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1. Unraveling Radiomics Complexity: Strategies for Optimal Simplicity in Predictive Modeling

Author

Loutfi, Mahdi Ait Lhaj, Podasca, Teodora Boblea, Zwanenburg, Alex, Upadhaya, Taman, Barrios, Jorge, Raleigh, David R., Chen, William C., Capaldi, Dante P. I., Zheng, Hong, Gevaert, Olivier, Wu, Jing, Silva, Alvin C., Zhang, Paul J., Bai, Harrison X., Seuntjens, Jan, Löck, Steffen, Richard, Patrick O., Morin, Olivier, Reinhold, Caroline, Lepage, Martin, and Vallières, Martin

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

Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational requirements all underscore the need for an effective method to identify the smallest set of predictive features for a given clinical problem. Purpose: Develop a methodology and tools to identify and explain the smallest set of predictive radiomic features. Materials and Methods: 89,714 radiomic features were extracted from five cancer datasets: low-grade glioma, meningioma, non-small cell lung cancer (NSCLC), and two renal cell carcinoma cohorts (n=2104). Features were categorized by computational complexity into morphological, intensity, texture, linear filters, and nonlinear filters. Models were trained and evaluated on each complexity level using the area under the curve (AUC). The most informative features were identified, and their importance was explained. The optimal complexity level and associated most informative features were identified using systematic statistical significance analyses and a false discovery avoidance procedure, respectively. Their predictive importance was explained using a novel tree-based method. Results: MEDimage, a new open-source tool, was developed to facilitate radiomic studies. Morphological features were optimal for MRI-based meningioma (AUC: 0.65) and low-grade glioma (AUC: 0.68). Intensity features were optimal for CECT-based renal cell carcinoma (AUC: 0.82) and CT-based NSCLC (AUC: 0.76). Texture features were optimal for MRI-based renal cell carcinoma (AUC: 0.72). Tuning the Hounsfield unit range improved results for CECT-based renal cell carcinoma (AUC: 0.86). Conclusion: Our proposed methodology and software can estimate the optimal radiomics complexity level for specific medical outcomes, potentially simplifying the use of radiomics in predictive modeling across various contexts.

Published
2024

2. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

Author

Kocak, Burak, Akinci D’Antonoli, Tugba, Mercaldo, Nathaniel, Alberich-Bayarri, Angel, Baessler, Bettina, Ambrosini, Ilaria, Andreychenko, Anna E., Bakas, Spyridon, Beets-Tan, Regina G. H., Bressem, Keno, Buvat, Irene, Cannella, Roberto, Cappellini, Luca Alessandro, Cavallo, Armando Ugo, Chepelev, Leonid L., Chu, Linda Chi Hang, Demircioglu, Aydin, deSouza, Nandita M., Dietzel, Matthias, Fanni, Salvatore Claudio, Fedorov, Andrey, Fournier, Laure S., Giannini, Valentina, Girometti, Rossano, Groot Lipman, Kevin B. W., Kalarakis, Georgios, Kelly, Brendan S., Klontzas, Michail E., Koh, Dow-Mu, Kotter, Elmar, Lee, Ho Yun, Maas, Mario, Marti-Bonmati, Luis, Müller, Henning, Obuchowski, Nancy, Orlhac, Fanny, Papanikolaou, Nikolaos, Petrash, Ekaterina, Pfaehler, Elisabeth, Pinto dos Santos, Daniel, Ponsiglione, Andrea, Sabater, Sebastià, Sardanelli, Francesco, Seeböck, Philipp, Sijtsema, Nanna M., Stanzione, Arnaldo, Traverso, Alberto, Ugga, Lorenzo, Vallières, Martin, van Dijk, Lisanne V., van Griethuysen, Joost J. M., van Hamersvelt, Robbert W., van Ooijen, Peter, Vernuccio, Federica, Wang, Alan, Williams, Stuart, Witowski, Jan, Zhang, Zhongyi, Zwanenburg, Alex, and Cuocolo, Renato

Published
2024
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4. Contributors

