A nomogram-based radiomics for predicting survival to concurrent chemoradiotherapy in inoperable pancreatic cancer: a dual-center cohort study.

Objective: This study was designed to explore the value of machine learning-based radiology in predicting overall survival (OS) among patients with inoperable pancreatic cancer (PC) who are undergoing concurrent chemoradiotherapy (CCRT). Methods: This multicenter study enrolled 342 patients with inoperable PC. Firstly, radiomic features were pre-screened by univariate Cox regression and subsequently used to develop 101 machine-learning-based imaging models. An optimized selection algorithm was applied to these models to derive each patient's radiomic signature (Rad-score). Secondly, key clinical predictors of OS were identified via LASSO-Cox regression and incorporated into clinical nomogram. Finally, the Rad-score was combined with the independent clinical risk factors to construct clinical-radiomics nomogram. Results: LASSO-Cox regression identified age, clinical stage, tumor size, and albumin level as independent prognostic factors for OS. Based on these four variables, we constructed a clinical nomogram in the training cohort, which achieved a C-index of 0.71. In the internal validation cohort, the areas under the receiver operating characteristic curve (AUC-ROC) for predicting 1-, 2-, and 3-year OS were 0.577, 0.721, and 0.730, respectively; in the external validation cohort, the corresponding AUC-ROCs were 0.841, 0.757, and 0.598. Subsequently, each patient's Rad-score was integrated with these clinical predictors to develop a clinical-radiomics nomogram, which demonstrated a C-index of 0.892. The AUC-ROCs for predicting 1-, 2-, and 3-year OS were 0.791, 0.846, and 0.840 in the internal validation cohort, and 0.863, 0.830, and 0.734 in the external validation cohort. Conclusion: The clinical-radiomics nomogram demonstrated superior predictive performance for OS compared to the clinical nomogram in inoperable PC patients undergoing CCRT.