Nomogram of baseline CT-radiomics from small-cell lung cancer patients: evaluation of added prognostic value for overall survival and probability of distant metastasis

Objectives: To evaluate the added prognostic value of baseline CT-radiomics using nomogram for overall survival and probability of distant metastasis in small-cell lung cancer patients. Materials and Methods This retrospective study consisted of 122 patients with stage IIA-IIIB small-cell lung cancer, which 97 patients for training dataset and 25 for validation dataset. The function defined as rad_score was constructed by the linear combination of selected radiomics features from baseline CT images weighted by their respective logistic coefficients and intercept in the LASSO-Cox model. The nomogram was developed based on the above function for overall survival (OS) and calibrated by the Harrell’s concordance index (C-index). The performance of the classifiers for DM was evaluated by receiver operating characteristics (ROC) curves with the indictor of area under curves (AUC). Furthermore, survival curve depicted by Kaplan-Meier method was compared with Log-rank test between low- and high-risk group. Results The nomogram performance of radiomics features and risk clinical factors (c-index of 0.64) don’t take advantage over the one of risk clinical factors-based alone (c-index of 0.596). The probability prediction of combination of the radiomics and clinical risk factor, radiomics alone, and clinical factors alone was shown, namely AUC of 0.673, 0.640 and 0.650, respectively. No significant different was found between ROCs (p-value > 0.4, Delong test). Moreover, we compared the Kaplan-Meier curves between low- and high-risk group, and showed p < 0.001 with Log-rank test. Conclusion In the study, we cann’t confirm the hypothesis that baseline CT-radiomics contribute to predict the OS and probability of DM significantly. Moreover, the nomogram model based on combination of radiomics and clinical parameters has disadvantage over clinical parameters alone, probably affected by heterogeneity of datasets or SCLC need more valuable information for prediction outcomes.