Author

Ahmed, Sara, primary, Au Yong, Tracy P.T., additional, Bakst, Richard, additional, Barnett, Gill, additional, Bond, Sarah, additional, Boon, Ian S., additional, Boon, Cheng S., additional, Buckstein, Michael, additional, Chen, Liyuan, additional, Chowdhry, Amit Kumar, additional, Cui, Sunan, additional, Dekker, Andre, additional, Dohopolski, Michael, additional, El-Charif, Omar, additional, El Naqa, Issam, additional, Fijten, Rianne, additional, Fuller, Clifton D., additional, Green, Andrew, additional, Jain, Anshu, additional, Kalendralis, Petros, additional, Kalet, Alan M., additional, Kang, John, additional, Kann, Benjamin H., additional, Kerns, Sarah, additional, Kim, Ellen, additional, Kiser, Kendall J., additional, Levitin, Ronald, additional, Liu, Jinyuan, additional, Lyon, Robert J., additional, McCoy, Lance A., additional, McDonald, Brigid A., additional, McWilliam, Alan, additional, Milano, Michael, additional, Mulder, Samuel, additional, Nayak, Stuti, additional, Nobel, Martijn, additional, Placide, John, additional, Porter, Evan, additional, Preston, Kathryn, additional, Puts, Sander, additional, Pybus, Matt, additional, Rashid, Arif S., additional, Rattay, Tim, additional, Rosenstein, Barry S., additional, Sanders, Keith L., additional, Sanjay, Aneja, additional, Schwartz, Russell, additional, Sharafi, Christina Setareh, additional, Sher, David, additional, Shumway, John, additional, Siddiqui, Zaid, additional, Speers, Corey, additional, Strawderman, Robert, additional, Tao, Yifeng, additional, Thomas, Charles R., additional, Thompson, Reid F., additional, Tu, Xin Ming, additional, Tu, Justin Xiang-Yuan, additional, Vallières, Martin, additional, van Dijk, Lisanne V., additional, Ventura, Juan, additional, Wahid, Kareem A., additional, Wald, Hedy S., additional, Wang, Jing, additional, Wasserman, Jason Adam, additional, West, Catharine, additional, Yang, Kun, additional, Yap, Moi Hoon, additional, Yuan, Yading, additional, Yuan, Ye, additional, Zegers, Catharina, additional, Zhu, Lin L., additional, and Zuhour, Raed, additional

Published
2024
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5. Machine learning strategies to predict late adverse effects in childhood acute lymphoblastic leukemia survivors

Author

Raymond, Nicolas, Caru, Maxime, Laribi, Hakima, Mitiche, Mehdi, Marcil, Valérie, Krajinovic, Maja, Curnier, Daniel, Sinnett, Daniel, and Vallières, Martin

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Acute lymphoblastic leukemia is the most frequent pediatric cancer. Approximately two third of survivors develop one or more health complications known as late adverse effects following their treatments. The existing measures offered to patients during their follow-up visits to the hospital are rather standardized for all childhood cancer survivors and not necessarily personalized for childhood ALL survivors. As a result, late adverse effects may be underdiagnosed and, in most cases, only taken care of following their appearance. Thus, it is necessary to predict these treatment-related conditions earlier in order to prevent them and enhance the survivors' health. Multiple studies have investigated the development of late adverse effects prediction tools to offer better personalized follow-up methods. However, no solution integrated the usage of neural networks to date. In this work, we developed graph-based parameters-efficient neural networks and promoted their interpretability with multiple post-hoc analyses. We first proposed a new disease-specific VO$_2$ peak prediction model that does not require patients to participate to a physical function test (e.g., 6-minute walk test) and further created an obesity prediction model using clinical variables that are available from the end of childhood ALL treatment as well as genomic variables. Our solutions were able to achieve better performance than linear and tree-based models on small cohorts of patients ($\leq$ 223) for both tasks., Comment: Submitted to Communications Medicine

Published
2022

6. 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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7. Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Author

Andrearczyk, Vincent, Oreiller, Valentin, Boughdad, Sarah, Rest, Catherine Chez Le, Elhalawani, Hesham, Jreige, Mario, Prior, John O., Vallières, Martin, Visvikis, Dimitris, Hatt, Mathieu, and Depeursinge, Adrien

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

This paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. The challenge is composed of three tasks related to the automatic analysis of PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the automatic segmentation of H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is the automatic prediction of Progression Free Survival (PFS) from the same FDG-PET/CT. Finally, Task 3 is the same as Task 2 with ground truth GTVt annotations provided to the participants. The data were collected from six centers for a total of 325 images, split into 224 training and 101 testing cases. The interest in the challenge was highlighted by the important participation with 103 registered teams and 448 result submissions. The best methods obtained a Dice Similarity Coefficient (DSC) of 0.7591 in the first task, and a Concordance index (C-index) of 0.7196 and 0.6978 in Tasks 2 and 3, respectively. In all tasks, simplicity of the approach was found to be key to ensure generalization performance. The comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used. This potentially obviates the need for GTVt contouring, opening avenues for reproducible and large scale radiomics studies including thousands potential subjects.

