Evidence map›Paper›PMID 42861172›Full record

ArticleQuantitative imaging in medicine and surgery2026

Two parallel systems constructed from complementary optical coherence tomography and histopathology for the assessment of murine intestinal inflammation: a dual-modality assessment.

Liuliu Wei, Hanling Jiang, Zhongxu Wu, Yijuan Liu, Ying Deng, Boyang Huang, Qiukun Zhang, Lifang Lin, Yijuan Lin, Kaiyan Wei and 7 more

Abstract read
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Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

17 authors.

Liuliu WeiDepartment of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.ORCID https://orcid.org/0009-0001-7133-6391
Hanling JiangDepartment of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Zhongxu WuDepartment of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Yijuan LiuDepartment of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Ying DengDepartment of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Boyang HuangDepartment of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Qiukun ZhangSchool of Physics and Electronic Information Engineering, Minjiang University, Fuzhou, China.
Lifang LinDepartment of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Yijuan LinDepartment of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Kaiyan WeiDepartment of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Linying ChenDepartment of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Jinguo ChenSchool of Mechanical, Electrical and Information Engineering, Putian University, Putian, China.
Shuncong ZhongSchool of Physics and Electronic Information Engineering, Minjiang University, Fuzhou, China.
Zhongguang LuoDepartment of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Jie LiuDepartment of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Dan LiDepartment of Gastroenterology, Union Hospital, Fujian Medical University, Fuzhou, China.
Jian DingDepartment of Gastroenterology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.ORCID https://orcid.org/0000-0002-3132-0045

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Quantitative assessment of intestinal inflammation among patients with inflammatory bowel disease (IBD) remains a clinical challenge. Conventional histopathology lacks robust quantification, and a real-time, noninvasive alternative is needed. This study aimed to establish a quantitative histopathological system for IBD and to develop a corresponding optical coherence tomography (OCT) system for real-time detection. Methods: Murine models for both ulcerative colitis (UC) and Crohn disease (CD) were developed, and corresponding intestinal histopathological samples were obtained. Artificial intelligence-based image segmentation enabled systematic analysis of pathological images, including two-dimensional planar structures, three-dimensional volumetric morphology, texture features, and grayscale histograms, through which a comprehensive histopathological evaluation system for precise quantitative assessment of intestinal inflammation severity was established. Based on this framework, an OCT system was developed for intestinal wall structural analysis, which demonstrated high concordance with histopathological benchmarks while enabling real-time, noninvasive quantitative detection. Results: Both systems effectively differentiated UC, CD, and normal tissue. Planar histopathology revealed significant differences in these models in comparison to controls. The median mucosal fold angle was markedly elevated in UC [median 63.854°, interquartile range (IQR), 37.110-91.151°] and CD (49.925°, IQR, 34.140-62.550°) mice as compared controls (7.635°, IQR, 5.975-10.873°) (both P values <0.001). Submucosal area and cell count were higher in both disease groups, while the gland count was lower (all P values <0.05). Mucosal area ratio decreased from 73.608% in controls to 66.703% in UC mice (P=0.020) and to 59.182% in CD mice (P<0.001), whereas the submucosa ratio increased from 8.127% to 10.812% (P=0.031) and 16.076% (P<0.001), respectively. Three-dimensional reconstruction confirmed increased intestinal wall volumes in both disease groups (all P values <0.05). OCT texture analysis showed decreased energy and increased entropy in the UC and CD groups as compared to the control group (P<0.05), indicating disrupted microstructure. Gray-scale analysis revealed a shift toward lower values in diseased tissues (P<0.001). Pathological texture analysis demonstrated significantly elevated contrast in CD mice (median 153.425) as compared with UC (62.160) and control (70.495) mice (P<0.001), suggesting that CD and UC each have distinct textural signatures. Conclusions: This work establishes an integrated framework: a quantitative histopathological system providing novel diagnostic indices, and a complementary OCT system enabling real-time assessment. It offers a proof of concept for multimodal quantitative biomarkers in IBD, an objective tool for preclinical research, and a foundation for future OCT endoscopic applications in clinical evaluation.

Indexed as

chronic ulcerative colitisCrohn’s colitisInflammationmachine learningulcerative colitis

Identifiers

PMID42861172
PMCPMC13654094

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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.