Optimizing surgical decision-making for lung adenocarcinoma by preoperatively identifying pathological high-risk factors: retrospective development and prospective validation of a deep learning model.

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Abstract

Accurate identification of pathological high-risk factors (PHRFs) in early-stage lung adenocarcinoma (LUAD) is critical for optimizing surgical decision-making. However, reliance on intraoperative frozen section (FS) assessment is limited by insufficient sensitivity. This study aimed to retrospectively develop and prospectively validate a deep learning model (a knowledge-based graph convolutional network, KB-GCN) based on preoperative CT scans to identify PHRFs in LUAD. We retrospectively developed and externally validated a KB-GCN using two cohorts (A: 268 patients/297 lesions for training and internal validation; B: 68 patients/75 lesions for external validation). We then conducted a pre-registered, single-center, prospective observational validation in 200 consecutive surgical candidates with early-stage LUAD. Before prospective enrollment, the model architecture, weights, preprocessing pipeline, and the decision threshold (0.40, determined from the retrospective phase) were locked. For each patient, a preoperative prediction was generated before intraoperative FS and final pathology (FP); the clinical team was blinded to the model output. The performance of the locked model and FS was compared with FP. In retrospective validation, the KB-GCN model achieved an area under the curve (AUC) of 0.92 (95% CI: 0.86-0.97) in the internal validation cohort and 0.88 (95% CI: 0.81-0.94) in the external validation cohort. The KB-GCN model outperformed all 6 compared classical 2D/3D CNN models (best comparative AUC: 0.79). In prospective validation, intraoperative FS achieved an overall sensitivity of 59% and accuracy of 77% for detecting PHRFs, misclassifying 40.6% (39/96) of PHRF-positive cases. In contrast, the KB-GCN model demonstrated significantly higher overall sensitivity (82%) and AUC (0.83), although with lower specificity (75% vs FS: 92%). The KB-GCN model showed superior performance in part-solid nodules (PSN, AUC: 0.86) and in 2-3 cm tumors (AUC: 0.86), with moderate performance in ≤1 cm tumors (AUC: 0.82) and in 1-2 cm tumors (AUC: 0.79). This study retrospectively developed and, for the first time, prospectively demonstrates that a deep learning model based on preoperative chest CT predicts PHRFs, achieving significantly higher sensitivity for identifying PHRFs in early-stage invasive LUAD than conventional intraoperative FS. Despite slightly lower specificity, the KB-GCN model effectively compensates for the critical sensitivity deficit of FS, particularly for tumors (1-3 cm) containing solid components. Preoperative deep learning assessment combined with intraoperative FS provides thoracic surgeons with more comprehensive and accurate information to optimize surgical decisions (e.g., extent of resection: lobectomy or sublobar resection). Future development requires integrating this preoperative model with intraoperative FS assessment into standardized workflows.

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