021 to cater towards both the development and the evaluation of models for the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Specifically, the FeTS 2021 challenge uses clinically acquired, multi-institutional magnetic resonance imaging (MRI) scans from the BraTS 2020 challenge, as well as from various remote independent institutions included in the collaborative network of a real-world federation (https://www.fets.ai/). The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i.e. on data from institutional distributions that were not part of the training datasets.

Published
2021

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. Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer

Author

Chen, Bihong T, Jin, Taihao, Ye, Ningrong, Mambetsariev, Isa, Wang, Tao, Wong, Chi Wah, Chen, Zikuan, Rockne, Russell C, Colen, Rivka R, Holodny, Andrei I, Sampath, Sagus, and Salgia, Ravi

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Lung, Biomedical Imaging, Clinical Research, Cancer, Lung Cancer, Neurosciences, 4.2 Evaluation of markers and technologies, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Good Health and Well Being, radiomics, machine learning, survival, lung cancer, brain metastases, brain MRI, artificial intelligence, Clinical sciences, Oncology and carcinogenesis
Abstract

Background: Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features that were important for predicting survival duration. Methods: We retrospectively identified our study cohort via an institutional database search for patients with brain metastases from EGFR, ALK, and/or KRAS mutation-positive NSCLC. We segmented the brain metastatic tumors on the brain MR images, extracted radiomic features, constructed radiomic scores from significant radiomic features based on multivariate Cox regression analysis (p < 0.05), and built predictive models for survival duration. Result: Of the 110 patients in the cohort (mean age 57.51 ± 12.32 years; range: 22-85 years, M:F = 37:73), 75, 26, and 15 had NSCLC with EGFR, ALK, and KRAS mutations, respectively. Predictive modeling of survival duration using both clinical and radiomic features yielded areas under the receiver operative characteristic curve of 0.977, 0.905, and 0.947 for the EGFR, ALK, and KRAS mutation-positive groups, respectively. Radiomic scores enabled the separation of each mutation-positive group into two subgroups with significantly different survival durations, i.e., shorter vs. longer duration when comparing to the median survival duration of the group. Conclusion: Our data supports the use of radiomic scores, based on MR imaging of brain metastases from NSCLC, as non-invasive biomarkers for survival duration. Future research with a larger sample size and external cohorts is needed to validate our results.

Published
2021

13. Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

Author

Chen, Bihong T, Chen, Zikuan, Ye, Ningrong, Mambetsariev, Isa, Fricke, Jeremy, Daniel, Ebenezer, Wang, George, Wong, Chi Wah, Rockne, Russell C, Colen, Rivka R, Nasser, Mohd W, Batra, Surinder K, Holodny, Andrei I, Sampath, Sagus, and Salgia, Ravi

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Lung Cancer, Lung, Cancer, Clinical Research, Biomedical Imaging, Good Health and Well Being, small cell lung cancer, non-small cell lung cancer, computed tomography radiomics, non-linear classifier, artificial neural network, Clinical sciences, Oncology and carcinogenesis
Abstract

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.

Published
2020

14. Cancer Imaging in Immunotherapy

Author

Ak, Murat, Eleneen, Yousra, Ayoub, Mira, Colen, Rivka R., Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Steinlein, Ortrud, Series Editor, Xiao, Junjie, Series Editor, Naing, Aung, editor, and Hajjar, Joud, editor

Published
2021
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15. Extracellular vesicle PD-L1 dynamics predict durable response to immune-checkpoint inhibitors and survival in patients with non-small cell lung cancer

Author

de Miguel-Perez, Diego, Russo, Alessandro, Arrieta, Oscar, Ak, Murat, Barron, Feliciano, Gunasekaran, Muthukumar, Mamindla, Priyadarshini, Lara-Mejia, Luis, Peterson, Christine B., Er, Mehmet E., Peddagangireddy, Vishal, Buemi, Francesco, Cooper, Brandon, Manca, Paolo, Lapidus, Rena G., Hsia, Ru-Ching, Cardona, Andres F., Naing, Aung, Kaushal, Sunjay, Hirsch, Fred R., Mack, Philip C., Serrano, Maria Jose, Adamo, Vincenzo, Colen, Rivka R., and Rolfo, Christian

