Deep learning-based differentiation between mucinous cystic neoplasm and serous cystic neoplasm in the pancreas using endoscopic ultrasonography

Leang Sim Nguon, Kangwon Seo, Jung Hyun Lim, Tae Jun Song, Sung Hyun Cho, Jin Seok Park, Suhyun Park

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.

Original languageEnglish
Article number1052
JournalDiagnostics
Volume11
Issue number6
DOIs
StatePublished - 2021

Keywords

  • Convolutional neural network
  • Deep learning
  • Diagnostic imaging
  • Endoscopic ultrasonography
  • Mucinous cystic neoplasm
  • Pancreatic cystic neoplasms
  • Serous cystic neoplasm

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