Published
2022

8. 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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9. Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics

Author

Keek, Simon A, Beuque, Manon, Primakov, Sergey, Woodruff, Henry C, Chatterjee, Avishek, van Timmeren, Janita E, Vallières, Martin, Hendriks, Lizza EL, Kraft, Johannes, Andratschke, Nicolaus, Braunstein, Steve E, Morin, Olivier, and Lambin, Philippe

Subjects
Patient Safety, Neurosciences, Cancer, Good Health and Well Being, brain metastases, radiation necrosis, deep learning, artificial neural network, radiomics, MRI, adverse radiation effects, deep learning - artificial neural network, Oncology and Carcinogenesis
Abstract

IntroductionThere is a cumulative risk of 20-40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain. It is therefore of clinical interest to identify patients at a high risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, and patient characteristics or their combination can predict ARE risk in patients with BM before SRT.MethodsGadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (N = 1,404) and a validation cohort (N = 237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features, and patient characteristics or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per patient developing ARE were investigated.ResultsThe best-performing XGBoost model on a lesion level was trained on a combination of radiomics features and DL features (AUC of 0.71 and recall of 0.80). On a patient level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72 and recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per lesion and an AUC of 0.70 and recall of 0.60 per patient.ConclusionMachine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion levels. These models could be used in clinical decision making, informing patients on their risk of ARE and allowing physicians to opt for different therapies.

Published
2022

10. Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT

Author

Andrearczyk, Vincent, Oreiller, Valentin, Abobakr, Moamen, Akhavanallaf, Azadeh, Balermpas, Panagiotis, Boughdad, Sarah, Capriotti, Leo, Castelli, Joel, Cheze Le Rest, Catherine, Decazes, Pierre, Correia, Ricardo, El-Habashy, Dina, Elhalawani, Hesham, Fuller, Clifton D., Jreige, Mario, Khamis, Yomna, La Greca, Agustina, Mohamed, Abdallah, Naser, Mohamed, Prior, John O., Ruan, Su, Tanadini-Lang, Stephanie, Tankyevych, Olena, Salimi, Yazdan, Vallières, Martin, Vera, Pierre, Visvikis, Dimitris, Wahid, Kareem, Zaidi, Habib, Hatt, Mathieu, Depeursinge, Adrien, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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11. Standardised convolutional filtering for radiomics

Author

Depeursinge, Adrien, Andrearczyk, Vincent, Whybra, Philip, van Griethuysen, Joost, Müller, Henning, Schaer, Roger, Vallières, Martin, and Zwanenburg, Alex

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, 68U10 (Primary) 68T45 (Secondary), I.4.7, J.3
Abstract

The Image Biomarker Standardisation Initiative (IBSI) aims to improve reproducibility of radiomics studies by standardising the computational process of extracting image biomarkers (features) from images. We have previously established reference values for 169 commonly used features, created a standard radiomics image processing scheme, and developed reporting guidelines for radiomic studies. However, several aspects are not standardised. Here we present a complete version of a reference manual on the use of convolutional filters in radiomics and quantitative image analysis. Filters, such as wavelets or Laplacian of Gaussian filters, play an important part in emphasising specific image characteristics such as edges and blobs. Features derived from filter response maps were found to be poorly reproducible. This reference manual provides definitions for convolutional filters, parameters that should be reported, reference feature values, and tests to verify software compliance with the reference standard., Comment: 87 pages. For additional information see https://theibsi.github.io/

