Abstract
ObjectiveBased on non-contrast-enhanced (NCE)/contrast-enhanced (CE) computed tomography (CT) images, we try to identify a combined-radiomics model and evaluate its predictive capacity regarding response to anti-PD1 immunotherapy of patients with non-small-cell lung cancer (NSCLC).Methods131 patients with NSCLC undergoing anti-PD1 immunotherapy were retrospectively enrolled from 7 institutions. Using largest lesion (LL) and target lesions (TL) approaches, we performed a radiomics analysis based on pretreatment NCE-CT (NCE-radiomics) and CE-CT images (CE-radiomics), respectively. Meanwhile, a combined-radiomics model based on NCE-CT and CE-CT images was constructed. Finally, we developed their corresponding nomograms incorporating clinical factors. ROC was used to evaluate models’ predictive performance in the training and testing set, and a DeLong test was employed to compare the differences between different models.ResultsFor TL approach, both NCE-radiomics and CE-radiomics performed poorly in predicting response to immunotherapy. For LL approach, NCE-radiomics nomograms and CE-radiomics nomograms incorporating with clinical factor of distant metastasis all showed satisfactory results, reflected by the AUCs in the training (AUC=0.84, 95% CI: 0.75-0.92; AUC=0.77, 95% CI: 0.67-0.87) and test sets (AUC=0.78, 95% CI: 0.64-0.92, AUC=0.73, 95% CI: 0.57-0.88), respectively. Compared with the NCE-radiomics nomograms, the combined-radiomics nomogram showed incremental predictive capacity in the training set (AUC=0.85, 95% CI: 0.77-0.92) and test set (AUC=0.81, 95% CI: 0.67-0.94), respectively, but no statistical difference (P=0.86, P=0.79).ConclusionCompared with radiomics based on single NCE or CE-CT images, the combined-radiomics model has potential advantages to identify patients with NSCLC most likely to benefit from immunotherapy, and may effectively improve more precise and individualized decision support.
Highlights
Immunotherapy has revolutionized the therapeutic strategies for non-small cell lung cancer (NSCLC) [1,2,3,4]
We developed a combined-radiomics model based on both NCE-CT and CE-CT images from the largest target lesion (LL) approach to further improve the prediction efficiency
A total of 285 patients with advanced NSCLC treated with a PD-1/ PD-L1 immune checkpoint inhibitors (ICIs) therapy from August 1, 2016 to February 28, 2019 in 7 participating institutions were enrolled according to the inclusion criteria (Appendix S1 and Figure 2)
Summary
Immunotherapy has revolutionized the therapeutic strategies for non-small cell lung cancer (NSCLC) [1,2,3,4]. Due to the intratumoral heterogeneity and evolution over time [16, 17], the effective use of these biomarkers as predictive biomarkers is seriously affected by sampling bias [18] and the absence of standardization between different tests [19]. Another issue is that patients with negative PD-L1 status may still benefit from anti-PD(L) immunotherapy [4, 20, 21]. To better predict response to immunotherapy, there is an urgent need to identify alternative predictive biomarkers
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