Digital health · 2026
Enhancing early prediction of pathological complete response in breast cancer using attention-based convolutional neural networks in digital pathology.
СтатьяНазвание и аннотация: Перевод в очереди
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Comes Maria Colomba, Lupo Andrea, Bozzi Arianna, Fanizzi Annarita, Cirillo Angelo, Nunzio Giorgio De, Pastena Maria Irene, Rizzo Alessandro, Guven Deniz Can, Vitale Elsa, Zito Francesco Alfredo, Bove Samantha, Massafra Raffaella
Аннотация
OBJECTIVE: To develop an attention-based convolutional neural network (CNN) pipeline for early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer, improving feature selection and interpretability in whole slide image (WSI) analysis.
METHODS: A retrospective analysis was conducted on 384,076 tiles extracted from 122 Hematoxylin and Eosin-stained WSIs, divided among an investigational cohort (IC, 82 patients enrolled at IRCCS Istituto Tumori "Giovanni Paolo II"), a validation cohort (VC, 20 patients, same Institution), and an external validation cohort (EVC, 20 patients belonging the Yale trastuzumab response cohort public dataset). WSIs were first annotated and then automatically segmented into tiles. Noninformative regions were filtered using Mini-Batch C-Fuzzy K-Means. Remaining tiles were analyzed using a CNN with a Convolutional Block Attention Module, prioritizing both histological features and tiles critical for predicting pCR.
RESULTS: The model achieved robust performance: 81.4% AUC, 81.3% accuracy, 80.0% specificity, and 83.3% sensitivity in IC; 80.9% AUC, 80.0% accuracy, 85.78% specificity, and 66.7% sensitivity in VC; and 76.2% AUC, 70.0% accuracy, 71.4% specificity, and 66.7% sensitivity in EVC. The EVC, consisting of WSIs at 20× magnification compared to the 40× magnification of IC and VC, demonstrated the model's robustness to varying resolutions.
CONCLUSION: This is an innovative pipeline that not only improves prediction but also enhances the clinical utility of digital pathology.
METHODS: A retrospective analysis was conducted on 384,076 tiles extracted from 122 Hematoxylin and Eosin-stained WSIs, divided among an investigational cohort (IC, 82 patients enrolled at IRCCS Istituto Tumori "Giovanni Paolo II"), a validation cohort (VC, 20 patients, same Institution), and an external validation cohort (EVC, 20 patients belonging the Yale trastuzumab response cohort public dataset). WSIs were first annotated and then automatically segmented into tiles. Noninformative regions were filtered using Mini-Batch C-Fuzzy K-Means. Remaining tiles were analyzed using a CNN with a Convolutional Block Attention Module, prioritizing both histological features and tiles critical for predicting pCR.
RESULTS: The model achieved robust performance: 81.4% AUC, 81.3% accuracy, 80.0% specificity, and 83.3% sensitivity in IC; 80.9% AUC, 80.0% accuracy, 85.78% specificity, and 66.7% sensitivity in VC; and 76.2% AUC, 70.0% accuracy, 71.4% specificity, and 66.7% sensitivity in EVC. The EVC, consisting of WSIs at 20× magnification compared to the 40× magnification of IC and VC, demonstrated the model's robustness to varying resolutions.
CONCLUSION: This is an innovative pipeline that not only improves prediction but also enhances the clinical utility of digital pathology.