Published
2022
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16. Cancer Imaging in Immunotherapy

Author

Ayoub, Mira, Eleneen, Yousra, Colen, Rivka R., Crusio, Wim E., Series Editor, Lambris, John D., Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, Naing, Aung, editor, and Hajjar, Joud, editor

Published
2020
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17. Novel theranostic agent for PET imaging and targeted radiopharmaceutical therapy of tumour-infiltrating immune cells in glioma

Author

Foster, Alexandra, Nigam, Shubhanchi, Tatum, David S, Raphael, Itay, Xu, Jide, Kumar, Rajeev, Plakseychuk, Elizabeth, Latoche, Joseph D, Vincze, Sarah, Li, Bo, Giri, Rajan, McCarl, Lauren H, Edinger, Robert, Ak, Murat, Peddagangireddy, Vishal, Foley, Lesley M, Hitchens, T Kevin, Colen, Rivka R, Pollack, Ian F, Panigrahy, Ashok, Magda, Darren, Anderson, Carolyn J, Edwards, W Barry, and Kohanbash, Gary

Published
2021
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18. Evaluation of radiomics as a predictor of efficacy and the tumor immune microenvironment in anti‐PD‐1 mAb treated recurrent/metastatic squamous cell carcinoma of the head and neck patients.

Author

Zandberg, Dan P., Zenkin, Serafettin, Ak, Murat, Mamindla, Priyadarshini, Peddagangireddy, Vishal, Hsieh, Ronan, Anderson, Jennifer L., Delgoffe, Greg M., Menk, Ashely, Skinner, Heath D., Duvvuri, Umamaheswar, Ferris, Robert L., and Colen, Rivka R.

Abstract

Background: We retrospectively evaluated radiomics as a predictor of the tumor microenvironment (TME) and efficacy with anti‐PD‐1 mAb (IO) in R/M HNSCC. Methods: Radiomic feature extraction was performed on pre‐treatment CT scans segmented using 3D slicer v4.10.2 and key features were selected using LASSO regularization method to build classification models with XGBoost algorithm by incorporating cross‐validation techniques to calculate accuracy, sensitivity, and specificity. Outcome measures evaluated were disease control rate (DCR) by RECIST 1.1, PFS, and OS and hypoxia and CD8 T cells in the TME. Results: Radiomics features predicted DCR with accuracy, sensitivity, and specificity of 76%, 73%, and 83%, for OS 77%, 86%, 70%, PFS 82%, 75%, 89%, and in the TME, for high hypoxia 80%, 88%, and 72% and high CD8 T cells 91%, 83%, and 100%, respectively. Conclusion: Radiomics accurately predicted the efficacy of IO and features of the TME in R/M HNSCC. Further study in a larger patient population is warranted. [ABSTRACT FROM AUTHOR]

Published
2025
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21. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas

