An intelligent fusion model for Ki-67 prediction in non-small cell lung cancer: A cloud-based prediction system integrating radiomics.

BACKGROUND: The expression level of Ki-67 affects the prognosis of NSCLC patients.

OBJECTIVE: Accurate preoperative prediction of Ki-67 expression in non-small cell lung cancer (NSCLC) is crucial for prognostic stratification.

METHODS: This multicenter retrospective study enrolled 876 NSCLC patients (January 2015-December 2024) from four institutions, randomly divided into training (n = 525), testing (n = 175), and external validation (n = 176) sets. Radiomic features were extracted from intratumoral and peritumoral (0-12 mm) regions on CT images to construct intra-, peri-, and combined (intra + peri) radiomic scores (Rad-score). Deep learning scores (DL-score) were generated using ResNet101 for whole-lung and tumor-specific analyses. A random forest model integrating Rad-scores, DL-scores, and clinical parameters (lobulation, emphysema, etc.) was developed and validated across all datasets.

RESULTS: The combined model (intra + peri Rad-score, intra-tumor DL-score, and clinical features) achieved AUCs of 0.98 (95% CI: 0.97-0.99), 0.92 (0.88-0.96), and 0.92 (0.87-0.96) in training, testing, and external validation sets, with corresponding F1-scores of 0.90, 0.75, and 0.70. SHAP interpretation identified intra-tumor DL-score as the most significant predictor (feature contribution: 46.8%).

CONCLUSION: The multimodal random forest model enables noninvasive and accurate Ki-67 prediction in NSCLC, demonstrating superior generalizability and interpretability to guide personalized therapeutic strategies.

CLINICAL RELEVANCE: Integrating deep learning with intratumoral and peritumoral radiomics enhances the preoperative prediction of Ki-67 expression in patients with non-small cell lung cancer.