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Predicting multiple linear stapler firings in double stapling technique with an MRI-based deep-learning model.

Zhanwei Fu1, Shuchun Li1, Lu Zang1

  • 1Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, People's Republic of China.

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Multiple linear stapler firings increase anastomotic leakage risk in laparoscopic low anterior resection. Tumor size and CEA levels are key risk factors. A deep-learning model integrating imaging and clinical data accurately predicts high-risk patients.

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Area of Science:

  • Surgical Oncology
  • Medical Imaging
  • Machine Learning in Medicine

Background:

  • Multiple linear stapler firings are a known risk factor for anastomotic leakage (AL) after laparoscopic low anterior resection (LAR) using the double stapling technique (DST).
  • Predicting the need for three or more stapler firings is crucial for optimizing surgical strategy and reducing AL risk.

Purpose of the Study:

  • To identify risk factors associated with three or more linear stapler firings in laparoscopic LAR with DST anastomosis.
  • To develop and validate predictive models, including a deep-learning approach using MRI, for identifying patients requiring multiple stapler firings.

Main Methods:

  • Retrospective analysis of 328 patients undergoing laparoscopic LAR with DST anastomosis, divided into training (n=260) and testing (n=68) sets.
  • Development of a clinical predictive model using logistic regression and an image-based model using 3D convolutional networks on MRI data.
  • Creation of an integrated model combining clinical variables and MRI data; all models were validated on an independent cohort of 128 patients.

Main Results:

  • 17.7% of patients required three or more stapler firings.
  • Independent risk factors identified were tumor size ≥5 cm (OR=2.54) and preoperative CEA level >5 ng/mL (OR=2.20).
  • The integrated model demonstrated superior predictive performance (AUC=0.88, accuracy=94.1% in training; AUC=0.84, accuracy=93.8% in validation) compared to clinical and image-only models.

Conclusions:

  • Tumor size and preoperative CEA levels are significant predictors of requiring multiple stapler firings during laparoscopic LAR.
  • A deep-learning model integrating pelvic MRI and clinical data effectively predicts patients at high risk for multiple stapler firings.
  • This predictive model can aid in preoperatively selecting the optimal anastomotic technique for mid-low rectal cancer patients.