Author

Brat, Daniel J, Verhaak, Roel GW, Aldape, Kenneth D, Yung, WK Alfred, Salama, Sofie R, Cooper, Lee AD, Rheinbay, Esther, Miller, C Ryan, Vitucci, Mark, Morozova, Olena, Robertson, A Gordon, Noushmehr, Houtan, Laird, Peter W, Cherniack, Andrew D, Akbani, Rehan, Huse, Jason T, Ciriello, Giovanni, Poisson, Laila M, Barnholtz-Sloan, Jill S, Berger, Mitchel S, Brennan, Cameron, Colen, Rivka R, Colman, Howard, Flanders, Adam E, Giannini, Caterina, Grifford, Mia, Iavarone, Antonio, Jain, Rajan, Joseph, Isaac, Kim, Jaegil, Kasaian, Katayoon, Mikkelsen, Tom, Murray, Bradley A, O'Neill, Brian Patrick, Pachter, Lior, Parsons, Donald W, Sougnez, Carrie, Sulman, Erik P, Vandenberg, Scott R, Van Meir, Erwin G, von Deimling, Andreas, Zhang, Hailei, Crain, Daniel, Lau, Kevin, Mallery, David, Morris, Scott, Paulauskis, Joseph, Penny, Robert, Shelton, Troy, Sherman, Mark, Yena, Peggy, Black, Aaron, Bowen, Jay, Dicostanzo, Katie, Gastier-Foster, Julie, Leraas, Kristen M, Lichtenberg, Tara M, Pierson, Christopher R, Ramirez, Nilsa C, Taylor, Cynthia, Weaver, Stephanie, Wise, Lisa, Zmuda, Erik, Davidsen, Tanja, Demchok, John A, Eley, Greg, Ferguson, Martin L, Hutter, Carolyn M, Mills Shaw, Kenna R, Ozenberger, Bradley A, Sheth, Margi, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean Claude, Ayala, Brenda, Baboud, Julien, Chudamani, Sudha, Jensen, Mark A, Liu, Jia, Pihl, Todd, Raman, Rohini, Wan, Yunhu, Wu, Ye, Ally, Adrian, Auman, J Todd, Balasundaram, Miruna, Balu, Saianand, Baylin, Stephen B, Beroukhim, Rameen, Bootwalla, Moiz S, Bowlby, Reanne, Bristow, Christopher A, Brooks, Denise, Butterfield, Yaron, Carlsen, Rebecca, Carter, Scott, Chin, Lynda, and Chu, Andy

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Clinical Research, Human Genome, Brain Cancer, Rare Diseases, Neurosciences, Biotechnology, Cancer Genomics, Genetics, Cancer, Brain Disorders, Adolescent, Adult, Aged, Chromosomes, Human, Pair 1, Chromosomes, Human, Pair 19, Cluster Analysis, DNA, Neoplasm, Female, Genes, p53, Glioblastoma, Glioma, Humans, Kaplan-Meier Estimate, Male, Middle Aged, Mutation, Neoplasm Grading, Proportional Hazards Models, Sequence Analysis, DNA, Signal Transduction, Cancer Genome Atlas Research Network, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundDiffuse low-grade and intermediate-grade gliomas (which together make up the lower-grade gliomas, World Health Organization grades II and III) have highly variable clinical behavior that is not adequately predicted on the basis of histologic class. Some are indolent; others quickly progress to glioblastoma. The uncertainty is compounded by interobserver variability in histologic diagnosis. Mutations in IDH, TP53, and ATRX and codeletion of chromosome arms 1p and 19q (1p/19q codeletion) have been implicated as clinically relevant markers of lower-grade gliomas.MethodsWe performed genomewide analyses of 293 lower-grade gliomas from adults, incorporating exome sequence, DNA copy number, DNA methylation, messenger RNA expression, microRNA expression, and targeted protein expression. These data were integrated and tested for correlation with clinical outcomes.ResultsUnsupervised clustering of mutations and data from RNA, DNA-copy-number, and DNA-methylation platforms uncovered concordant classification of three robust, nonoverlapping, prognostically significant subtypes of lower-grade glioma that were captured more accurately by IDH, 1p/19q, and TP53 status than by histologic class. Patients who had lower-grade gliomas with an IDH mutation and 1p/19q codeletion had the most favorable clinical outcomes. Their gliomas harbored mutations in CIC, FUBP1, NOTCH1, and the TERT promoter. Nearly all lower-grade gliomas with IDH mutations and no 1p/19q codeletion had mutations in TP53 (94%) and ATRX inactivation (86%). The large majority of lower-grade gliomas without an IDH mutation had genomic aberrations and clinical behavior strikingly similar to those found in primary glioblastoma.ConclusionsThe integration of genomewide data from multiple platforms delineated three molecular classes of lower-grade gliomas that were more concordant with IDH, 1p/19q, and TP53 status than with histologic class. Lower-grade gliomas with an IDH mutation either had 1p/19q codeletion or carried a TP53 mutation. Most lower-grade gliomas without an IDH mutation were molecularly and clinically similar to glioblastoma. (Funded by the National Institutes of Health.).

