AN AUTOMATED PIPELINE FOR TUMOUR-INFILTRATING LYMPHOCYTE SCORING IN BREAST CANCER

被引：2

作者：

Shephard, Adam J. ^{[1
]}

Jahanifar, Mostafa ^{[1
]}

Wang, Ruoyu ^{[1
]}

Dawood, Muhammad ^{[1
]}

Graham, Simon ^{[1
]}

Sidlauskas, Kastytis ^{[2
]}

Khurram, Syed Ali ^{[3
]}

Rajpoot, Nasir M. ^{[1
]}

Raza, Shan E. Ahmed ^{[1
]}

机构：

[1] Univ Warwick, Dept Comp Sci, Tissue Image Analyt Ctr, Coventry, England

[2] Queen Mary Univ London, Barts Canc Inst, London, England

[3] Univ Sheffield, Sch Clin Dent, Sheffield, England

来源：

IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI 2024 | 2024年

关键词：

Breast Cancer; Computational Pathology; TILs Detection; TILs Score; Histopathology;

D O I：

10.1109/ISBI56570.2024.10635302

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast cancer. In this study, we introduce an innovative deep learning pipeline based on the Efficient-UNet architecture to predict the TILs score for breast cancer whole-slide images (WSIs). We first segment tumour and stromal regions in order to compute a tumour bulk mask. We then detect TILs within the tumour-associated stroma, generating a TILs score by closely mirroring the pathologist's workflow. Our method exhibits state-of-the-art performance in segmenting tumour/stroma areas and TILs detection, as demonstrated by internal cross-validation on the TiGER Challenge training dataset (195 WSIs) and evaluation on the final leaderboards (38 WSIs). Additionally, our TILs score proves competitive in predicting survival outcomes within the same challenge (707 WSIs), underscoring the clinical relevance and potential of our automated pipeline as a breast cancer prognostic tool.

引用

页数：5

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