AI in Breast Cancer Pathology: Comprehensive Review of Diagnostic, Grading, and Predictive Applications
This literature review surveys AI applications across the full spectrum of breast cancer pathology, from invasive tumour detection and lymph node metastasis identification to hormone receptor quantification, grading, mitotic counting, and neoadjuvant chemotherapy response prediction. While AI demonstrates improved accuracy and reproducibility over manual methods, the authors highlight unresolved challenges in pre-analytical variability, annotation requirements, and morphological differentiation.
The original study
Artificial intelligence's impact on breast cancer pathology: a literature review.
- Authors
- Soliman A, Li Z, Parwani AV
- Journal
- Diagnostic pathology
- Type
- Journal Article, Review
- PMID
- 38388367
Original 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.