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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. 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

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. 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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6. 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

7. 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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8. 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

9. 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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10. 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

12. 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

13. 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

15. 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

16. 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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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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18. 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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19. 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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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. 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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25. 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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26. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

Author

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

Published
2024
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36. Classification des tumeurs rénales à l'aide d'imagerie par résonance magnétique dans un contexte d'apprentissage multitâches

Author

Ayotte, Alexandre, Carrier-Vallières, Martin, Ayotte, Alexandre, and Carrier-Vallières, Martin

Abstract

Pour les patients présentant une lésion au rein, la connaissance de la malignité, ainsi que du sous-type et du grade (dans le cas où la tumeur serait maligne), sont des éléments essentiels pour le pronostic. Actuellement, la procédure standard pour obtenir ces informations est de procéder à une biopsie de la lésion. La biopsie étant très invasive et manquant de fiabilité pour les tumeurs hétérogènes, l'analyse d'imagerie non invasive, telle que l'imagerie par résonnance magnétique (IRM), à l'aide d'intelligence artificielle est une alternative qui a suscité beaucoup d'intérêt au cours des dernières années. Bien que les récentes études montrent que les réseaux de neurones à convolution soient très prometteurs lorsqu'il s’agit de classifier les tumeurs rénales par rapport à leur malignité, leur sous-type ou leur grade, les résultats ne sont pas encore suffisamment convaincants pour constituer une solution de rechange à la biopsie. Dans ce mémoire, nous vérifierons s'il est possible, à l'aide de l'apprentissage multitâche, d'améliorer les performances d'un modèle d'apprentissage profond pour la classification de la malignité, du sous-type et du grade des tumeurs rénales. Nous utiliserons un jeu de données comportant les images par résonance magnétique de pondération T1 avec contraste amélioré et T2 de 1082 patients qui présente une lésion à l'un des reins, la segmentation de cette lésion pour chaque modalité et un ensemble de données cliniques. Nous commencerons par vérifier la pertinence d'un modèle d'apprentissage à tâche unique utilisant l'imagerie par rapport à un modèle se basant sur les données cliniques. Par la suite, nous allons comparer les réseaux de neurones à convolution entraînés sur les tâches individuellement à des modèles entraînés à prédire les 3 caractéristiques simultanément. Pour finir, nous déterminerons s'il est bénéfique de forcer un modèle à simultanément classifier la tumeur et prédire un ensemble de caractéristiques radiomiques qui joueront le

Published
2023

38. 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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39. 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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40. 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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41. 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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42. Identification de fusibles électriques de puissance par apprentissage profond

Author

Bouffard, François, Giard-Leroux, Simon, Vallières, Martin, Bouffard, François, Giard-Leroux, Simon, and Vallières, Martin

Abstract

Dans l’industrie de l’énergie électrique, des études d’arcs électriques sont effectuées par des ingénieurs et ingénieures afin d’évaluer l’énergie incidente à laquelle une personne qui travaille sur un équipement serait exposée si un accident provoquait un court-circuit. Cette énergie peut varier selon plusieurs paramètres, dont l’un des principaux est la durée de l’arc, qui dépend de la rapidité de coupure de l’équipement de protection qui alimente l’équipement où le défaut survient. Il est ainsi important d’identifier correctement les équipements de protection dans un réseau électrique, dont les types de fusibles, qui peuvent être difficiles à identifier à l’oeil nu à partir de photos d’installations électriques. Par contre, ces derniers peuvent être identifiés grâce à leurs caractéristiques physiques, telles que leur couleur ou leur forme. Cette tâche d’observation doit présentement être effectuée manuellement, ainsi une solution plus automatisée serait avantageuse. En parallèle, le domaine de la détection d’objets utilisant l’apprentissage profond a connu un essor remarquable dans les dernières années afin de permettre de localiser et d’identifier le type de différents objets dans des images. En appliquant une stratégie d’apprentissage supervisé, il est possible d’entraîner et d’optimiser un modèle de détection d’objets basé sur les réseaux de neurones pouvant identifier les fusibles dans de nouvelles images jamais vues auparavant. Dans ce mémoire, nous aborderons l’utilisation de techniques de détection d’objets basées sur l’apprentissage profond afin d’automatiser l’identification de fusibles électriques, incluant les modèles Faster R-CNN, RetinaNet et DETR. Nous poserons le problème d’identification de fusibles dans le contexte des études d’arcs électriques, décrirons les principes derrière les techniques de détection d’objets utilisées en apprentissage profond et proposerons une méthodologie afin d’optimiser un modèle de détection final pouvant être utilisé

