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Immune checkpoint inhibitors targeting the programmed cell death (PD)1/ L1 axis have been approved for treatment of chemotherapy refractory advanced non-small cell lung cancer (NSCLC) for a few years. While higher PD-L1 expression is associated with better outcomes after monotherapy with immune checkpoint inhibitors, it is not a perfect predictive biomarker for clinical benefit from immunotherapy, because some patients with low PD-L1 expression have sustained responses. In clinical practice, using radiological tools like Response Evaluation Criteria in Solid Tumors (RECIST), tends to underestimate the benefit of therapy. For instance, some patients treated with immunotherapy suffer from pseudoprogression while actually having a favorable response, RECIST in this setting is inadequate to capture the response. In this study we sought to explore whether radiomic texture features extracted from both inside and outside of the tumor from baseline CT scans were associated with overall patient survival (OS) in 139 NSCLC patients being treated with IO from two separate sites. Patients were divided into a discovery (D1 = 50; nivolumab from Cleveland Clinic) and two validation sets (D2 = 62 from Cleveland Clinic, D3 = 27 from University of Pennsylvania Health System. Patients in the validation sets had been treated with different types of checkpoint inhibitor drugs including nivolumab, pembrolizumab, and atezolizumab. 454 radiomic texture features from within (intra-tumoral) and outside the tumor (peri-tumoral) were extracted from baseline contrast CT images. Following feature selection on the discovery set, a radiomic risk-score signature was generated by using least absolute shrinkage and selection operator. Using a Cox regression model, the association of the radiomic signature with overall survival (OS) was evaluated in the discovery and two validation sets. In addition, 95% confidence intervals (CI) and relative hazard ratios (HR) were calculated. Our results revealed that the radiomics signature was significantly associated with OS, both in the discovery set (HR = 5.06, 95%CI = 3, 8.55; p-value < 0.0001) and the two validation data sets (D2: HR = 5.88, 95% CI = 2.19, 21.63, p-value = 0.0009; D3: HR = 5.37, 95% CI = 1.74, 16.57, p-value = 0.0034). Our initial results appear to suggest that our radiomic signature could serve as a non-invasive way of predicting and monitoring response to checkpoint inhibitors for patients with non-small cell lung cancer.
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