3D Auto-segmentation of pancreas cancer and surrounding anatomical structures for surgical planning.
Authors
Affiliations (4)
Affiliations (4)
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
- Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Republic of Korea.
- Department of Radiology, Chungbuk National University Hospital, Cheongju, Republic of Korea.
Abstract
This multicenter study aimed to develop a deep learning-based autosegmentation model for pancreatic cancer and surrounding anatomical structures using computed tomography (CT) to enhance surgical planning. We included patients with pancreatic cancer who underwent pancreatic surgery at three tertiary referral hospitals. A hierarchical Swin Transformer V2 model was implemented to segment the pancreas, pancreatic cancers, and peripancreatic structures from preoperative contrast-enhanced CT scans. Data was divided into training and internal validation sets at a 3:1 ratio (from one tertiary institution), with separately prepared external validation set (from two separate institutions). Segmentation performance was quantitatively assessed using the dice similarity coefficient (DSC) and qualitatively evaluated (complete vs partial vs absent). A total of 275 patients (51.6% male, mean age 65.8 ± 9.5 years) were included (176 training group, 59 internal validation group, and 40 external validation group). No significant differences in baseline characteristics were observed between the groups. The model achieved an overall mean DSC of 75.4 ± 6.0 and 75.6 ± 4.8 in the internal and external validation groups, respectively. It showed high accuracy particularly in the pancreas parenchyma (84.8 ± 5.3 and 86.1 ± 4.1) and lower accuracy in pancreatic cancer (57.0 ± 28.7 and 54.5 ± 23.5). The DSC scores for pancreatic cancer tended to increase with larger tumor sizes. Moreover, the qualitative assessments revealed high accuracy in the superior mesenteric artery (complete segmentation, 87.5%-100%), portal and superior mesenteric vein (97.5%-100%), pancreas parenchyma (83.1%-87.5%), but lower accuracy in cancers (62.7%-65.0%). The deep learning-based autosegmentation model for 3D visualization of pancreatic cancer and peripancreatic structures showed robust performance. Further improvement will enhance many promising applications in clinical research.