Histopathologic Differential Diagnosis and Estrogen Receptor/Progesterone Receptor Immunohistochemical Evaluation of Breast Carcinoma Using a Deep Learning–Based Artificial Intelligence Architecture

Abstract

In breast carcinoma, invasive ductal carcinoma (IDC) is the most common histopathological subtype, and ductal carcinoma in situ (DCIS) is a precursor of IDC. They are often concomitant. The immunohistochemical staining of estrogen receptor (ER)/progesterone receptor (PR) in IDC/DCIS on whole-slide histopathological images (WSIs) can predict the prognosis of patients. However, the inter-observer variability among pathologists in reading WSIs is inevitable. Thus, artificial intelligence (AI) technology is crucial. Herein, IDC/DCIS detection was conducted by deep learning approach, including Faster R-CNN, RetinaNet, SSD300, YOLOv3, YOLOv5, YOLOv7, YOLOv8, and Swin transformer. Their performance was estimated by mean average precision (mAP) values. Cell recognition and counting were performed using AI technology to evaluate the intensity and proportion of ER/PR-immunostained cancer cells in IDC/DCIS. A three-round ring study (RS) was conducted to assess WSIs. A database for modelling the underlying probability distribution of a dataset with labels was established. YOLOv8 exhibits the highest detection performance with an mAP@0.5 of 0.944 and an mAP@0.5-0.95 of 0.790. With the assistance of YOLOv8, the scoring concordance across all pathologists was boosted to excellent in RS3 (0.970) from moderate in RS1 (0.724) and good in RS2 (0.812). Deep learning detection can be applied in clinicopathological field. To facilitate the histopathological diagnosis of IDC/DCIS and immunostaining scoring of ER/PR, a novel AI architecture and well-organized dataset were developed.