Radiation and Cancer Physics

SS 01 - Physics 1 -Best of Physics

12 - Modeling of Locoregional Control in Hepatocellular Carcinoma After Stereotactic Body Radiation Therapy by Integrating Clinical and Immune Cell Profiles

Sunday, October 21
2:15 PM - 2:25 PM
Location: Room 214 A/B

Modeling of Locoregional Control in Hepatocellular Carcinoma After Stereotactic Body Radiation Therapy by Integrating Clinical and Immune Cell Profiles
I. El Naqa, D. Owen, K. C. Cuneo, C. Mayo, T. S. Lawrence, and R. K. Ten Haken; Department of Radiation Oncology, University of Michigan, Ann Arbor, MI

Purpose/Objective(s): Locoregional control (LRC) is associated with long-term outcomes in patients with hepatocellular carcinoma (HCC) who receive liver SBRT. The purpose of the current study is to develop new models for predicting LRC and to evaluate the role of clinical factors and circulating immune cells in predicting post-SBRT response.

Materials/Methods: Data from 146 HCC patients who received SBRT from 2005-14 were analyzed retrospectively. Tumor doses (median prescribed = 49.8 Gy, delivered in 3 or 5 fractions) were bio-corrected to 2 Gy equivalents (EQD2) using the LQ-L model. Circulating immune cell (lymphocytes, neutrophils, platelets) profiles (ICPs), red blood cell counts, and their changes during and after treatment were retrieved from the patients' laboratory records. The locoregional failure rate was 54.7% with a median follow-up and time-to-failure of 11 months and 6 months, respectively. Actuarial models based on machine learning algorithms were developed for predicting LRC. These models were based on variable shrinkage analysis with Cox proportional hazard (Lasso-Cox) and ensemble methods with Random survival forests (RSF). Relative variable importance was assessed in Lasso-Cox by hazard ratios (HR) and in RSF by the Gini impurity index. To avoid overfitting pitfalls, the bootstrap 0.632+ resampling method was used to validate prediction, and performance was measured using the concordance statistic (c-index).

Results: When modeling with only clinical variables for LRC, the Lasso-Cox selected in descending HR significance order: previous recurrences, age, Child Pugh, ALBI, and cirrhosis as relevant factors, achieving an average bootstrap c-index = 0.538 (95% CI: 0.496-0.564). The RSF achieved an average bootstrap c-index = 0.687 (95% CI: 0.666-0.704) with previous recurrences, Child Pugh, age, ALBI, gender, tumor mean dose, tumor volume as the top important factors according to their Gini impurity index. The addition of ICPs, available in about half the population, yielded an average bootstrap c-index = 0.569 (95% CI: 0.496-0.67) using Lasso-Cox with pre-treatment hematocrits, ALBI, and changes in lymphocyte and neutrophil counts as statistically significant variables according to their HRs (p<0.05). Whereas the RSF, with immune cells included, achieved the best performance overall with an average bootstrap validated c-index = 0.696 (95% CI: 0.638-0.751). Changes in lymphocytes and platelets counts, pre-treatment hematocrits, neutrophil counts, ALBI, and age were the most important factors according to the Gini impurity index ranking.

Conclusion: Machine learning methods based on RSF can provide a robust framework for estimating locoregional failure risk in HCC patients post-SBRT. The predictive power improves by including immune cells profile and their changes during treatment. These new LRC models can be used to personalize and guide new regimens for combining local (SBRT) and systemic (chemo- and/or immuno-) therapy in HCC patients.