Published
2015

22. TP53-PTEN-NF1 depletion in human brain organoids produces a glioma phenotype in vitro

Author

Singh, Sanjay K., primary, Wang, Yan, additional, Habib, Ahmed, additional, Priyadarshini, Mamindla, additional, Kodavali, Chowdari V., additional, Chen, Apeng, additional, Ma, Wencai, additional, Wang, Jing, additional, Hameed, N. U. Farrukh, additional, Hu, Baoli, additional, Fuller, Gregory N., additional, Kulich, Scott M., additional, Amankulor, Nduka, additional, Colen, Rivka R., additional, Edwards, Lincoln A., additional, and Zinn, Pascal O., additional

Published
2023
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23. A prospective in silico analysis of interdisciplinary and interobserver spatial variability in post-operative target delineation of high-risk oral cavity cancers: Does physician specialty matter?

Author

Ng, Sweet Ping, Dyer, Brandon A., Kalpathy-Cramer, Jayashree, Mohamed, Abdallah Sherif Radwan, Awan, Musaddiq J., Gunn, G. Brandon, Phan, Jack, Zafereo, Mark, Debnam, J. Matthew, Lewis, Carol M., Colen, Rivka R., Kupferman, Michael E., Guha-Thakurta, Nandita, Canahuate, Guadalupe, Marai, G. Elisabeta, Vock, David, Hamilton, Bronwyn, Holland, John, Cardenas, Carlos E., Lai, Stephen, Rosenthal, David, and Fuller, Clifton David

Published
2018
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32. The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting

Author

Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Oswald, Eva, de da Rosa, Ezequiel, Li, Hongwei Bran, Baid, Ujjwal, Hoelzl, Florian, Turgut, Oezguen, Horvath, Izabela, Waldmannstetter, Diana, Bukas, Christina, Adewole, Maruf, Anwar, Syed Muhammad, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W, Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Conte, Gian-Marco, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M, Wiest, Roland, Kirschke, Jan, Colen, Rivka R, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Colak, Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Rueckert, Daniel, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, Menze, Bjoern, Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Oswald, Eva, de da Rosa, Ezequiel, Li, Hongwei Bran, Baid, Ujjwal, Hoelzl, Florian, Turgut, Oezguen, Horvath, Izabela, Waldmannstetter, Diana, Bukas, Christina, Adewole, Maruf, Anwar, Syed Muhammad, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W, Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Conte, Gian-Marco, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M, Wiest, Roland, Kirschke, Jan, Colen, Rivka R, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Colak, Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Rueckert, Daniel, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, and Menze, Bjoern

Abstract

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with a scan that is already pathological. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantees for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS 2023 inpainting challenge. Here, the participants' task is to explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later it will be updated to summarize the findings of the challenge. The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada., Comment: 5 pages, 1 figure

Published
2023

36. Magnetic resonance imaging of swallowing-related structures in nasopharyngeal carcinoma patients receiving IMRT: Longitudinal dose–response characterization of quantitative signal kinetics

Author

Messer, Jay A., Mohamed, Abdallah S.R., Hutcheson, Katherine A., Ding, Yao, Lewin, Jan S., Wang, Jihong, Lai, Stephen Y., Frank, Steven J., Garden, Adam S., Sandulache, Vlad, Eichelberger, Hillary, French, Chloe C., Colen, Rivka R., Phan, Jack, Kalpathy-Cramer, Jayashree, Hazle, John D., Rosenthal, David I., Gunn, G. Brandon, and Fuller, Clifton D.

