Abstract 5618: Multi-institutional validation of a radiomics-based artificial intelligence method for predicting response to PD-1/PD-L1 immune checkpoint inhibitor (ICI) therapy in stage IV NSCLC

作者：Ravi B. Parikh, Petr Jordan, Rita J. Ciaravino, Ryan Beasley, Arpan A. Patel, Dwight H. Owen, Arya Amini, Brendan D. Curti, Ray D. Page, Aurélie Swalduz, Jean-Paul Bérégi, Jan Chrusciel, Eric M. Snyder, Pritam Mukherjee, Heather M. Selby, Soohee Lee, Roshanthi Weerasinghe, Shwetha Pindikuri, Jakob Weiss, Andrew L. Wentland, Anish Kirpalani, An Liu, Olivier Gevaert, George R. Simon, Hugo J.W.L. Aerts · 发表于：Cancer Research · 年份：2023 · DOI：10.1158/1538-7445.am2023-5618 · 被引用次数：1 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Gastric Cancer Management and Outcomes、Colorectal Cancer Treatments and Studies

Abstract There is an urgent clinical need to identify patients likely to benefit from immune checkpoint inhibitor ICI treatment. Approaches available in the clinic today, such as PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB), are insufficient for this task, in part as differences in microenvironments expressed by individual tumors may lead to heterogeneous response patterns. Recent efforts exploring the utility of quantitative imaging (radiomic) biomarkers to predict response to ICIs have shown promise to provide a more accurate and scalable method. In contrast to previously published models, our work focuses on generalizable models for predicting individual lesion-level as well as patient-level response at 3-month follow-up per RECIST criteria, using a large multi-institutional “real-world” dataset. The models combine radiomics features with demographic, molecular, and laboratory values routinely available in patients’ electronic medical records. We analyzed radiomic characteristics of 6,295 primary and metastatic lesions from 1,206 metastatic NSCLC patients treated with anti-PD-1/anti-PD-L1 ICIs from 8 institutions across the US and Europe. Patients with unavailable PD-L1 IHC, imaging follow-up, or with oncogenic driver mutations were excluded from analysis, resulting in a total dataset of 766 subjects randomly assigned to training (N=514) and validation sets (N=252). Using gradient-boosted decision tree algorithms, we developed a multi-modal predictive m...