Imaging Research on Gastrointestinal Disorders: Second Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 673

Special Issue Editor


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Guest Editor
Graduate School of Sciences and Technology, Tokushima University, Tokushima, Japan
Interests: gastrointestinal motility and function; gastrointestinal disorders; artificial inteligence; signal and image processing
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Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to the in-depth exploration of imaging studies of gastrointestinal diseases. Gastrointestinal diseases comprise a complex and diverse group of diseases of which the diagnosis and treatment require a comprehensive multidisciplinary approach. This Special Issue covers the imaging manifestations of various gastrointestinal diseases, including, but not limited to, inflammatory bowel disease, intestinal obstruction, irritable bowel syndrome, gastric cancer, colorectal cancer, etc. In addition, it will also delve into the specific applications of imaging in gastrointestinal diseases, including, but not limited to, CT, MRI, ultrasound, endoscopy, etc.

Overall, we aim to provide an all-round, multi-angle platform for readers to gain an in-depth understanding of the current status of and development trends in imaging research on gastrointestinal diseases. We look forward to your participation in and contribution to jointly promoting the advancement of imaging research on gastrointestinal diseases.

Dr. Takahiro Emoto
Guest Editor

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Keywords

  • gastrointestinal diseases
  • inflammatory bowel disease
  • intestinal obstruction
  • irritable bowel syndrome
  • gastric cancer
  • signal and imaging processing
  • visualization techniques for the gastrointestinal tract

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Published Papers (1 paper)

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Research

17 pages, 3545 KB  
Article
A Radiomics–Clinical Nomogram for Pre-Treatment Prediction of Neoadjuvant Chemotherapy Response in Locally Advanced Gastric Cancer
by Qianzheng Zhou, Jun Xu, Qiong Li, Fengyuan Li and Hao Xu
Diagnostics 2026, 16(6), 945; https://doi.org/10.3390/diagnostics16060945 - 23 Mar 2026
Cited by 1 | Viewed by 389
Abstract
Objective: To develop and evaluate a nomogram integrating radiomic features from contrast-enhanced CT with clinical variables for pre-treatment predictions of the response to neoadjuvant therapy (NAT) in locally advanced gastric cancer (LAGC). Methods: In this retrospective multicenter study, 183 LAGC patients from the [...] Read more.
Objective: To develop and evaluate a nomogram integrating radiomic features from contrast-enhanced CT with clinical variables for pre-treatment predictions of the response to neoadjuvant therapy (NAT) in locally advanced gastric cancer (LAGC). Methods: In this retrospective multicenter study, 183 LAGC patients from the First Affiliated Hospital of Nanjing Medical University (2014–2023) were included. Radiomic features were extracted from manually delineated pre-treatment CT regions of interest. A machine learning-based predictive model combining radiomic scores and clinical data was constructed. Model performance was assessed using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Results: Multivariate analysis identified the radiomic score, preoperative N stage, and neoadjuvant regimen as independent predictors of NAT responses (all p < 0.05). The integrated nomogram achieved an area under the ROC curve of 0.807 and showed a moderate net benefit in DCA compared with the radiomics-only model. Conclusions: The radiomics–clinical nomogram demonstrates moderate predictive performance for pre-treatment stratification of NAT responses in LAGC. These findings are exploratory and hypothesis-generating, and further validation in independent cohorts is required before clinical application. Full article
(This article belongs to the Special Issue Imaging Research on Gastrointestinal Disorders: Second Edition)
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