Artificial intelligence's impact on breast cancer pathology: a literature review

Abstract

This review discusses the profound impact of artificial intelligence (AI) on breast cancer (BC) diagnosis and management within the field of pathology. It examines the various applications of AI across diverse aspects of BC pathology, highlighting key findings from multiple studies. Integrating AI into routine pathology practice stands to improve diagnostic accuracy, thereby contributing to reducing avoidable errors. Additionally, AI has excelled in identifying invasive breast tumors and lymph node metastasis through its capacity to process large whole-slide images adeptly. Adaptive sampling techniques and powerful convolutional neural networks mark these achievements. The evaluation of hormonal status, which is imperative for BC treatment choices, has also been enhanced by AI quantitative analysis, aiding interobserver concordance and reliability. Breast cancer grading and mitotic count evaluation also benefit from AI intervention. AI-based frameworks effectively classify breast carcinomas, even for moderately graded cases that traditional methods struggle with. Moreover, AI-assisted mitotic figures quantification surpasses manual counting in precision and sensitivity, fostering improved prognosis. The assessment of tumor-infiltrating lymphocytes in triple-negative breast cancer using AI yields insights into patient survival prognosis. Furthermore, AI-powered predictions of neoadjuvant chemotherapy response demonstrate potential for streamlining treatment strategies. Addressing limitations, such as preanalytical variables, annotation demands, and differentiation challenges, is pivotal for realizing AI's full potential in BC pathology. Despite the existing hurdles, AI's multifaceted contributions to BC pathology hold great promise, providing enhanced accuracy, efficiency, and standardization. Continued research and innovation are crucial for overcoming obstacles and fully harnessing AI's transformative capabilities in breast cancer diagnosis and assessment.

Keywords: Artificial intelligence; Breast cancer; Digital pathology; Machine  learning.

Fig. 1

Automated lymph node metastasis detection using Visiopharm. A Downloading lymph node whole slide image metadata from image managing system; B Streaming the whole slide image in Visiopharm; C Analyzing lymph node metastasis using Visiopharm App; D demonstrating results with metastasis highlighted on image and measurement of the largest metastasis

Fig. 2

Representative images of breast lesions identified by the GALEN Breast (IBEX). A, B Invasive carcinoma; C, D Ductal carcinoma in situ; E, F Microcalcification. A, C, E original images; B, D, F Images with lesions highlighted by the GALEN Breast (heatmap)