Published
2016
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39. Supplementary Figure 4 from A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

Author

Zinn, Pascal O., primary, Singh, Sanjay K., primary, Kotrotsou, Aikaterini, primary, Hassan, Islam, primary, Thomas, Ginu, primary, Luedi, Markus M., primary, Elakkad, Ahmed, primary, Elshafeey, Nabil, primary, Idris, Tagwa, primary, Mosley, Jennifer, primary, Gumin, Joy, primary, Fuller, Gregory N., primary, de Groot, John F., primary, Baladandayuthapani, Veera, primary, Sulman, Erik P., primary, Kumar, Ashok J., primary, Sawaya, Raymond, primary, Lang, Frederick F., primary, Piwnica-Worms, David, primary, and Colen, Rivka R., primary

Published
2023
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40. Supplementary Figure 5 from A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

Author

Zinn, Pascal O., primary, Singh, Sanjay K., primary, Kotrotsou, Aikaterini, primary, Hassan, Islam, primary, Thomas, Ginu, primary, Luedi, Markus M., primary, Elakkad, Ahmed, primary, Elshafeey, Nabil, primary, Idris, Tagwa, primary, Mosley, Jennifer, primary, Gumin, Joy, primary, Fuller, Gregory N., primary, de Groot, John F., primary, Baladandayuthapani, Veera, primary, Sulman, Erik P., primary, Kumar, Ashok J., primary, Sawaya, Raymond, primary, Lang, Frederick F., primary, Piwnica-Worms, David, primary, and Colen, Rivka R., primary

Published
2023
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41. Supplementary Figure 8 from A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

Author

Zinn, Pascal O., primary, Singh, Sanjay K., primary, Kotrotsou, Aikaterini, primary, Hassan, Islam, primary, Thomas, Ginu, primary, Luedi, Markus M., primary, Elakkad, Ahmed, primary, Elshafeey, Nabil, primary, Idris, Tagwa, primary, Mosley, Jennifer, primary, Gumin, Joy, primary, Fuller, Gregory N., primary, de Groot, John F., primary, Baladandayuthapani, Veera, primary, Sulman, Erik P., primary, Kumar, Ashok J., primary, Sawaya, Raymond, primary, Lang, Frederick F., primary, Piwnica-Worms, David, primary, and Colen, Rivka R., primary

Published
2023
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42. Supplementary Figure Legends and Methods from A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

Author

Zinn, Pascal O., primary, Singh, Sanjay K., primary, Kotrotsou, Aikaterini, primary, Hassan, Islam, primary, Thomas, Ginu, primary, Luedi, Markus M., primary, Elakkad, Ahmed, primary, Elshafeey, Nabil, primary, Idris, Tagwa, primary, Mosley, Jennifer, primary, Gumin, Joy, primary, Fuller, Gregory N., primary, de Groot, John F., primary, Baladandayuthapani, Veera, primary, Sulman, Erik P., primary, Kumar, Ashok J., primary, Sawaya, Raymond, primary, Lang, Frederick F., primary, Piwnica-Worms, David, primary, and Colen, Rivka R., primary

Published
2023
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43. Supplementary Figure 6 from A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

Author

Zinn, Pascal O., primary, Singh, Sanjay K., primary, Kotrotsou, Aikaterini, primary, Hassan, Islam, primary, Thomas, Ginu, primary, Luedi, Markus M., primary, Elakkad, Ahmed, primary, Elshafeey, Nabil, primary, Idris, Tagwa, primary, Mosley, Jennifer, primary, Gumin, Joy, primary, Fuller, Gregory N., primary, de Groot, John F., primary, Baladandayuthapani, Veera, primary, Sulman, Erik P., primary, Kumar, Ashok J., primary, Sawaya, Raymond, primary, Lang, Frederick F., primary, Piwnica-Worms, David, primary, and Colen, Rivka R., primary

Published
2023
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44. Supplementary Figure 7 from A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

