Preoperative Computed Tomography Radiomics for Recurrence-Pattern Classification of T3-T4 Non-Small Cell Lung Cancer

Local recurrence after surgery remains a major clinical challenge in patients with T3 and T4 non-small cell lung cancer (NSCLC). To address the lack of a standardized preoperative imaging-based strategy for characterizing local recurrence, a computed tomography (CT)-based radiomic framework is presented to capture intratumoral and peritumoral heterogeneity and differentiate local recurrence from distant metastasis after R0 resection. The proposed protocol integrates radiomic features extracted from tumor and peritumoral regions on preoperative contrast-enhanced CT images. The application cohort comprised 103 patients with pathologically confirmed T3-T4, N0-2, M0 NSCLC who underwent surgery with R0 resection between January 2013 and December 2020. 33 developed local recurrence, and 70 developed distant metastasis. All preprocessing, feature selection, normalization, hyperparameter tuning, model fitting, and threshold determination were restricted to the training cohort. Tumor-only, peritumoral, and combined radiomic models were evaluated in a held-out validation cohort. Clinical characteristics, tumor location, preoperative laboratory parameters, and treatment information were collected. Tumor-specific and tumor-peritumoral combined radiomic representations were constructed and evaluated to characterize the behavior of the proposed framework. In the validation cohort, the peritumoral radiomic representation demonstrated improved discrimination compared with tumor-only features, while the combined tumor-peritumoral framework showed the most stable and consistent performance (AUC = 0.80). This protocol is a preliminary demonstration of a standardized CT radiomics workflow for differentiating postoperative failure patterns after R0 resection; multicenter external validation is required before clinical implementation.