Tu1352 FULLY AUTOMATED HISTOLOGICAL CLASSIFICATION OF CELL TYPES AND TISSUE REGIONS OF CELIAC DISEASE IS FEASIBLE AND CORRELATES WITH THE MARSH SCORE

Griffin, Michael; Gruver, Aaron M.; Shah, Chintan; Wani, Qasim; Fahy, Darren; Khosla, Archit; Kirkup, Christian; Borders, Daniel; Brosnan-Cashman, Jacqueline A.; Fulford, Angie D.; Credille, Kelly M.; Jayson, Christina; Najdawi, Fedaa; Gottlieb, Klaus

doi:10.1016/s0016-5085(23)03385-1

Cited by 2 publications

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“…In summary, we describe a novel approach for using pathologist-trained machine learning classi ers for the assessment of celiac disease biopsies. While other viable approaches are examining the ability to classify disease features from H&E stained tissues [27], this method demonstrated the feasibility of customizing off-the-shelf, AI software to objectively quantify histopathologic features from routinely processed and immune-stained sections available in the standard clinical laboratory. Future work with larger cohorts will be needed to understand the impact of disease heterogeneity on classi er performance and explore model generalizability.…”

Section: Discussionmentioning

confidence: 99%

“…Recent ML applications have focused on quantitating image features to improve histology-based assessments, identifying the presence of tumor, predicting genetic status, and enhancing disease staging [17][18][19][20][21]. Although prior investigations have assessed computational approaches to the histologic diagnosis of celiac disease using H&E images [22][23][24][25][26][27], many of these appear to be driven by expertise in the domains of data science, computer programming, and AI engineering. To our knowledge, few have attempted to highlight the ability of pathologists to train, develop, and employ user-friendly ML classi ers to address the practical challenges of developing histology-based solutions despite the increased attention AI tools are receiving in pathology journals [14,16,28].…”

Section: Introductionmentioning

confidence: 99%

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Pathologist-Trained Machine Learning Classifiers Developed to Quantitate Celiac Disease Features Differentiate Endoscopic Biopsies According to Modified Marsh Score and Dietary Intervention Response

Gruver,

Lu,

Zhao

et al. 2023

Preprint

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Background Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e., Type 0-3c), significant variability has been documented between observers using this ordinal scoring system. Therefore, we evaluated whether pathologist-trained machine learning classifiers can be developed to objectively quantitate the pathological changes of villus blunting, intraepithelial lymphocytosis, and crypt hyperplasia in small intestine endoscopic biopsies. Methods A convolutional neural network (CNN) was trained and combined with a secondary algorithm to quantitate intraepithelial lymphocytes (IEL) with 5 classes on CD3 immunohistochemistry whole slide images (WSI) and used to correlate feature outputs with ground truth modified Marsh scores in a total of 116 small intestine biopsies. Results Across all samples, median %CD3 counts (positive cells/enterocytes) from villous epithelium (VE) increased with higher Marsh scores (Type 0 %CD3 VE=13.4; Type 1-3 %CD3 VE=41.9, p<0.0001). Indicators of villus blunting and crypt hyperplasia were also observed (Type 0-2 villous epithelium/lamina propria area ratio = 0.81; Type 3a-3c villous epithelium/lamina propria area ratio = 0.29, p<0.0001), and Type 0-1 crypt/villous epithelial area ratio = 0.59; Type 2-3 crypt/villous epithelial area ratio = 1.64, p<0.0001). Using these individual features, a combined feature machine learning score (MLS) was created to evaluate a set of 28 matched pre- and post-intervention biopsies captured before and after dietary gluten restriction. The disposition of the continuous MLS paired biopsy result aligned with the Marsh score in 96.4% (27/28) of the cohort. Conclusions Machine learning classifiers can be developed to objectively quantify histologic features and capture additional data not achievable with manual scoring. Such approaches should be further investigated to improve biopsy evaluation, especially for clinical trials.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pathologist-Trained Machine Learning Classifiers Developed to Quantitate Celiac Disease Features Differentiate Endoscopic Biopsies According to Modified Marsh Score and Dietary Intervention Response

Gruver,

Lu,

Zhao

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response

Gruver,

Lu,

Zhao

et al. 2023

Diagn Pathol

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Background Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e., Type 0-3c), significant variability has been documented between observers using this ordinal scoring system. Therefore, we evaluated whether pathologist-trained machine learning classifiers can be developed to objectively quantitate the pathological changes of villus blunting, intraepithelial lymphocytosis, and crypt hyperplasia in small intestine endoscopic biopsies. Methods A convolutional neural network (CNN) was trained and combined with a secondary algorithm to quantitate intraepithelial lymphocytes (IEL) with 5 classes on CD3 immunohistochemistry whole slide images (WSI) and used to correlate feature outputs with ground truth modified Marsh scores in a total of 116 small intestine biopsies. Results Across all samples, median %CD3 counts (positive cells/enterocytes) from villous epithelium (VE) increased with higher Marsh scores (Type 0%CD3 VE = 13.4; Type 1–3%CD3 VE = 41.9, p < 0.0001). Indicators of villus blunting and crypt hyperplasia were also observed (Type 0–2 villous epithelium/lamina propria area ratio = 0.81; Type 3a-3c villous epithelium/lamina propria area ratio = 0.29, p < 0.0001), and Type 0–1 crypt/villous epithelial area ratio = 0.59; Type 2–3 crypt/villous epithelial area ratio = 1.64, p < 0.0001). Using these individual features, a combined feature machine learning score (MLS) was created to evaluate a set of 28 matched pre- and post-intervention biopsies captured before and after dietary gluten restriction. The disposition of the continuous MLS paired biopsy result aligned with the Marsh score in 96.4% (27/28) of the cohort. Conclusions Machine learning classifiers can be developed to objectively quantify histologic features and capture additional data not achievable with manual scoring. Such approaches should be further investigated to improve biopsy evaluation, especially for clinical trials.

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Tu1352 FULLY AUTOMATED HISTOLOGICAL CLASSIFICATION OF CELL TYPES AND TISSUE REGIONS OF CELIAC DISEASE IS FEASIBLE AND CORRELATES WITH THE MARSH SCORE

Cited by 2 publications

References 0 publications

Pathologist-Trained Machine Learning Classifiers Developed to Quantitate Celiac Disease Features Differentiate Endoscopic Biopsies According to Modified Marsh Score and Dietary Intervention Response

Pathologist-Trained Machine Learning Classifiers Developed to Quantitate Celiac Disease Features Differentiate Endoscopic Biopsies According to Modified Marsh Score and Dietary Intervention Response

Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response

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