Journal of gastric cancer · 2025
Predicting Anastomosis or Stump Leakage After Laparoscopic Gastrectomy: A Deep Learning Approach to Intraoperative Image Analysis.
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Park Ki Bum, Lee Hayemin, Kim Sojung, Lee Han Hong, Song Kyo Young, Woo Soyeon, Hwang Chi Shin, Kim Yonghwan, Seo Hoseok
Аннотация
PURPOSE: Postoperative leakage is a critical complication of laparoscopic gastrectomy for gastric cancer. Predicting leakage during surgery can enhance patient outcomes by enabling a timely intervention. This study aimed to develop and validate deep learning models for predicting leakage using laparoscopic images of the anastomosis sites.
MATERIALS AND METHODS: We analyzed 10,256 laparoscopic images from 2,035 patients who underwent gastrectomy for gastric cancer at three institutions. Six datasets (EXP1 to EXP6) were created based on variations in image quality and analytical methods. Six deep learning architectures, ResNet18, ResNet34, ResNet50, EfficientNet_V2_L, Inception_V3, and DenseNet121, were employed for training. Deep learning models were trained to classify images into normal or leakage categories at the duodenal stump (DS) and esophagojejunal (EJ) anastomoses. Model performance was evaluated using F1 scores, recall, and Grad-CAM visualization.
RESULTS: Leakage was identified in 1.3% and 4.3% of the patients with DS and EJ, respectively. Among the six datasets, EXP1, which used one image per patient and applied augmentation, exhibited the best performance. ResNet18 trained on EXP1 demonstrates the highest recall values, achieving 0.8474 for DS and 0.8000 for EJ, with F1 scores of 0.6357 and 0.6938, respectively. Grad-CAM revealed that both local and surrounding tissue features were critical for model prediction.
CONCLUSIONS: Deep learning could predict leakage during gastric cancer surgery. High-resolution imaging, single-image analysis, and data augmentation were pivotal for model performance. These findings lay the groundwork for clinical applications and future research on surgical image analysis.
MATERIALS AND METHODS: We analyzed 10,256 laparoscopic images from 2,035 patients who underwent gastrectomy for gastric cancer at three institutions. Six datasets (EXP1 to EXP6) were created based on variations in image quality and analytical methods. Six deep learning architectures, ResNet18, ResNet34, ResNet50, EfficientNet_V2_L, Inception_V3, and DenseNet121, were employed for training. Deep learning models were trained to classify images into normal or leakage categories at the duodenal stump (DS) and esophagojejunal (EJ) anastomoses. Model performance was evaluated using F1 scores, recall, and Grad-CAM visualization.
RESULTS: Leakage was identified in 1.3% and 4.3% of the patients with DS and EJ, respectively. Among the six datasets, EXP1, which used one image per patient and applied augmentation, exhibited the best performance. ResNet18 trained on EXP1 demonstrates the highest recall values, achieving 0.8474 for DS and 0.8000 for EJ, with F1 scores of 0.6357 and 0.6938, respectively. Grad-CAM revealed that both local and surrounding tissue features were critical for model prediction.
CONCLUSIONS: Deep learning could predict leakage during gastric cancer surgery. High-resolution imaging, single-image analysis, and data augmentation were pivotal for model performance. These findings lay the groundwork for clinical applications and future research on surgical image analysis.