Published
2022

43. Stratégies d'apprentissage automatique pour la prédiction d'effets tardifs associés au traitement de la leucémie aiguë lymphoblastique infantile

Author

Raymond, Nicolas, Carrier-Vallières, Martin, Raymond, Nicolas, and Carrier-Vallières, Martin

Abstract

La leucémie aiguë lymphoblastique (LAL) est le cancer le plus fréquemment diagnostiqué chez l'enfant. Approximativement deux tiers des survivants de la LAL infantile présentent une ou plusieurs complications de santé à l'âge adulte. Connues sous le nom d'effets tardifs, ces complications sont plutôt le fruit du traitement que de la maladie elle-même. Les mesures actuellement mises en place pour les visites de suivi post-traitement sont généralement uniformes à l'ensemble des survivants de cancers infantiles et ne sont pas nécessairement adaptées précisément aux survivants de la LAL infantile. Conséquemment, les effets tardifs peuvent être sous-diagnostiqués et, dans la plupart des cas, seulement pris en charge après leurs apparitions. D'autre part, les directives de soins actuelles peuvent également mener à un suivi plus intensif que nécessaire, entraînant parfois des inquiétudes chez les survivants en plus d'augmenter les coûts de soins. Ainsi, il est nécessaire de prédire l'apparition des effets tardifs plus tôt pour contribuer à la santé et au bien-être des survivants. Plusieurs travaux se sont concentrés sur la recherche de biomarqueurs pouvant aider à la prédiction des effets tardifs et, notamment, un article a mis de l'avant l'utilisation d'un modèle d'apprentissage automatique pour prévenir les effets liés à la détérioration de la forme cardio-respiratoire. Toutefois, aucune solution n'a fait usage de réseaux neuronaux jusqu'à présent. Dans ce projet de recherche, nous avons développé des réseaux de neurones graphiques efficients et mis en valeur leur interprétabilité à l'aide de multiples analyses conduites suite à leurs entraînements. En premier lieu, nous avons proposé un nouveau modèle d'estimation de la consommation d'oxygène maximale (c.-à-d., VO2 max) qui ne nécessite aucune participation à un test physique (e.g., test de marche de six minutes). Le VO2 max est reconnu comme la meilleure mesure de la forme cardio-respiratoire, qui à son tour, est un bon

Published
2022

44. Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer

Author

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

Abstract

Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for treatment planning. Radiomics analysis at preoperative MRI holds potential to identify high-risk phenotypes. Purpose To evaluate the performance of multiparametric MRI three-dimensional radiomics-based machine learning models for differentiating low- from high-risk histopathologic markers-deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and high-grade status-and advanced-stage endometrial carcinoma. Materials and Methods This dual-center retrospective study included women with histologically proven endometrial carcinoma who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. Exclusion criteria were tumor diameter less than 1 cm, missing MRI sequences or histopathology reports, neoadjuvant therapy, and malignant neoplasms other than endometrial carcinoma. Three-dimensional radiomics features were extracted after tumor segmentation at MRI (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI). Predictive features were selected in the training set with use of random forest (RF) models for each end point, and trained RF models were applied to the external test set. Five board-certified radiologists conducted MRI-based staging and deep MI assessment in the training set. Areas under the receiver operating characteristic curve (AUCs) were reported with balanced accuracies, and radiologists' readings were compared with radiomics with use of McNemar tests. Results In total, 157 women were included: 94 at the first institution (training set; mean age, 66 years ± 11 [SD]) and 63 at the second institution (test set; 67 years ± 12). RF models dichotomizing deep MI, LVSI, high grade, and International Federation of Gynecology and Obstetrics (FIGO) stage led to AUCs of 0.81 (95% CI: 0.68, 0.88), 0.80 (95% CI: 0.67, 0.93), 0.74 (95% CI: 0.61, 0.86), and 0.84 (95% CI: 0.72, 0.92), respectively, in the test set. In the traini