Published
2020

13. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Author

Zwanenburg, Alex, Vallières, Martin, Abdalah, Mahmoud A, Aerts, Hugo JWL, Andrearczyk, Vincent, Apte, Aditya, Ashrafinia, Saeed, Bakas, Spyridon, Beukinga, Roelof J, Boellaard, Ronald, Bogowicz, Marta, Boldrini, Luca, Buvat, Irène, Cook, Gary JR, Davatzikos, Christos, Depeursinge, Adrien, Desseroit, Marie-Charlotte, Dinapoli, Nicola, Dinh, Cuong Viet, Echegaray, Sebastian, El Naqa, Issam, Fedorov, Andriy Y, Gatta, Roberto, Gillies, Robert J, Goh, Vicky, Götz, Michael, Guckenberger, Matthias, Ha, Sung Min, Hatt, Mathieu, Isensee, Fabian, Lambin, Philippe, Leger, Stefan, Leijenaar, Ralph TH, Lenkowicz, Jacopo, Lippert, Fiona, Losnegård, Are, Maier-Hein, Klaus H, Morin, Olivier, Müller, Henning, Napel, Sandy, Nioche, Christophe, Orlhac, Fanny, Pati, Sarthak, Pfaehler, Elisabeth AG, Rahmim, Arman, Rao, Arvind UK, Scherer, Jonas, Siddique, Muhammad Musib, Sijtsema, Nanna M, Socarras Fernandez, Jairo, Spezi, Emiliano, Steenbakkers, Roel JHM, Tanadini-Lang, Stephanie, Thorwarth, Daniela, Troost, Esther GC, Upadhaya, Taman, Valentini, Vincenzo, van Dijk, Lisanne V, van Griethuysen, Joost, van Velden, Floris HP, Whybra, Philip, Richter, Christian, and Löck, Steffen

Subjects
Biomedical Imaging, Cancer, Bioengineering, Clinical Trials and Supportive Activities, Clinical Research, Good Health and Well Being, Biomarkers, Calibration, Fluorodeoxyglucose F18, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Magnetic Resonance Imaging, Phantoms, Imaging, Phenotype, Positron-Emission Tomography, Radiopharmaceuticals, Reproducibility of Results, Sarcoma, Software, Tomography, X-Ray Computed, Medical and Health Sciences, Nuclear Medicine & Medical Imaging
Abstract

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Published
2020

14. MP07-08 A FULLY AUTOMATED MULTI-TASK MACHINE LEARNING PROGNOSTIC MODEL INTEGRATING RADIOMICS AND CLINICAL DATA TO PREDICT OUTCOMES IN HIGH-GRADE PROSTATE CANCER

Author

Touma, Nawar, primary, Larose, Maxence, additional, Brodeur, Raphaël, additional, Desroches, Félix, additional, Bédard-Tremblay, Daphnée, additional, Raymond, Nicolas, additional, Leblanc, Danahé, additional, Rasekh, Fatemeh, additional, Hovington, Hélène, additional, Neveu, Bertrand, additional, Vallières, Martin, additional, Archambault, Louis, additional, and Pouliot, Frédéric, additional

Published
2024
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15. MP07-07 RADIOMICS-BASED PROGNOSTIC MODEL GUIDED BY ARTIFICIAL INTELLIGENCE FOR PREDICTING CLINICAL OUTCOMES IN INDIVIDUALS WITH HIGH-GRADE PROSTATE CANCER

Author

Touma, Nawar, primary, Larose, Maxence, additional, Brodeur, Raphaël, additional, Desroches, Félix, additional, Raymond, Nicolas, additional, Bédard-Tremblay, Daphnée, additional, Leblanc, Danahé, additional, Rasekh, Fatemeh, additional, Hovington, Hélène, additional, Neveu, Bertrand, additional, Vallières, Martin, additional, Archambault, Louis, additional, and Pouliot, Frédéric, additional

Published
2024
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16. Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival

Author

Morin, Olivier, Chen, William C, Nassiri, Farshad, Susko, Matthew, Magill, Stephen T, Vasudevan, Harish N, Wu, Ashley, Vallières, Martin, Gennatas, Efstathios D, Valdes, Gilmer, Pekmezci, Melike, Alcaide-Leon, Paula, Choudhury, Abrar, Interian, Yannet, Mortezavi, Siavash, Turgutlu, Kerem, Bush, Nancy Ann Oberheim, Solberg, Timothy D, Braunstein, Steve E, Sneed, Penny K, Perry, Arie, Zadeh, Gelareh, McDermott, Michael W, Villanueva-Meyer, Javier E, and Raleigh, David R