Author

Zinn, Pascal O., primary, Singh, Sanjay K., primary, Kotrotsou, Aikaterini, primary, Hassan, Islam, primary, Thomas, Ginu, primary, Luedi, Markus M., primary, Elakkad, Ahmed, primary, Elshafeey, Nabil, primary, Idris, Tagwa, primary, Mosley, Jennifer, primary, Gumin, Joy, primary, Fuller, Gregory N., primary, de Groot, John F., primary, Baladandayuthapani, Veera, primary, Sulman, Erik P., primary, Kumar, Ashok J., primary, Sawaya, Raymond, primary, Lang, Frederick F., primary, Piwnica-Worms, David, primary, and Colen, Rivka R., primary

Published
2023
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45. Supplementary Figure 3 from A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

Author

Zinn, Pascal O., primary, Singh, Sanjay K., primary, Kotrotsou, Aikaterini, primary, Hassan, Islam, primary, Thomas, Ginu, primary, Luedi, Markus M., primary, Elakkad, Ahmed, primary, Elshafeey, Nabil, primary, Idris, Tagwa, primary, Mosley, Jennifer, primary, Gumin, Joy, primary, Fuller, Gregory N., primary, de Groot, John F., primary, Baladandayuthapani, Veera, primary, Sulman, Erik P., primary, Kumar, Ashok J., primary, Sawaya, Raymond, primary, Lang, Frederick F., primary, Piwnica-Worms, David, primary, and Colen, Rivka R., primary

Published
2023
Full Text
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46. Supplementary Tables from A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

Author

Zinn, Pascal O., primary, Singh, Sanjay K., primary, Kotrotsou, Aikaterini, primary, Hassan, Islam, primary, Thomas, Ginu, primary, Luedi, Markus M., primary, Elakkad, Ahmed, primary, Elshafeey, Nabil, primary, Idris, Tagwa, primary, Mosley, Jennifer, primary, Gumin, Joy, primary, Fuller, Gregory N., primary, de Groot, John F., primary, Baladandayuthapani, Veera, primary, Sulman, Erik P., primary, Kumar, Ashok J., primary, Sawaya, Raymond, primary, Lang, Frederick F., primary, Piwnica-Worms, David, primary, and Colen, Rivka R., primary

Published
2023
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47. Supplementary Materials and Methods, Supplementary Figures 1-7, Supplementary Tables 1-2 from CD90 Expression Controls Migration and Predicts Dasatinib Response in Glioblastoma

Author

Avril, Tony, primary, Etcheverry, Amandine, primary, Pineau, Raphaël, primary, Obacz, Joanna, primary, Jegou, Gwénaële, primary, Jouan, Florence, primary, Le Reste, Pierre-Jean, primary, Hatami, Masumeh, primary, Colen, Rivka R., primary, Carlson, Brett L., primary, Decker, Paul A., primary, Sarkaria, Jann N., primary, Vauléon, Elodie, primary, Chiforeanu, Dan Cristian, primary, Clavreul, Anne, primary, Mosser, Jean, primary, Chevet, Eric, primary, and Quillien, Véronique, primary

Published
2023
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48. Prospective observer and software-based assessment of magnetic resonance imaging quality in head and neck cancer: Should standard positioning and immobilization be required for radiation therapy applications?

Author

Ding, Yao, Mohamed, Abdallah S.R., Yang, Jinzhong, Colen, Rivka R., Frank, Steven J., Wang, Jihong, Wassal, Eslam Y., Wang, Wenjie, Kantor, Michael E., Balter, Peter A., Rosenthal, David I., Lai, Stephen Y., Hazle, John D., and Fuller, Clifton D.

Published
2015
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49. Characteristics and kinetics of cervical lymph node regression after radiation therapy for human papillomavirus-associated oropharyngeal carcinoma: Quantitative image analysis of post-radiotherapy response

Author

Tang, Chad, Fuller, Clifton D., Garden, Adam S., Awan, Musaddiq J., Colen, Rivka R., Morrison, William H., Frank, Steven J., Beadle, Beth M., Phan, Jack, Sturgis, Erich M., Zafereo, Mark E., Weber, Randal S., Rosenthal, David I., and Gunn, G. Brandon

Published
2015
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