Abstract
Diagnosis of breast preneoplastic and neoplastic lesions is difficult due to their similar morphology in breast biopsy specimens. To diagnose these lesions, pathologists perform immunohistochemical analysis and consult with expert breast pathologists. These additional examinations are time-consuming and expensive. Artificial intelligence (AI)-based image analysis has recently improved, and may help in ordinal pathological diagnosis. Here, we showed the significance of machine learning-based image analysis of breast preneoplastic and neoplastic lesions for facilitating high-throughput diagnosis. Images were obtained from normal mammary glands, hyperplastic lesions, preneoplastic lesions and neoplastic lesions, such as usual ductal hyperplasia (UDH), columnar cell lesion (CCL), ductal carcinoma in situ (DCIS), and DCIS with comedo necrosis (comedo DCIS) in breast biopsy specimens. The original enhanced convoluted neural network (CNN) system was used for analyzing the pathological images. The AI-based image analysis provided the following area under the curve values (AUC): normal lesion versus DCIS, 0.9902; DCIS versus comedo DCIS, 0.9942; normal lesion versus CCL, 0.9786; and UDH versus DCIS, 1.000. Multiple comparison analysis showed precision and recall scores similar to those of single comparison analysis. Based on the gradient-weighted class activation mapping (Grad-CAM) used to visualize the important regions reflecting the result of CNN analysis, the ratio of stromal tissue in the whole weighted area was significantly higher in UDH and CCL than that in DCIS. These analyses may provide a more accurate and rapid pathological diagnosis of patients. Moreover, Grad-CAM identifies uncharted important histological characteristics for newer pathological findings and targets of research for understanding diseases.
Highlights
Breast cancer is a major cause of death in women [1,2]
Images were obtained from normal mammary glands, hyperplastic lesions, preneoplastic lesions and neoplastic lesions, such as usual ductal hyperplasia (UDH), columnar cell lesion (CCL), ductal carcinoma in situ (DCIS), and DCIS with comedo necrosis in breast biopsy specimens
The Artificial intelligence (AI)-based image analysis provided the following area under the curve values (AUC): normal lesion vs. DCIS, 0.9902; DCIS vs. comedo DCIS, 0.9942; normal lesion vs. CCL, 0.9786; and UDH vs. DCIS, 1.000
Summary
Breast cancer is a major cause of death in women [1,2]. Breast cancer treatments improve prognoses of patients with early stage disease, but patients with advanced stage still have poor prognosis [3,4,5]. Early diagnosis of non-invasive carcinoma or precancerous lesions is most important for preventing development of advanced breast cancer development. The most recent classification of breast ductal lesion is based on the differences in biological behavior from basic and clinical research outcomes, such as normal mammary ducts, hyperplastic lesions, precancerous lesions, and carcinomas [6,7]. These lesions differ in their proliferative behavior and genetic background [6,8], and a precise diagnosis of these lesions contributes to good clinical outcomes in the patients. Despite meticulous analysis, the final diagnosis of these lesions differs between pathologists because of the difficulty of morphological and immunohistochemical assessments [10,11]
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