肺癌手術における臓側胸膜浸潤予測のための人工知能手術ビデオ分析モデルの開発と検証：多施設共同研究

Intraoperative diagnosis of visceral pleural invasion (VPI) during video-assisted thoracoscopic surgery (VATS) remains challenging. This study aimed to develop and validate a deep learning-based model to improve diagnostic accuracy and guide surgical decision-making.

Thoracoscopic videos and clinical data from 346 patients (3367 images, 2015-2024) in one hospital were divided into training, validation, and internal-test sets (7:2:1), whereas data from 53 patients (1274 images) in two other hospitals formed the external-test set. A spatial dropout-based Residual Convolutional Neural Network (VPI-Net) was developed for estimating patients' VPI status and VPI risk score (VPIscore). The model's performance was compared against intraoperative estimations by surgeons and preoperative assessments by radiologists.

The VPI-Net model demonstrated significantly higher area under the curve (AUC, 0.84-0.94) and accuracy (79.67-88.68%,) than two surgeons and one radiologist across all cohorts (p < 0.05). Additionally, the VPI-Net model outperformed human experts in sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) across all cohorts. A lower VPIscore (VPIscoreL) was significantly correlated with longer overall survival (OS), relapse-free survival (RFS), and time to progression (TTP) than a higher VPIscore (VPIscoreH) (all p < 0.001). Similar results were observed in patients who had small tumors, with those who had VPIscoreH exhibiting significantly worse RFS and TTP than those with VPIscoreL (RFS [p = 0.012], TTP [p = 0.035]). The VPIscoreL patients had a significantly longer TTP (p = 0.03) than the VPIscoreH patients after sublobectomy.

The proposed model enables satisfactory intraoperative identification of VPI, potentially improving patient outcomes during VATS.