Deep-Learning Virtual Superior Mesenteric Artery Modeling for Risk Stratification in Pancreas Surgery

Understanding the patient-specific anatomy of the superior mesenteric artery (SMA) and its branches is of critical importance when performing a pancreatic surgery. This study assesses deep-learning-based virtual SMA modelling for three-dimensional (3D) visualization of SMA's course and branching patterns. This model is then used to correlate anatomical features with intra-/postoperative outcomes.

Preoperative computed tomography (CT) scans of 124 patients undergoing pancreatic resection for pancreatic malignancy at St. Elizabeth's Medical Center and Kyoto University were analyzed for course, branching, caliber, and aortic angle using a deep learning modeling software. Following anatomic modelling, the SMA was divided into regions on the basis of its relationship to the pancreas: SMA1 (above pancreas), SMA2 (intrapancreatic), and SMA3 (below pancreas). Univariate and multivariate logistic and linear regression were used to compare anatomical measurements to perioperative outcomes.

Differences in anatomic measurements were observed between both populations. The mean caliber of SMA1, SMA2, and SMA3 was 7.05, 6.20, and 5.69 mm, respectively. A mean of 2.21 branches were observed in SMA2, and 4.52 in SMA3. Furthermore, fewer branches in SMA2 was associated with both postoperative pancreatic fistula (POPF) and Clavien-Dindo complication grade ≥ III. Finally, when stratified by minimally invasive approach, a greater distance between the superior border of pancreas and SMA was associated with POPF.

This study shows that deep-learning-based virtual three-dimensional reconstruction of SMA enables accurate assessment of the anatomical relationship between the pancreas and SMA. Specific anatomical features were found to be associated with intra- and postoperative outcomes. Therefore, SMA modeling not only contributes to improved preoperative planning and intraoperative navigation, but also to outcome prognostication.