Integrating multi-plane and multi-region radiomic features to predict pathologic response to neoadjuvant chemoradiation in rectal cancers via pre-treatment MRI

Thomas DeSilvio; Leo Bao; Dhruv Seth; Prathyush Chirra; Sneha Singh; Atreya Sridharan; Murad Labbad; Katie Bingmer; Diana Jodeh; Eric L. Marderstein; Rajmohan Paspulati; David Liska; Kenneth A. Friedman; Smitha Krishnamurthi; Sharon L. Stein; Andrei S. Purysko; Satish E. Viswanath

doi:10.1117/12.2655787

3 April 2023 Integrating multi-plane and multi-region radiomic features to predict pathologic response to neoadjuvant chemoradiation in rectal cancers via pre-treatment MRI

Thomas DeSilvio, Leo Bao, Dhruv Seth, Prathyush Chirra, Sneha Singh, Atreya Sridharan, Murad Labbad, Katie Bingmer, Diana Jodeh, Eric L. Marderstein, Rajmohan Paspulati, David Liska, Kenneth A. Friedman, Smitha Krishnamurthi, Sharon L. Stein, Andrei S. Purysko, Satish E. Viswanath

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Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 124660K (2023) https://doi.org/10.1117/12.2655787
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Radiomic analysis has shown significant potential for predicting treatment response to neoadjuvant therapy in rectal cancers via routine MRI, though primarily based off a single acquisition plane or single region of interest. To exploit intuitive clinical and biological aspects of tumor extent on MRI, we present a novel multi-plane, multi-region radiomics framework to more comprehensively characterize and interrogate treatment response on MRI. Our framework was evaluated on a cohort of 71 T2-weighted axial and coronal MRIs from patients diagnosed with rectal cancer and who underwent chemoradiation. 2D radiomic features were extracted from three regions of interest (tumor, fat proximal to tumor, and perirectal fat) across axial and coronal planes, with a two-stage feature selection scheme designed to identify descriptors associated with pathologic complete response. When evaluated via a quadratic discriminant analysis classifier, our multi-plane, multi-region radiomics model outperformed single-plane or single-region feature sets with an area under the ROC curve (AUC) of 0.80 ± 0.03 in discovery and AUC=0.65 in hold-out validation. Uniquely, the optimal feature set comprised descriptors from across multiple planes (axial, coronal) as well as multiple regions (tumor, proximal fat, perirectal fat). Our multi-plane, multi-region radiomics framework may thus enable more comprehensive phenotyping of treatment response on MRI, potentially finding application for improved personalization of therapeutic and surgical interventions in rectal cancers.

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Thomas DeSilvio, Leo Bao, Dhruv Seth, Prathyush Chirra, Sneha Singh, Atreya Sridharan, Murad Labbad, Katie Bingmer, Diana Jodeh, Eric L. Marderstein, Rajmohan Paspulati, David Liska, Kenneth A. Friedman, Smitha Krishnamurthi, Sharon L. Stein, Andrei S. Purysko, and Satish E. Viswanath "Integrating multi-plane and multi-region radiomic features to predict pathologic response to neoadjuvant chemoradiation in rectal cancers via pre-treatment MRI", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 124660K (3 April 2023); https://doi.org/10.1117/12.2655787

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