AI may help predict tumor sensitivity to systemic cancer therapies

AI may help predict tumor sensitivity to systemic cancer therapies

Reviewed by James Ives, M.Psych. (Editor) Mar 20 2020 Bottom Line: Using standard-of-care computed tomography (CT) scans in patients with advanced non-small cell lung cancer (NSCLC), researchers utilized artificial intelligence (AI) to train algorithms to predict tumor sensitivity to three systemic cancer therapies. Journal in Which the Study was Published: Clinical Cancer Research , a journal of the American Association for Cancer Research Author: Laurent Dercle, MD, PhD, associate research scientist in the Department of Radiology at the Columbia University Irving Medical Center Background: "Radiologists' interpretation of CT scans of cancer patients treated with systemic therapies is inherently subjective," said Dercle. "The purpose of this study was to train cutting-edge AI technologies to predict patients' responses to treatment, allowing radiologists to deliver more accurate and reproducible predictions of treatment efficacy at an early stage of the disease." To determine if patients with NSCLC are responding to systemic therapy, radiologists currently quantify changes in tumor size and the appearance of new tumor lesions, Dercle explained. However, this type of evaluation can be limited, especially in patients treated with immunotherapy, who can display atypical patterns of response and progression, he noted. "Newer systemic therapies prompt the need for alternative metrics for response assessment, which can shape therapeutic decision-making," Dercle said. How the study was conducted and results: Dercle and colleagues utilized data from multiple phase II/phase III clinical trials that evaluated systemic treatment in patients with NSCLC. These patients were treated with one of three agents: the immunotherapeutic agent nivolumab (Opdivo), the chemotherapeutic agent docetaxel (Taxotere), or the targeted therapeutic gefitinib (Iressa). The researchers retrospectively analyzed standard-of-care CT images from 92 patients receiving nivolumab in two trials; 50 patients receiving docetaxel in one trial; and 46 patients receiving gefitinib in one trial. To develop the model, the researchers used the CT images taken at baseline and on first-treatment assessment (three weeks for patients treated with gefitinib; eight weeks for patients treated with either nivolumab or docetaxel). Tumors were classified as treatment-sensitive or treatment-insensitive based on the reference standard of each trial (median progression-free survival in the nivolumab and docetaxel cohorts; analysis of surgical specimen following gefitinib treatment). Among all three cohorts, patients were randomized into training or validation groups. Related Stories



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