Abstract
Evaluating the invasiveness of lung adenocarcinoma is crucial for determining the appropriate surgical strategy, impacting postoperative outcomes. This study developed a multimodality model combining radiomics, intraoperative frozen section (FS) pathology, and clinical indicators to predict invasion status. The study enrolled 1,424 patients from two hospitals, divided into multimodal training, radiology testing, and pathology testing cohorts. A prospective validation cohort of 114 patients was selected between March and May 2023. The radiomics + pathology + clinical indicators multimodality model (multi-RPC model) achieved an area under the curve (AUC) of 0.921 (95% confidence interval [CI] 0.899-0.939) in the multimodal training cohort and 0.939 (95% CI 0.878-0.975) in the validation cohort, outperforming single- and dual-modality models. The multi-RPC model's predictive accuracy of 0.860 (95% CI 0.782-0.918) suggests that it could significantly reduce inappropriate surgical procedures, enhancing precision oncology by integrating multimodal information to guide surgical decisions.