ArticleTherapeutic advances in gastroenterology2026
Differentiating cytology of pancreatic ductal adenocarcinoma and pancreatic neuroendocrine tumors by EUS-FNA through hyperspectral imaging technology combined with artificial intelligence.
Article in Therapeutic advances in gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Pancreatic Endoscopic Ultrasonography: From Image-Based Diagnosis to Cytopathology.Journal of clinical medicine · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.