CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph...

Accurate preoperative assessment of occult lymph node metastasis (OLNM) plays a crucial role in informing therapeutic decision-making for lung cancer patients. Computed tomography (CT) is the most widely used imaging modality for preoperative work-up. The aim of this study was to develop and validate a CT-based machine learning model integrating intra-and peri-tumoral features to predict OLNM in lung cancer patients.

Eligible patients with peripheral lung cancer confirmed by radical surgical excision with systematic lymphadenectomy were retrospectively recruited from January 2019 to December 2021. 1688 radiomics features were obtained from each manually segmented VOI which was composed of gross tumor volume (GTV) covering the boundary of entire tumor and three peritumoral volumes (PTV3, PTV6 and PTV9) that capture the region outside the tumor. A clinical-radiomics model incorporating radiomics signature, independent clinical factors and CT semantic features was established via multivariable logistic regression analysis and presented as a nomogram. Model performance was evaluated by discrimination, calibration, and clinical utility.

Overall, 591 patients were recruited in the training cohort and 253 in the validation cohort. The radiomics signature of PTV9 showed superior diagnostic performance compared to PTV3 and PTV6 models. Integrating GPTV radiomics signature (incorporating Rad-score of GTV and PTV9) with clinical risk factor of serum CEA levels and CT imaging features of lobulation sign and tumor-pleura relationship demonstrated favorable accuracy in predicting OLNM in the training cohort (AUC, 0.819; 95% CI: 0.780-0.857) and validation cohort (AUC, 0.801; 95% CI: 0.741-0.860). The predictive performance of the clinical-radiomics model demonstrated statistically significant superiority over that of the clinical model in both cohorts (all p < 0.05).

The clinical-radiomics model was able to serve as a noninvasive preoperative prediction tool for personalized risk assessment of OLNM in peripheral lung cancer patients.