Subjects
Genetics, Cancer, Brain Disorders, Clinical Research, Human Genome, Rare Diseases, Brain Cancer, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, diffusion weighted imaging, magnetic resonance imaging, meningioma, quantitative imaging, radiomics
Abstract

BackgroundWe investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes.MethodsRetrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization grade I, 35% grade II, and 8% grade III) at two independent institutions, which comprised primary and external datasets. For each patient in the primary dataset, 16 radiologic and 172 radiomic features were extracted from preoperative magnetic resonance images, and prognostic features for grade, local failure (LF) or overall survival (OS) were identified using the Kaplan-Meier method, log-rank tests and recursive partitioning analysis. Regressions and random forests were used to generate and test prognostic models, which were validated using the external dataset.ResultsMultivariate analysis revealed that apparent diffusion coefficient hypointensity (HR 5.56, 95% CI 2.01-16.7, P = .002) was associated with high grade meningioma, and low sphericity was associated both with increased LF (HR 2.0, 95% CI 1.1-3.5, P = .02) and worse OS (HR 2.94, 95% CI 1.47-5.56, P = .002). Both radiologic and radiomic predictors of adverse meningioma outcomes were significantly associated with molecular markers of aggressive meningioma biology, such as somatic mutation burden, DNA methylation status, and FOXM1 expression. Integrated prognostic models combining clinical, radiologic, and radiomic features demonstrated improved accuracy for meningioma grade, LF, and OS (area under the curve 0.78, 0.75, and 0.78, respectively) compared to models based on clinical features alone.ConclusionsPreoperative radiologic and radiomic features such as apparent diffusion coefficient and sphericity can predict tumor grade, LF, and OS in patients with meningioma.

Published
2019

17. Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Author

Andrearczyk, Vincent, Oreiller, Valentin, Jreige, Mario, Vallières, Martin, Castelli, Joel, Elhalawani, Hesham, Boughdad, Sarah, Prior, John O., Depeursinge, Adrien, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, and Depeursinge, Adrien, editor

Published
2021
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19. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Author

Vallières, Martin, Kay-Rivest, Emily, Perrin, Léo Jean, Liem, Xavier, Furstoss, Christophe, Aerts, Hugo J. W. L., Khaouam, Nader, Nguyen-Tan, Phuc Felix, Wang, Chang-Shu, Sultanem, Khalil, Seuntjens, Jan, and Naqa, Issam El

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.2.1, I.2.10, I.4.7, I.4.9, J.3
Abstract

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups., Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 tables

Published
2017

20. Image biomarker standardisation initiative

Author

Zwanenburg, Alex, Leger, Stefan, Vallières, Martin, and Löck, Steffen

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, I.2.1, I.2.10, I.4.7, I.4.9, J.3, I.2.1, I.2.10, I.4.7, I.4.9, J.3
Abstract

The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of image biomarkers from acquired imaging for the purpose of high-throughput quantitative image analysis (radiomics). Lack of reproducibility and validation of high-throughput quantitative image analysis studies is considered to be a major challenge for the field. Part of this challenge lies in the scantiness of consensus-based guidelines and definitions for the process of translating acquired imaging into high-throughput image biomarkers. The IBSI therefore seeks to provide image biomarker nomenclature and definitions, benchmark data sets, and benchmark values to verify image processing and image biomarker calculations, as well as reporting guidelines, for high-throughput image analysis., Comment: Added figures 2.5, 2.6. Replaced figure 2.7. Added missing section header for the normalised dependence count non-uniformity feature. Fixed layout issues with small font sizes that appeared in the last half of the document

Published
2016
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21. Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Author

Andrearczyk, Vincent, primary, Oreiller, Valentin, additional, Boughdad, Sarah, additional, Rest, Catherine Cheze Le, additional, Elhalawani, Hesham, additional, Jreige, Mario, additional, Prior, John O., additional, Vallières, Martin, additional, Visvikis, Dimitris, additional, Hatt, Mathieu, additional, and Depeursinge, Adrien, additional

Published
2022
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22. The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights

