International forum of allergy & rhinology · 2022
Deep learning-based prediction of treatment prognosis from nasal polyp histology slides.
Многоцентровое исследованиеСтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Wang Kanghua, Ren Yong, Ma Ling, Fan Yunping, Yang Zheng, Yang Qintai, Shi Jianbo, Sun Yueqi
Аннотация
BACKGROUND: Histopathology of nasal polyps contains rich prognostic information, which is difficult to extract objectively. In the present study, we aimed to develop a prognostic indicator of patient outcomes by analyzing scanned conventional hematoxylin and eosin (H&E)-stained slides alone using deep learning.
METHODS: An interpretable supervised deep learning model was developed using 185 H&E-stained whole-slide images (WSIs) of nasal polyps, each from a patient randomly selected from the pool of 232 patients who underwent endoscopic sinus surgery at the First Affiliated Hospital of Sun Yat-Sen University (internal cohort). We internally validated the model on a holdout dataset from the internal cohort (47 H&E-stained WSIs) and externally validated the model on 122 H&E-stained WSIs from the Seventh Affiliated Hospital of Sun Yat-Sen University and the University of Hong Kong-Shenzhen Hospital (external cohort). A poor prognosis score (PPS) was established to evaluate patient outcomes, and then risk activation mapping was applied to visualize the histopathological features underlying PPS.
RESULTS: The model yielded a patient-level sensitivity of 79.5%, and specificity of 92.3%, with areas under the receiver operating characteristic curve of 0.943, on the multicenter external cohort. The predictive ability of PPS was superior to that of conventional tissue eosinophil number. Notably, eosinophil infiltration, goblet cell hyperplasia, glandular hyperplasia, squamous metaplasia, and fibrin deposition were identified as the main underlying features of PPS.
CONCLUSIONS: Our deep learning model is an effective method for decoding pathological images of nasal polyps, providing a valuable solution for disease prognosis prediction and precise patient treatment.
METHODS: An interpretable supervised deep learning model was developed using 185 H&E-stained whole-slide images (WSIs) of nasal polyps, each from a patient randomly selected from the pool of 232 patients who underwent endoscopic sinus surgery at the First Affiliated Hospital of Sun Yat-Sen University (internal cohort). We internally validated the model on a holdout dataset from the internal cohort (47 H&E-stained WSIs) and externally validated the model on 122 H&E-stained WSIs from the Seventh Affiliated Hospital of Sun Yat-Sen University and the University of Hong Kong-Shenzhen Hospital (external cohort). A poor prognosis score (PPS) was established to evaluate patient outcomes, and then risk activation mapping was applied to visualize the histopathological features underlying PPS.
RESULTS: The model yielded a patient-level sensitivity of 79.5%, and specificity of 92.3%, with areas under the receiver operating characteristic curve of 0.943, on the multicenter external cohort. The predictive ability of PPS was superior to that of conventional tissue eosinophil number. Notably, eosinophil infiltration, goblet cell hyperplasia, glandular hyperplasia, squamous metaplasia, and fibrin deposition were identified as the main underlying features of PPS.
CONCLUSIONS: Our deep learning model is an effective method for decoding pathological images of nasal polyps, providing a valuable solution for disease prognosis prediction and precise patient treatment.