Clinical and translational gastroenterology · 2026
Automated Eosinophil Quantification Using Deep Learning to Predict Therapy Escalation in Pediatric Ulcerative Colitis.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Reigle James, Liu Xiaoxuan, Lopez-Nunez Oscar, Drysdale Erik, Qtaish Dua, Walters Thomas D, Mack David, Boyle Brendan M, Kugathasan Subra, Denson Nicholas, Erdman Lauren, Prasath Surya, Collins Margaret H, Hyams Jeffrey S, Denson Lee A, Griffiths Anne M, Siddiqui Iram, Dhaliwal Jasbir
Аннотация
INTRODUCTION: Emerging evidence implicates eosinophils as important modulators of disease activity and therapeutic response in ulcerative colitis (UC). Automated image analysis provides a scalable and reproducible approach to their evaluation, overcoming the limitations of manual quantification.
METHODS: We developed a U-Net-based deep learning model to automate eosinophil detection, using whole-slide images from 2 pediatric sites. Pathologist-annotated 500 × 500-pixel patches served as training data; model performance was evaluated using area under the receiver operator curve (AUROC), precision, recall, and F1 score and benchmarked against SAU-Net, YOLO-v8, and HoVer-Next. The validated algorithm was applied to rectal biopsies from treatment-naïve children with moderate-to-severe UC in the PROTECT inception cohort (n = 221). Eosinophil metrics (density in cells/mm 2 and peak high-power field counts) were analyzed for associations with baseline clinical, histologic, and endoscopic features and 1-year clinical outcomes including anti-tumor necrosis factor α (TNFα) escalation and corticosteroid-free clinical remission. Predictive models were evaluated with logistic regression and machine learning approaches using 5-fold cross-validation.
RESULTS: The segmentation model achieved high accuracy (test AUROC = 0.94, F1 score = 0.86) and strong concordance with expert counts (ρ = 0.87-0.89). Higher eosinophil density and counts correlated with basal lymphoid aggregates ( P < 0.001) and predicted week 4 and corticosteroid-free remission ( P = 0.03 and P = 0.01, respectively). Lower eosinophil counts predicted escalation to anti-TNFα therapy ( P = 0.04). Incorporation of eosinophil metrics into Predicting Response to Standardized Pediatric Colitis Therapy (PROTECT) clinical models maintained predictive accuracy for anti-TNFα escalation (AUROC = 0.79; accuracy = 0.76).
DISCUSSION: Deep learning-based eosinophil quantification has the potential to transform pediatric UC care by serving as a reproducible biomarker that informs risk stratification and to guide therapy.
METHODS: We developed a U-Net-based deep learning model to automate eosinophil detection, using whole-slide images from 2 pediatric sites. Pathologist-annotated 500 × 500-pixel patches served as training data; model performance was evaluated using area under the receiver operator curve (AUROC), precision, recall, and F1 score and benchmarked against SAU-Net, YOLO-v8, and HoVer-Next. The validated algorithm was applied to rectal biopsies from treatment-naïve children with moderate-to-severe UC in the PROTECT inception cohort (n = 221). Eosinophil metrics (density in cells/mm 2 and peak high-power field counts) were analyzed for associations with baseline clinical, histologic, and endoscopic features and 1-year clinical outcomes including anti-tumor necrosis factor α (TNFα) escalation and corticosteroid-free clinical remission. Predictive models were evaluated with logistic regression and machine learning approaches using 5-fold cross-validation.
RESULTS: The segmentation model achieved high accuracy (test AUROC = 0.94, F1 score = 0.86) and strong concordance with expert counts (ρ = 0.87-0.89). Higher eosinophil density and counts correlated with basal lymphoid aggregates ( P < 0.001) and predicted week 4 and corticosteroid-free remission ( P = 0.03 and P = 0.01, respectively). Lower eosinophil counts predicted escalation to anti-TNFα therapy ( P = 0.04). Incorporation of eosinophil metrics into Predicting Response to Standardized Pediatric Colitis Therapy (PROTECT) clinical models maintained predictive accuracy for anti-TNFα escalation (AUROC = 0.79; accuracy = 0.76).
DISCUSSION: Deep learning-based eosinophil quantification has the potential to transform pediatric UC care by serving as a reproducible biomarker that informs risk stratification and to guide therapy.