Preoperative assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study.

Objective: To develop a multimodal predictive model, Radiomics Integrated TLSs System (RAITS), based on preoperative CT radiomic features for the identification of TLSs in stage I lung adenocarcinoma patients and to evaluate its potential in prognosis stratification and guiding personalized treatment.

Methods: The most recent preoperative chest CT thin-slice scans and postoperative hematoxylin and eosin-stained pathology sections of patients diagnosed with stage I LUAD were retrospectively collected. Tumor segmentation was achieved using an automatic virtual adversarial training segmentation algorithm based on a three-dimensional U-shape convolutional neural network (3D U-Net). Radiomic features were extracted from the tumor and peritumoral areas, with extensions of 2 mm, 4 mm, 6 mm, and 8 mm, respectively, and deep learning image features were extracted through a convolutional neural network. Subsequently, the RAITS was constructed. The performance of RAITS was then evaluated in both the train and validation cohorts.

Results: RAITS demonstrated superior AUC, sensitivity, and specificity in both the training and external validation cohorts, outperforming traditional unimodal models. In the validation cohort, RAITS achieved an AUC of 0.78 (95% CI, 0.69-0.88) and showed higher net benefits across most threshold ranges. RAITS exhibited strong discriminative ability in risk stratification, with p < 0.01 in the training cohort and p = 0.02 in the validation cohort, consistent with the actual predictive performance of TLSs, where TLS-positive patients had significantly higher recurrence-free survival (RFS) compared to TLS-negative patients (p = 0.04 in the training cohort, p = 0.02 in the validation cohort).

Conclusion: As a multimodal predictive model based on preoperative CT radiomic features, RAITS demonstrated excellent performance in identifying TLSs in stage I LUAD and holds potential value in clinical decision-making.