Author

Whybra, Philip, primary, Zwanenburg, Alex, additional, Andrearczyk, Vincent, additional, Schaer, Roger, additional, Apte, Aditya P., additional, Ayotte, Alexandre, additional, Baheti, Bhakti, additional, Bakas, Spyridon, additional, Bettinelli, Andrea, additional, Boellaard, Ronald, additional, Boldrini, Luca, additional, Buvat, Irène, additional, Cook, Gary J. R., additional, Dietsche, Florian, additional, Dinapoli, Nicola, additional, Gabryś, Hubert S., additional, Goh, Vicky, additional, Guckenberger, Matthias, additional, Hatt, Mathieu, additional, Hosseinzadeh, Mahdi, additional, Iyer, Aditi, additional, Lenkowicz, Jacopo, additional, Loutfi, Mahdi A. L., additional, Löck, Steffen, additional, Marturano, Francesca, additional, Morin, Olivier, additional, Nioche, Christophe, additional, Orlhac, Fanny, additional, Pati, Sarthak, additional, Rahmim, Arman, additional, Rezaeijo, Seyed Masoud, additional, Rookyard, Christopher G., additional, Salmanpour, Mohammad R., additional, Schindele, Andreas, additional, Shiri, Isaac, additional, Spezi, Emiliano, additional, Tanadini-Lang, Stephanie, additional, Tixier, Florent, additional, Upadhaya, Taman, additional, Valentini, Vincenzo, additional, van Griethuysen, Joost J. M., additional, Yousefirizi, Fereshteh, additional, Zaidi, Habib, additional, Müller, Henning, additional, Vallières, Martin, additional, and Depeursinge, Adrien, additional

Published
2024
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24. An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication

Author

Morin, Olivier, Vallières, Martin, Braunstein, Steve, Ginart, Jorge Barrios, Upadhaya, Taman, Woodruff, Henry C., Zwanenburg, Alex, Chatterjee, Avishek, Villanueva-Meyer, Javier E., Valdes, Gilmer, Chen, William, Hong, Julian C., Yom, Sue S., Solberg, Timothy D., Löck, Steffen, Seuntjens, Jan, Park, Catherine, and Lambin, Philippe

Published
2021
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37. 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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38. Development and Validation of Multiparametric MRI–based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer

Author

Lefebvre, Thierry L., primary, Ueno, Yoshiko, additional, Dohan, Anthony, additional, Chatterjee, Avishek, additional, Vallières, Martin, additional, Winter-Reinhold, Eric, additional, Saif, Sameh, additional, Levesque, Ives R., additional, Zeng, Xing Ziggy, additional, Forghani, Reza, additional, Seuntjens, Jan, additional, Soyer, Philippe, additional, Savadjiev, Peter, additional, and Reinhold, Caroline, additional

Published
2022
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39. Developing and internally validating a predictive model of adverse radiation effects in brain tumours after stereotactic radiosurgery treatment

Author

Keek, Simon, Lambin, Philippe, wooodruff, Henry, Chatterjee, Avishek, Vallières, Martin, and Morin, Olivier

Subjects
Artificial Intelligence and Robotics, Oncology, Computer Sciences, Physical Sciences and Mathematics, Medicine and Health Sciences, Medical Specialties, Life Sciences
Abstract

Pre-registration of our study to investigate predictive modelling of adverse radiation effects in secondary brain tumor patients.

Published
2022
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40. Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics

Author

Keek, Simon A., primary, Beuque, Manon, additional, Primakov, Sergey, additional, Woodruff, Henry C., additional, Chatterjee, Avishek, additional, van Timmeren, Janita E., additional, Vallières, Martin, additional, Hendriks, Lizza E. L., additional, Kraft, Johannes, additional, Andratschke, Nicolaus, additional, Braunstein, Steve E., additional, Morin, Olivier, additional, and Lambin, Philippe, additional

Published
2022
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41. Head and neck tumor segmentation in PET/CT: The HECKTOR challenge

