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1. Federated learning enables big data for rare cancer boundary detection

Author

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

Subjects
Science
Abstract

Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here, the authors present the largest FL study to-date to generate an automatic tumor boundary detector for glioblastoma.

Published
2022
Full Text
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2. Author Correction: Federated learning enables big data for rare cancer boundary detection

Author

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

Subjects
Science
Published
2023
Full Text
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3. Imaging of the head and neck during the COVID19 pandemic

Author

Joaquim, Farinhas and Jimmy S, Lee

Subjects
Otorhinolaryngology, Surgery
Abstract

There is a wide spectrum of clinical manifestation of COVID-19 in the head and neck, but often these do not have an imaging correlate. This review will highlight the most common imaging features of COVID-19 in the head and neck that can be seen on routine head and neck CT and MRI. In addition, situations where a more dedicated imaging protocol is required will be highlighted. Finally, as mass vaccination efforts are underway worldwide, post vaccination imaging can often complicate cancer surveillance imaging. Post vaccination imaging features and recommendations will be discussed.

Published
2022

4. Feasibility of an Evolutionary Tumor Board for Generating Novel Personalized Therapeutic Strategies

Author

Mark Robertson-Tessi, Joel S. Brown, Maria I. Poole, Matthew Johnson, Andriy Marusyk, Jill A. Gallaher, Kimberly A. Luddy, Christopher J. Whelan, Jeffrey West, Maximillian Strobl, Virginia Turati, Heiko Enderling, Michael J. Schell, AikChoon Tan, Terry Boyle, Rikesh Makanji, Joaquim Farinhas, Hatem Soliman, Dawn Lemanne, Robert A. Gatenby, Damon R. Reed, Alexander R. A. Anderson, and Christine H. Chung

Abstract

The current paradigm of clinical trials treating patients until disease progression using maximum tolerated dose does not account for the dynamic tumor-host-drug interactions that result in acquired resistance. Here, we present the concept of an Evolutionary Tumor Board (ETB) and report interim results from a prospective, non-interventional pilot study in which novel therapeutic strategies based on evolutionary principles were developed under the ETB framework. The ETB approach relies on an interdisciplinary team that integrates clinical, preclinical, and theoretical knowledge and the application of mathematical modeling to predict patient responses to different therapies, including novel approaches derived from eco-evolutionary first principles. We have previously proposed several evolutionary therapies that aim to enhance the efficacy of an overall treatment regimen, using existing agents for a given disease. Key among these evolutionary therapies is the idea of “first-strike second-strike”, where different agents are administered in sequence, and new strikes are applied as soon as the efficacy of the previous strike is nearing a minimum, as opposed to waiting until progression is identified on periodic imaging. This approach requires careful analysis of longitudinal patient data coupled with predictive dynamics generated by mathematical models. Here we describe the ETB process and the interim results from 15 patients enrolled in the feasibility trial. In addition, we describe the challenges faced as well as the solutions that can be implemented via improved modeling approaches, better patient data collection, and a reassessment of how we understand tumor dynamics in the light of evolutionary principles.

Published
2023

5. Abstract PR010: Evolutionary Tumor Board: Implementing dynamic personalized therapy using evolutionary theory and mathematical modeling for clinical decision support

Author

Mark Robertson-Tessi, Joel Brown, Maria Poole, Kimberly Luddy, Andriy Marusyk, Jill Gallaher, Jeffrey West, Matthew Johnson, Heiko Enderling, Rikesh Makanji, Joaquim Farinhas, Robert Gatenby, Damon Reed, Christine Chung, and Alexander Anderson

Subjects
Cancer Research, Oncology
Abstract

The era of big data in oncology has led to the promise of precision medicine for individual patients. However, many therapy decisions continue to be made based on “one size fits most” approaches, primarily since there exist few theoretical and practical tools to deal with a patient’s data over time. In parallel with this growing interest in personalized medicine, cancer is being increasingly recognized as an eco-evolutionary system that adapts to treatments, suggesting that static therapy regimens are often doomed to eventual failure. Here, we present preliminary results from a novel pilot clinical trial (NCT04343365), the Evolutionary Tumor Board (ETB), which uses eco-evolutionary theory (based on experiments and modeling) to assist with clinical decision making for each patient. We developed an informational and computational framework for applying evolutionary therapy approaches to individual patients in a dynamic fashion, using their clinical data in real time. The framework relies on detailed data curation and imaging measurements for each patient, as well as a mathematical modeling approach that accounts for multi-lesion tumor growth, treatment-induced death, and the evolution of resistance. The models are calibrated by historical datasets of similar patients, as well as the patient’s own temporal data. We use a “Phase i trial” approach to account for prediction uncertainty and provide decision support for therapy options available to the patient at any given time point. Crucially, this is presented in a way that harmonizes with the treating oncologist’s intuition. Fifteen patients at Moffitt have been enrolled into the ETB, many of whom have proceeded through the entire process, including follow-up analysis. The ETB generated outcome predictions and therapy recommendations for each case, and subsequent follow-up predictions and recommendations. Our current results demonstrate that the ETB approach has provided both novel and useful decision support for the clinicians. At the same time, numerous opportunities for further research and development have been identified. Our efforts show that there are both challenges and opportunities in the area of personalized therapy, particularly in the context of real-time clinical care. Early results from the ETB show great promise for improving patient outcomes in cancer using mathematical modeling and evolutionary therapy. Citation Format: Mark Robertson-Tessi, Joel Brown, Maria Poole, Kimberly Luddy, Andriy Marusyk, Jill Gallaher, Jeffrey West, Matthew Johnson, Heiko Enderling, Rikesh Makanji, Joaquim Farinhas, Robert Gatenby, Damon Reed, Christine Chung, Alexander Anderson. Evolutionary Tumor Board: Implementing dynamic personalized therapy using evolutionary theory and mathematical modeling for clinical decision support [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr PR010.

