A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy

Sci Rep. 2021 Apr 19;11(1):8454. doi: 10.1038/s41598-021-87748-0.


Histopathological diagnosis of pancreatic ductal adenocarcinoma (PDAC) on endoscopic ultrasonography-guided fine-needle biopsy (EUS-FNB) specimens has become the mainstay of preoperative pathological diagnosis. However, on EUS-FNB specimens, accurate histopathological evaluation is difficult due to low specimen volume with isolated cancer cells and high contamination of blood, inflammatory and digestive tract cells. In this study, we performed annotations for training sets by expert pancreatic pathologists and trained a deep learning model to assess PDAC on EUS-FNB of the pancreas in histopathological whole-slide images. We obtained a high receiver operator curve area under the curve of 0.984, accuracy of 0.9417, sensitivity of 0.9302 and specificity of 0.9706. Our model was able to accurately detect difficult cases of isolated and low volume cancer cells. If adopted as a supportive system in routine diagnosis of pancreatic EUS-FNB specimens, our model has the potential to aid pathologists diagnose difficult cases.

MeSH terms

  • Adenocarcinoma / diagnosis*
  • Adenocarcinoma / diagnostic imaging
  • Adenocarcinoma / surgery
  • Carcinoma, Pancreatic Ductal / diagnosis*
  • Carcinoma, Pancreatic Ductal / diagnostic imaging
  • Carcinoma, Pancreatic Ductal / surgery
  • Deep Learning*
  • Endoscopic Ultrasound-Guided Fine Needle Aspiration / methods*
  • Humans
  • Image-Guided Biopsy / methods*
  • Pancreatic Neoplasms / diagnosis*
  • Pancreatic Neoplasms / diagnostic imaging
  • Pancreatic Neoplasms / surgery
  • Retrospective Studies