Author

Oreiller, Valentin, primary, Andrearczyk, Vincent, additional, Jreige, Mario, additional, Boughdad, Sarah, additional, Elhalawani, Hesham, additional, Castelli, Joel, additional, Vallières, Martin, additional, Zhu, Simeng, additional, Xie, Juanying, additional, Peng, Ying, additional, Iantsen, Andrei, additional, Hatt, Mathieu, additional, Yuan, Yading, additional, Ma, Jun, additional, Yang, Xiaoping, additional, Rao, Chinmay, additional, Pai, Suraj, additional, Ghimire, Kanchan, additional, Feng, Xue, additional, Naser, Mohamed A., additional, Fuller, Clifton D., additional, Yousefirizi, Fereshteh, additional, Rahmim, Arman, additional, Chen, Huai, additional, Wang, Lisheng, additional, Prior, John O., additional, and Depeursinge, Adrien, additional

Published
2022
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44. Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data

Author

Purkayastha, Subhanik, primary, Xiao, Yanhe, additional, Jiao, Zhicheng, additional, Thepumnoeysuk, Rujapa, additional, Halsey, Kasey, additional, Wu, Jing, additional, Tran, Thi My Linh, additional, Hsieh, Ben, additional, Choi, Ji Whae, additional, Wang, Dongcui, additional, Vallières, Martin, additional, Wang, Robin, additional, Collins, Scott, additional, Feng, Xue, additional, Feldman, Michael, additional, Zhang, Paul J., additional, Atalay, Michael, additional, Sebro, Ronnie, additional, Yang, Li, additional, Fan, Yong, additional, Liao, Wei-hua, additional, and Bai, Harrison X., additional

Published
2021
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45. Machine and deep learning methods for radiomics

Author

Avanzo, Michele, primary, Wei, Lise, additional, Stancanello, Joseph, additional, Vallières, Martin, additional, Rao, Arvind, additional, Morin, Olivier, additional, Mattonen, Sarah A., additional, and El Naqa, Issam, additional

Published
2020
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46. Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging

Author

Xi, Ianto Lin, primary, Zhao, Yijun, additional, Wang, Robin, additional, Chang, Marcello, additional, Purkayastha, Subhanik, additional, Chang, Ken, additional, Huang, Raymond Y., additional, Silva, Alvin C., additional, Vallières, Martin, additional, Habibollahi, Peiman, additional, Fan, Yong, additional, Zou, Beiji, additional, Gade, Terence P., additional, Zhang, Paul J., additional, Soulen, Michael C., additional, Zhang, Zishu, additional, Bai, Harrison X., additional, and Stavropoulos, S. William, additional

Published
2020
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47. Deep Learning Based on MRI for Differentiation of Low‐ and High‐Grade in Low‐Stage Renal Cell Carcinoma

Author

Zhao, Yijun, primary, Chang, Marcello, additional, Wang, Robin, additional, Xi, Ianto Lin, additional, Chang, Ken, additional, Huang, Raymond Y, additional, Vallières, Martin, additional, Habibollahi, Peiman, additional, Dagli, Mandeep S., additional, Palmer, Matthew, additional, Zhang, Paul J., additional, Silva, Alvin C., additional, Yang, Li, additional, Soulen, Michael C., additional, Zhang, Zishu, additional, Bai, Harrison X., additional, and Stavropoulos, S. William, additional

Published
2020
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48. External Validation of an MRI-Derived Radiomics Model to Predict Biochemical Recurrence after Surgery for High-Risk Prostate Cancer

Author

Bourbonne, Vincent, primary, Fournier, Georges, additional, Vallières, Martin, additional, Lucia, François, additional, Doucet, Laurent, additional, Tissot, Valentin, additional, Cuvelier, Gilles, additional, Hue, Stephane, additional, Le Penn Du, Henri, additional, Perdriel, Luc, additional, Bertrand, Nicolas, additional, Staroz, Frederic, additional, Visvikis, Dimitris, additional, Pradier, Olivier, additional, Hatt, Mathieu, additional, and Schick, Ulrike, additional

Published
2020
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50. External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy

Author

Lucia, François, primary, Visvikis, Dimitris, additional, Vallières, Martin, additional, Desseroit, Marie-Charlotte, additional, Miranda, Omar, additional, Robin, Philippe, additional, Bonaffini, Pietro Andrea, additional, Alfieri, Joanne, additional, Masson, Ingrid, additional, Mervoyer, Augustin, additional, Reinhold, Caroline, additional, Pradier, Olivier, additional, Hatt, Mathieu, additional, and Schick, Ulrike, additional

Published
2018
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