The role of artificial intelligence and deep learning in determining the histopathological grade of pancreatic neuroendocrine tumors by using EUS images.

in Endoscopic ultrasound by Sercan Kiremitci, Gulseren Seven, Gokhan Silahtaroglu, Koray Kochan, Serife Degirmencioglu Tosun, Hakan Senturk

TLDR

  • The study used AI to analyze ultrasound images of the pancreas and accurately predict the grade of pancreatic neuroendocrine tumors (pNETs).

Abstract

Pancreatic neuroendocrine tumors (pNETs) are relatively rare and consist of 2% of the all pancreatic tumors. Although some of pNETs have a benign, nonprogressive course, some may be progressive and result with metastasis. We aimed to estimate the grade of pNETs by using artificial intelligence (AI) via deep learning (DL) algorithms as indexing to cyto/histopathological classification according to the World Health Organization 2017. A total of 803 EUS images were collected from 44 patients who had a cyto/histo-pathologically confirmed diagnosis with EUS fine-needle aspiration or biopsy (FNA/B). First, raw EUS images were prepared for processing by AI via DL algorithms, and convolutional neural networks were utilized to train the machine to predict the grades from EUS images. IBM SPSS 25.0 program was used for statistical analyses. Thirty of the 44 patients (68%) were female, with a median age of 61 (range, 16-80) years. pNETs were mostly located in the pancreatic head: 24 cases (55%). Location was the neck in 3 (7%), body in 10 (22%), and tail in 7 (16%) patients. According to EUS-FNA/B results, 27 patients were grade 1 (G1) (61%); 12, grade 2 (G2) (27%); and 5, grade 3 (G3) (12%). In reference to the performance of AI for predicting the pathological grade, sensitivity was 94.29%; specificity, 97.14%; and accuracy, 96.19%. When the patient groups were subanalyzed as G1, G2, and G3 by the AI model to predict the pathological grade, the accuracy was as follows: for G1, 93.15%; for G2, 91.61%; and for G3, 98.05%. This pilot study suggests that pNET grade prediction can be reliably done on EUS images using AI-based technology.

Overview

  • The study aimed to estimate the grade of pancreatic neuroendocrine tumors (pNETs) using artificial intelligence (AI) via deep learning (DL) algorithms.
  • The study involved analyzing 803 EUS images from 44 patients who had a cyto/histo-pathologically confirmed diagnosis with EUS fine-needle aspiration or biopsy (FNA/B).
  • The primary objective of the study was to evaluate the performance of AI in predicting the pathological grade of pNETs using EUS images.

Comparative Analysis & Findings

  • The study found that the accuracy of AI in predicting the pathological grade of pNETs was 96.19%. The sensitivity was 94.29%, and the specificity was 97.14%.
  • The study also found that the accuracy of AI in predicting the pathological grade varied depending on the grade of the tumor, with an accuracy of 93.15% for grade 1 (G1), 91.61% for grade 2 (G2), and 98.05% for grade 3 (G3).
  • The study suggests that AI-based technology can be used reliably to predict the grade of pNETs on EUS images.

Implications and Future Directions

  • The study's findings have implications for the diagnosis and treatment of pNETs, as accurate grading of tumors is important for determining treatment options.
  • Future studies should investigate the use of AI in predicting the grade of pNETs in larger and more diverse populations.
  • Additional research is needed to evaluate the clinical utility of AI-based technology in predicting the grade of pNETs and to determine its potential for routine clinical use.
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