Published
2022

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

Author

Zwanenburg, Alex; https://orcid.org/0000-0002-0342-9545, Vallières, Martin; https://orcid.org/0000-0001-7639-8172, Abdalah, Mahmoud A; https://orcid.org/0000-0002-0573-4121, Aerts, Hugo J W L; https://orcid.org/0000-0002-2122-2003, Andrearczyk, Vincent, Apte, Aditya; https://orcid.org/0000-0001-8120-0669, Ashrafinia, Saeed, Bakas, Spyridon; https://orcid.org/0000-0001-8734-6482, Beukinga, Roelof J; https://orcid.org/0000-0003-3500-8523, Boellaard, Ronald; https://orcid.org/0000-0002-0313-5686, Bogowicz, Marta; https://orcid.org/0000-0002-4747-5375, Boldrini, Luca; https://orcid.org/0000-0002-5631-1575, Buvat, Irène; https://orcid.org/0000-0002-7053-6471, Cook, Gary J R; https://orcid.org/0000-0002-8732-8134, Davatzikos, Christos; https://orcid.org/0000-0002-1025-8561, Depeursinge, Adrien; https://orcid.org/0000-0002-2362-0304, Desseroit, Marie-Charlotte, Dinapoli, Nicola, Dinh, Cuong Viet, Echegaray, Sebastian; https://orcid.org/0000-0001-9150-4412, El Naqa, Issam; https://orcid.org/0000-0001-6023-1132, Fedorov, Andriy Y; https://orcid.org/0000-0003-4806-9413, Gatta, Roberto; https://orcid.org/0000-0002-4716-9925, Gillies, Robert J; https://orcid.org/0000-0002-8888-7747, Goh, Vicky; https://orcid.org/0000-0002-2321-8091, Götz, Michael; https://orcid.org/0000-0003-0984-224X, Guckenberger, Matthias; https://orcid.org/0000-0002-7146-9071, Ha, Sung Min; https://orcid.org/0000-0002-3526-9101, Hatt, Mathieu, Isensee, Fabian; https://orcid.org/0000-0002-3519-5886, Tanadini-Lang, Stephanie; https://orcid.org/0000-0002-4387-1522, Zwanenburg, Alex; https://orcid.org/0000-0002-0342-9545, Vallières, Martin; https://orcid.org/0000-0001-7639-8172, Abdalah, Mahmoud A; https://orcid.org/0000-0002-0573-4121, Aerts, Hugo J W L; https://orcid.org/0000-0002-2122-2003, Andrearczyk, Vincent, Apte, Aditya; https://orcid.org/0000-0001-8120-0669, Ashrafinia, Saeed, Bakas, Spyridon; https://orcid.org/0000-0001-8734-6482, Beukinga, Roelof J; https://orcid.org/0000-0003-3500-8523, Boellaard, Ronald; https://orcid.org/0000-0002-0313-5686, Bogowicz, Marta; https://orcid.org/0000-0002-4747-5375, Boldrini, Luca; https://orcid.org/0000-0002-5631-1575, Buvat, Irène; https://orcid.org/0000-0002-7053-6471, Cook, Gary J R; https://orcid.org/0000-0002-8732-8134, Davatzikos, Christos; https://orcid.org/0000-0002-1025-8561, Depeursinge, Adrien; https://orcid.org/0000-0002-2362-0304, Desseroit, Marie-Charlotte, Dinapoli, Nicola, Dinh, Cuong Viet, Echegaray, Sebastian; https://orcid.org/0000-0001-9150-4412, El Naqa, Issam; https://orcid.org/0000-0001-6023-1132, Fedorov, Andriy Y; https://orcid.org/0000-0003-4806-9413, Gatta, Roberto; https://orcid.org/0000-0002-4716-9925, Gillies, Robert J; https://orcid.org/0000-0002-8888-7747, Goh, Vicky; https://orcid.org/0000-0002-2321-8091, Götz, Michael; https://orcid.org/0000-0003-0984-224X, Guckenberger, Matthias; https://orcid.org/0000-0002-7146-9071, Ha, Sung Min; https://orcid.org/0000-0002-3526-9101, Hatt, Mathieu, Isensee, Fabian; https://orcid.org/0000-0002-3519-5886, and Tanadini-Lang, Stephanie; https://orcid.org/0000-0002-4387-1522

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 verifica

Published
2020

49. 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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50. 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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