Treatment Response Assessment in Oncology: Clinical Challenges and Technical Advances

Overview

Our MICCAI 2020 tutorial is motivated by the need for developing radiomic and image analytics tools for post-treatment response assessment in oncology. While significant strides have recently been made in the development of radiomics tools through multiple open-source efforts (pyRadiomics, CapTk, CERR), these have been primarily seen application in improved disease characterization on diagnostic imaging. However, nearly 80-90% of over 1.6 million patients diagnosed with cancer annually in the U.S have to be re-evaluated following neoadjuvant or adjuvant chemo-, radiation, or combination therapies, to identify those with residual or progressive disease (i.e. non-responders) compared to those with stable or regressing disease (i.e. responders). Unfortunately, benign “tumor-mimicking” treatment changes (i.e. pseudo-progression, fibrosis, radiation necrosis) confound the appearance of residual disease on routine imaging. There is hence an increasing awareness of the need for specialized quantitative tools to reliably assess post-treatment changes, preferably using routine imaging to distinguish non-responders from responders. This goal of this tutorial is to provide a comprehensive overview of both the clinical as well as the technical challenges involved in tumor response assessment in oncology. Our tutorial will showcase a range of didactic talks presented by leading clinical experts across radiology, radiation oncology, and medical physics as well as technical experts in image modeling and machine learning approaches.

Learning Objectives

• New technical advances in developing radiomic approaches and deep learning strategies for predicting response to traditional and experimental oncology therapies, as well as post-treatment response assessment towards distinguishing treatment changes from recurrent disease.
• Computational modeling, medical physics, and mathematical oncology approaches to characterizing chemo-resistant and radio-resistant tumors on imaging.
• Oncological perspective on the need for AI and machine learning in solving challenging problems with patient stratification for clinical trials as well as prognosis and prediction of disease using imaging.
• Radiologist’s perspective on burning questions in response to assessment in oncology and the role of new machine learning and AI techniques in serving as decision support to improve radiology reads. Additionally, extant clinical trial datasets will also be discussed to encourage the MICCAI community to develop new and advanced tools for improved response assessment in oncology.
• Radiation oncology perspective on the challenges in radiation therapy treatment planning and the need for AI tools that integrate with the radiation planning suites for accurate treatment dose planning.

Organizing Committee

Pallavi Tiwari, Case Western Reserve University
Satish E. Viswanath, Case Western Reserve University
Anant Madabhushi, Case Western Reserve University
Despina Kontos, University of Pennsylvania
Christos Davatzikos, University of Pennsylvania
Jennifer Yu, Cleveland Clinic
Joseph Deasy, Memorial Sloan Kettering Cancer Center
Jun Xu, Nanjing University of Information Science & Technology, China
Thomas Yankeelov, University of Texas at Austin

Live Event: Sun, Oct 4 2020 | 8:30 AM EDT (1230 UTC)

Our panel will discuss the interplay and challenges faced in treatment response assessment will examine perspectives from radiology, medical physics, image analytics/radiomics, and mathematical modeling. Panelists will be:

• Despina Kontos, University of Pennsylvania
• Joseph Deasy, Memorial Sloan Kettering Cancer Center
• George Biros, University of Texas at Austin
• Mark Rosen, University of Pennsylvania
• Carlos Castaneda, Instituto Nacional de enfermedades Neoplàsicas, Peru
• Moderators: Pallavi Tiwari, Satish E. Viswanath

Pre-recorded Talks (available through the MICCAI Pathable interface)

Despina Kontos, PhD
Cancer Imaging and Treatment Response Assessment: Radiomics, Radiogenomics, adn the Role of AI (30m)
Associate Professor of Radiology, and director of the Computational Breast Imaging Group (CBIG) in the Center for Biomedical Image Computing and Analytics (CBICA) at the Radiology Department of the University of Pennsylvania.

Carlos Castaneda, MD
Challenges in Anti-Cancer Treatment Response Assessment (30 m)
Executive Director of the Research Department and a Staff Member in the Department of Medical Oncology in Instituto Nacional de Enfermedades Neoplàsicas, as well as a Professor of Pharmacology and Toxicology at San Marcos National University. He has over 14 years of experience as an oncologist in Lima, Peru.

Jennifer Yu, MD, PhD
The Promise of Artificial Intelligence in Radiation Oncology (25m)
Physician-scientist at the Cleveland Clinic with dual appointments in the Department of Radiation Oncology and Department of Stem Cell Biology and Regenerative Medicine.

Joseph Deasy, PhD
Imaging for treatment assessment in radiation oncology: what variables are likely to carry biological meaning? (40m)
Chair of the Department of Medical Physics, and holder of the Enid A. Haupt Endowed Chair in Medical Physics, at Memorial Sloan Kettering Cancer Center, New York.

Mark Rosen, MD, PhD
Tumor Response Assessment: A Radiologist's Perspective (20m)
Division Chief of Abdominal Imaging at the Hospital of the University of Pennsylvania. He is currently on of the project leaders for ECOG-ACRIN co-operative group with specific focus on advancing quantitative imaging in clinical trials, with over 20 years of radiology experience.

George Biros, PhD
SIBIA: Scalable Integrated Biophysically-driven Image Analysis (30 m)
Professor of Mechanical Engineering and holder of the W. A. "Tex" Moncrief, Jr. Simulation-Based Engineering Science Chair #2 and leads the Parallel Algorithms for Data Analysis and Simulation Group in the Institute of Computational Engineering and Sciences.

Contact

Please contact us at radxtools@case.edu with comments, suggestions, or concerns.