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In Cancer immunology research ; h5-index 78.0

No predictive biomarkers can robustly identify non-small cell lung cancer (NSCLC) patients who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes ("delta") in the radiomic texture (DelRADx) of computed tomography (CT) patterns both within and outside tumor nodules before and after 2-3 cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 NSCLC patients at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST (response evaluation criteria in solid tumors)-derived response. Association of delta-radiomic risk-score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an area under the curve (AUC) of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, 0.85 and 0.81 in D2 and D3. DRS was associated with OS (hazard ratio: 1.64, 95% CI: 1.22 - 2.21, P = 0.0011, C-Index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in NSCLC patients.

Khorrami Mohammadhadi, Prasanna Prateek, Gupta Amit, Patil Pradnya, Velu Priya D, Thawani Rajat, Corredor Germán, Alilou Mehdi, Bera Kaustav, Fu Pingfu, Feldman Michael, Velcheti Vamsidhar, Madabhushi Anant