Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

Abstract

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

Keywords: Artificial Intelligence; Breast Neoplasms; Diagnostic Imaging; Pathology; Precision Medicine.

Figure 1. Diagnostic flow chart of breast cancer and application of artificial intelligence.

Potential application of AI in the diagnostic workflow of patients with breast cancer. IC = interval cancer; MMG = mammography; DBT = digital breast tomosynthesis; USG = ultrasonography; MRI = magnetic resonance imaging; LN = lymph node; SLNB = sentinel lymph node biopsy; W/U = workup; CR = complete remission; PR = partial response; SD = stable disease; PD = progressive disease; AI = artificial intelligence.

Figure 2. Various workflow scenarios for artificial intelligence usage in two-dimensional breast screening.

(A) Standard double reading with arbitration. (B) Standalone AI as a replacement of a second reader. (C) Concurrent reading by the second reader. (D) AI in a rule-out rule-in approach. AI = artificial intelligence.

Figure 3. Example of artificial intelligence application in two-dimensional breast mammography.

The figures show the original two views (LCC and LMLO), as well as the AI outputs generated using the Lunit INSIGHT MMG (Lunit Inc.). These AI outputs display abnormality scores to indicate a cancerous lesion and heat maps for localization. A density score was provided, according to the BI-RADS category on a scale of 1–10. LCC = left craniocaudal; LMLO = left mediolateral oblique; AI = artificial intelligence; MMG = mammography; BI-RADS = Breast Imaging Reporting and Data System.

Figure 4. Example of artificial intelligence application in whole slide images.

(A) PD-L1 IHC-stained WSI. (B) H&E-stained WSI. Both figures illustrate the original WSI at low and high magnification with the AI outputs using the Lunit SCOPE PD-L1 (Lunit Inc.) for (A) and Lunit SCOPE IO (Lunit Inc.) for (B). Each cell type (± PD-L1 positivity) identified by the AI model is represented by colored dots, while the AI-segmented areas are depicted with colored patches. PD-L1 = programmed cell death-ligand 1; IHC = immunohistochemistry; WSI = whole slide image; H&E = hematoxylin and eosin; AI = artificial intelligence; IO = immune-oncology.