A Novel Three-Dimensional Imaging System Based on Polysaccharide Staining for Accurate Histopathological Diagnosis of Inflammatory Bowel Diseases

基于多糖染色的新型三维成像系统,用于炎症性肠病的准确组织病理学诊断

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作者:Satoshi Nojima, Shoichi Ishida, Kei Terayama, Katsuhiko Matsumoto, Takahiro Matsui, Shinichiro Tahara, Kenji Ohshima, Hiroki Kiyokawa, Kansuke Kido, Koto Ukon, Shota Y Yoshida, Tomoki T Mitani, Yuichiro Doki, Tsunekazu Mizushima, Yasushi Okuno, Etsuo A Susaki, Hiroki R Ueda, Eiichi Morii

Aims

Tissue-clearing and three-dimensional (3D) imaging techniques aid clinical histopathological evaluation; however, further methodological developments are required before use in clinical practice.

Background & aims

Tissue-clearing and three-dimensional (3D) imaging techniques aid clinical histopathological evaluation; however, further methodological developments are required before use in clinical practice.

Conclusions

PAFhy staining and PAFhy-3D imaging are promising approaches for next-generation experimental and clinical histopathology.

Methods

We sought to develop a novel fluorescence staining method based on the classical periodic acid-Schiff stain. We further attempted to develop a 3D imaging system based on this staining method and evaluated whether the system can be used for quantitative 3D pathological evaluation and deep learning-based automatic diagnosis of inflammatory bowel diseases.

Results

We successfully developed a novel periodic acid-FAM hydrazide (PAFhy) staining method for 3D imaging when combined with a tissue-clearing technique (PAFhy-3D). This strategy enabled clear and detailed imaging of the 3D architectures of crypts in human colorectal mucosa. PAFhy-3D imaging also revealed abnormal architectural changes in crypts in ulcerative colitis tissues and identified the distributions of neutrophils in cryptitis and crypt abscesses. PAFhy-3D revealed novel pathological findings including spiral staircase-like crypts specific to inflammatory bowel diseases. Quantitative analysis of crypts based on 3D morphologic changes enabled differential diagnosis of ulcerative colitis, Crohn's disease, and non-inflammatory bowel disease; such discrimination could not be achieved by pathologists. Furthermore, a deep learning-based system using PAFhy-3D images was used to distinguish these diseases The accuracies were excellent (macro-average area under the curve = 0.94; F1 scores = 0.875 for ulcerative colitis, 0.717 for Crohn's disease, and 0.819 for non-inflammatory bowel disease). Conclusions: PAFhy staining and PAFhy-3D imaging are promising approaches for next-generation experimental and clinical histopathology.

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