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Background <p>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.</p> Methods <p>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.</p> Results <p>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 (<i>p</i> &lt; 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 <i>p</i> &lt; 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 [<i>p</i> = 0.012], TTP [<i>p</i> = 0.035]). The VPIscoreL patients had a significantly longer TTP (<i>p</i> = 0.03) than the VPIscoreH patients after sublobectomy.</p> Conclusion <p>The proposed model enables satisfactory intraoperative identification of VPI, potentially improving patient outcomes during VATS.</p>

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Development and Validation of an Artificial Intelligence Surgical Video Analysis Model for Predicting Visceral Pleural Invasion in Lung Cancer Surgery: A Multicenter Study

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Yukun Wu,
  • Hao Xu,
  • Xinghua Cheng,
  • Pengchong Li,
  • Jiantao Li,
  • Ruiheng Jiang,
  • Fengwei Li,
  • Songjing Zhao,
  • Yuxuan Wang,
  • Shenrui Zhang,
  • Zewen Sun,
  • Sida Cheng,
  • Tian Guan,
  • Hao Li,
  • Xiuyuan Chen,
  • Feng Yang,
  • Guanchao Jiang,
  • Shanshan Li,
  • Jun Wang,
  • Yun Li,
  • Fan Yang,
  • Jie Tian,
  • Wei Mu,
  • Jian Zhou

摘要

Background

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.

Methods

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.

Results

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.

Conclusion

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