TY - JOUR
T1 - Differentiating cytology of pancreatic ductal adenocarcinoma and pancreatic neuroendocrine tumors by EUS-FNA through hyperspectral imaging technology combined with artificial intelligence
AU - Qin, Xianzheng
AU - Gao, Lili
AU - Wang, Kui
AU - Ran, Taojing
AU - Pan, Yundi
AU - Deng, Yingjiao
AU - Xie, Xingran
AU - Zhang, Yao
AU - Gong, Tingting
AU - Zhang, Benyan
AU - Zhang, Ling
AU - Wang, Yan
AU - Li, Qingli
AU - Wang, Dong
AU - Zhou, Chunhua
AU - Zou, Duowu
N1 - Publisher Copyright:
© The Author(s), 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
PY - 2026/1/1
Y1 - 2026/1/1
N2 - Background: Pancreatic cancer is a common and lethal malignancy, with the two primary subtypes being pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine tumors (pNET). Accurate diagnosis and effective treatment are crucial. Hyperspectral imaging (HSI) is a novel optical diagnostic technology that can capture spectral features inaccessible by traditional imaging techniques. With the aid of artificial intelligence (AI), HSI can provide richer information. Objectives: This study aims to develop a convolutional neural network (CNN) based on HSI to assist in the diagnosis of liquid-based cytology (LBC) specimens of PDAC and pNET obtained by endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA). Design: We designed a deep learning model using HSI data to differentiate between PDAC and pNET specimens. The CNN model was developed and evaluated using a dataset of LBC slides. Methods: During the EUS-FNA procedure, we prepared LBC slides of PDAC and pNET specimens. These slides were scanned using HSI technology to acquire both spectral and spatial information. We employed a modified ResNet18 model to analyze this information and perform classifications. In addition, we used attribute-guided factorization visualization (AGF-visualization) to visualize the CNN’s decision-making process. Results: Based on samples from 59 patients, 2014 HSI images were acquired. The spectral curves of PDAC and pNET cells exhibited recognizable differences in the wavelength range of 520–600 nm. Our modified ResNet18 model processes images at approximately 9 images/s and achieves a sensitivity of 90.80%, a specificity of 94.68%, and an accuracy rate of 92.82% (area under the receiver operating characteristic curve = 0.9721). AGF-visualization confirmed that our CNN model classifies based on the features of the tumor cell nucleus. Conclusion: Our HSI-CNN model accurately differentiates PDAC and pNET in EUS-FNA specimens, aiding pathologists in diagnosis and reducing their workload.
AB - Background: Pancreatic cancer is a common and lethal malignancy, with the two primary subtypes being pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine tumors (pNET). Accurate diagnosis and effective treatment are crucial. Hyperspectral imaging (HSI) is a novel optical diagnostic technology that can capture spectral features inaccessible by traditional imaging techniques. With the aid of artificial intelligence (AI), HSI can provide richer information. Objectives: This study aims to develop a convolutional neural network (CNN) based on HSI to assist in the diagnosis of liquid-based cytology (LBC) specimens of PDAC and pNET obtained by endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA). Design: We designed a deep learning model using HSI data to differentiate between PDAC and pNET specimens. The CNN model was developed and evaluated using a dataset of LBC slides. Methods: During the EUS-FNA procedure, we prepared LBC slides of PDAC and pNET specimens. These slides were scanned using HSI technology to acquire both spectral and spatial information. We employed a modified ResNet18 model to analyze this information and perform classifications. In addition, we used attribute-guided factorization visualization (AGF-visualization) to visualize the CNN’s decision-making process. Results: Based on samples from 59 patients, 2014 HSI images were acquired. The spectral curves of PDAC and pNET cells exhibited recognizable differences in the wavelength range of 520–600 nm. Our modified ResNet18 model processes images at approximately 9 images/s and achieves a sensitivity of 90.80%, a specificity of 94.68%, and an accuracy rate of 92.82% (area under the receiver operating characteristic curve = 0.9721). AGF-visualization confirmed that our CNN model classifies based on the features of the tumor cell nucleus. Conclusion: Our HSI-CNN model accurately differentiates PDAC and pNET in EUS-FNA specimens, aiding pathologists in diagnosis and reducing their workload.
KW - artificial intelligence
KW - endoscopic ultrasound-guided fine-needle aspiration
KW - hyperspectral imaging
KW - pancreatic ductal adenocarcinoma
KW - pancreatic neuroendocrine tumor
UR - https://www.scopus.com/pages/publications/105027575980
U2 - 10.1177/17562848251414188
DO - 10.1177/17562848251414188
M3 - 文章
AN - SCOPUS:105027575980
SN - 1756-283X
VL - 19
JO - Therapeutic Advances in Gastroenterology
JF - Therapeutic Advances in Gastroenterology
ER -