Author Disclosure: I. El Naqa: None. D. Owen: None. K.C. Cuneo: Service Chief; Ann Arbor Veterans Hospital. C. Mayo: Research Grant; Varian Medical Systems. Chair TG-263 Radiation Oncology Nomenclature; AAPM. T.S. Lawrence: royalties; Lippincott, Williams and Wilkins. Honoraria; Massachusetts General Hospital, Pfizer Oncology Innovation Summit, Sidney Kimmel Foundation for Cancer Research. Consultant; Pfizer Oncology Innovation Summit. Advisory Board; ASTRO Radiation Oncology Institute, Dana Farber Cancer Institute, Massachusetts General Hospital, Sidney Kimmel Compreh Cancer Ctr at Johns Hopkins, Sidney Kimmel Foundation for Cancer Research, St. Jude Children's Research Hospital, University of Wisconsin Comprehensive Cancer Ctr. Travel Expenses; AACR, ASTRO Radiation Oncology Institute, Dana Farber Cancer Institute, Lippincott, Williams and Wilkins, Massachusetts General Hospital, Pfizer Oncology Innovation Summit, RSNA, Sidney Kimmel Compreh Cancer Ctr at Johns Hopkins, Sidney Kimmel Foundation for Cancer Research, St. Jude Children's Research Hospital, University of Wisconsin Comprehensive Cancer Ctr. Patent/License Fees/Copyright; Pi Squared Therapeutics. Editor, Cancer Discovery; AACR. Member, Editorial Advisory Board, Cancer Today; AACR. Senior Editor, Cancer Research; AACR. Member, External Advisory Board for Lung SPORE; Dana Farber Cancer Institute. Co-Editor of Principles and Practices of Oncology; Lippincott, Williams and Wilkins. Member, NCI Board of Scientific Advisors; NCI - BSA. President; ROI. Member, External Advisory Board for the Cancer Ctr; Sidney Kimmel CCC at Johns Hopkins University. Member of the Medical Advisory Board; Sidney Kimmel Foundation for Cancer Research. Vice-Chair, St. Jude Scientific Advisory Board; St. Jude Children's Research Hospital. Member, V Foundation Scientific Advisory Board; V Foundation for Cancer Research. R.K. Ten Haken: Research Grant; NIH-NCI. Honoraria; University of Copenhagen. Travel Expenses; Varian Medical Systems Inc, University of Copenhagen.

Issam El Naqa, PhD, MS

University of Michigan

Disclosure:
Employment
University of Michigan: Employee

Leadership
Endectra, LLC: Advisory board

Biography:
Issam El Naqa, MA,PhD, DABR. Associate Professor of Radiation Oncology, University of Michigan, Ann Arbor.
He received his B.Sc. (1992) and M.Sc. (1995) in Electrical and Communication Engineering from the University of Jordan. He worked as a software engineer at the Computer Engineering Bureau, Jordan, 1995-1996. He was awarded a DAAD scholarship to Germany, where he was a visiting scholar at the RWTH Aachen, 1996-1998. He completed his Ph.D. (2002) in Electrical and Computer Engineering from Illinois Institute of Technology, Chicago, IL, USA, receiving highest academic distinction award. He completed an M.A. (2007) in Biology Science from Washington University in St. Louis, St. Louis, MO, USA, where he was pursuing a post-doctoral fellowship in medical physics and was subsequently hired as a Instructor (2005-2007) and then an Assistant Professor (2007-2010) at the departments of radiation oncology and the division of biomedical and biological sciences. He became an Associate Professor at McGill University Health Centre/Medical Physics Unit (2010-2015) and associate member of at the departments of Physics, Biomedical Engineering, and Experimental medicine, where he was a designated FRSQ and CIHR scholar. He is currently an Associate Professor of Radiation Oncology at the University of Michigan at Ann Arbor and associate member in Applied Physics. He is a certified Medical Physicist by the American Board of Radiology. He is a recognized expert in the fields of medical image analysis, bioinformatics, computational radiobiology, and treatment outcomes modeling and has published extensively in these areas with more than 130 peer-reviewed journal publications and 3 edited textbooks. He has been an acting member of several academic and professional societies. His research has been funded by several federal and private grants and serves as a peer-reviewer and editorial board member for several leading international journals in his areas of expertise.

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