Published
2022

6. Abstract B006: Evolutionary Tumor Board: Implementing dynamic personalized therapy using evolutionary theory and mathematical modeling for clinical decision support

Author

Mark Robertson-Tessi, Joel Brown, Maria Poole, Kimberly Luddy, Andriy Marusyk, Jill Gallaher, Jeffrey West, Matthew Johnson, Heiko Enderling, Rikesh Makanji, Joaquim Farinhas, Robert Gatenby, Damon Reed, Christine Chung, and Alexander Anderson

Subjects
Cancer Research, Oncology
Abstract

This abstract is being presented as a short talk in the scientific program. A full abstract is available in the Proffered Abstracts section (PR010) of the Conference Proceedings. Citation Format: Mark Robertson-Tessi, Joel Brown, Maria Poole, Kimberly Luddy, Andriy Marusyk, Jill Gallaher, Jeffrey West, Matthew Johnson, Heiko Enderling, Rikesh Makanji, Joaquim Farinhas, Robert Gatenby, Damon Reed, Christine Chung, Alexander Anderson. Evolutionary Tumor Board: Implementing dynamic personalized therapy using evolutionary theory and mathematical modeling for clinical decision support [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B006.

Published
2022

7. Early response dynamics predict treatment failure in patients with recurrent and/or metastatic head and neck squamous cell carcinoma treated with cetuximab and nivolumab

Author

Daniel J, Glazar, Matthew, Johnson, Joaquim, Farinhas, Conor E, Steuer, Nabil F, Saba, Marcelo, Bonomi, Christine H, Chung, and Heiko, Enderling

Subjects
Cancer Research, Nivolumab, Oncology, Head and Neck Neoplasms, Squamous Cell Carcinoma of Head and Neck, Antineoplastic Combined Chemotherapy Protocols, Cetuximab, Humans, Treatment Failure, Neoplasm Recurrence, Local, Oral Surgery
Abstract

Recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) is currently an incurable disease. To improve treatment strategies, combinations of cetuximab plus nivolumab or pembrolizumab were evaluated for efficacy and safety for incurable R/M HNSCC. While some patients had a significant clinical benefit with complete or partial response, most patients had stable or progressive disease (PD). To identify patients with a high likelihood of treatment failure and prevent futile treatments, we developed a mathematical model of early response dynamics as an early biomarker of treatment failure.Demographics, RECIST assessment, and outcome were obtained from patients who were treated with combination of cetuximab and nivolumab on a previously published phase I/II clinical trial. We trained a tumor growth inhibition (TGI) ordinary differential equation (ODE) model describing patient-specific pre-treatment growth rate and uniform initial treatment sensitivity and rate of evolution of resistance. In a leave-one-out approach, we forecasted tumor burden and predicted time to progression (TTP) and PD.The TGI model accurately represented tumor burden dynamics (RThe TGI model can identify patients with high likelihood of PD based on early dynamics. Further studies including prospective validation are warranted.

Published
2022

8. Resident and Fellow Section

Author

Clifford J Eskey, Joaquim Farinhas, Jeffrey Allen Cohen, Mehdi Kazmi, and Brian M. Grosberg

Subjects
Central nervous system disease, medicine.medical_specialty, Text mining, Neurology, business.industry, Anesthesia, medicine, Neurology (clinical), Neurological disorder, Intensive care medicine, medicine.disease, business
Published
2007

9. Teaching case: inflammatory headache

Author

Brian M, Grosberg, Joaquim, Farinhas, Mehdi, Kazmi, Jeffrey Allen, Cohen, and Clifford J, Eskey

Subjects
Adult, Migraine without Aura, Sarcoidosis, Pulmonary, Humans, Internship and Residency, Education, Medical, Continuing, Female, Gadolinium, Fellowships and Scholarships, Magnetic Resonance Imaging
Published
2007

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