AI Article Synopsis

  • Endoscopic ultrasound (EUS) is an important tool for evaluating pancreatic lesions, but its accuracy is not very high, prompting the development of a convolutional neural network (CNN) to improve diagnosis of pancreatic cystic neoplasms (PCN) and pancreatic solid lesions (PSL).
  • The CNN was trained on a large dataset from four international centers, comprising over 126,000 images, and successfully distinguished between different types of pancreatic lesions with high accuracy.
  • The results showed the CNN achieved over 99% accuracy for normal pancreatic tissue and PCNs, while distinguishing between pancreatic ductal adenocarcinoma (P-DAC) and neuroendocrine tumors (P-NET) at 94% accuracy, pointing to the

Article Abstract

Introduction: Endoscopic ultrasound (EUS) allows for characterization and biopsy of pancreatic lesions. Pancreatic cystic neoplasms (PCN) include mucinous (M-PCN) and nonmucinous lesions (NM-PCN). Pancreatic ductal adenocarcinoma (P-DAC) is the commonest pancreatic solid lesion (PSL), followed by pancreatic neuroendocrine tumor (P-NET). Although EUS is preferred for pancreatic lesion evaluation, its diagnostic accuracy is suboptimal. This multicentric study aims to develop a convolutional neural network (CNN) for detecting and distinguishing PCN (namely M-PCN and NM-PCN) and PSL (particularly P-DAC and P-NET).

Methods: A CNN was developed with 378 EUS examinations from 4 international reference centers (Centro Hospitalar Universitário São João, Hospital Universitario Puerta de Hierro Majadahonda, New York University Hospitals, Hospital das Clínicas Faculdade de Medicina da Universidade de São Paulo). About 126.000 images were obtained-19.528 M-PCN, 8.175 NM-PCN, 64.286 P-DAC, 29.153 P-NET, and 4.858 normal pancreas images. A trinary CNN differentiated normal pancreas tissue from M-PCN and NM-PCN. A binary CNN distinguished P-DAC from P-NET. The total data set was divided into a training and testing data set (used for model's evaluation) in a 90/10% ratio. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, and accuracy.

Results: The CNN had 99.1% accuracy for identifying normal pancreatic tissue, 99.0% and 99.8% for M-PCN and NM-PCN, respectively. P-DAC and P-NET were distinguished with 94.0% accuracy.

Discussion: Our group developed the first worldwide CNN capable of detecting and differentiating the commonest PCN and PSL in EUS images, using examinations from 4 centers in 2 continents, minimizing the impact of the demographic bias. Larger multicentric studies are needed for technology implementation.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11596526PMC
http://dx.doi.org/10.14309/ctg.0000000000000771DOI Listing

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