Computed tomography-based quantification of intra-tumoral heterogeneity for predicting treatment response to concurrent chemoradiotherapy in non-small cell lung cancer: a multicenter retrospective study.

Background: Concurrent chemoradiotherapy (CCRT) constitutes a cornerstone of treatment for non-small cell lung cancer (NSCLC). However, NSCLC's intrinsic heterogeneity yields variable therapeutic responses. Noninvasive, quantitative assessment of intra-tumoral heterogeneity (ITH) may improve prediction of treatment efficacy. This study sought to derive ITH metrics from pretreatment contrast-enhanced computed tomography (CT) and to assess their prognostic value for outcomes following CCRT in NSCLC patients. Methods: In this multicenter, retrospective study, pretreatment CT scans from NSCLC patients undergoing CCRT between January 1, 2019, and December 31, 2023 were allocated to training (n=132) and validation (n=32) cohorts. Conventional radiomic features were extracted from the tumor region of interest. A Gaussian mixture model (GMM) determined three optimal tumor subregions, from which ecological diversity metrics were computed in Python (v3.6) and subjected to least absolute shrinkage and selection operator (LASSO) regression. Support vector machine (SVM) generated both the ITH and C‑radiomics classifiers. Multivariable logistic regression then integrated clinical, C‑radiomics, and ITH predictors into a unified prognostic model, whose discriminative ability was quantified by the area under the receiver operating characteristic curve (AUC). Patients were dichotomized by ITH and combined‑model thresholds to evaluate stratification of predicted response. Results: Thirteen ecological diversity metrics informed the ITH classifier. The ITH model achieved AUCs of 0.78 [95% confidence interval (CI): 0.70-0.86] in training and 0.77 (95% CI: 0.59-0.94) in validation. Incorporation of clinical and radiomic features yielded a combined model with AUCs of 0.92 (95% CI: 0.86-0.98) and 0.87 (95% CI: 0.72-1.00), respectively. According to the cutoff values of the ITH and combined models, patients were stratified (ITH model: 0.65; combined model: 0.53, 0.86), and significant differences in progression-free survival (PFS) and overall survival (OS) were observed in the high-risk and low-risk groups (P<0.05). Conclusions: The CT‑based ITH model exhibited robust predictive accuracy, surpassing standalone clinical and conventional radiomic models. A unified model combining clinical, radiomic, and ITH features further enhanced prognostic precision for CCRT response in NSCLC. Both models successfully stratified patients by PFS and OS risk, underscoring their potential to inform personalized therapeutic decision‑making.