Abstract
Background: The accurate determination of estrogen receptor (ER)-alpha and progesterone receptor (PR) status is critical for the prognostication and therapeutic management of breast carcinoma. Various diagnostic modalities are employed worldwide, yet comparative evidence on their accuracy, cost, and clinical applicability remains fragmented, particularly from a gynecologic oncology perspective.Methods: A systematic review was conducted following PRISMA guidelines. A comprehensive search across PubMed, Scopus, Web of Science, and Google Scholar databases, supplemented by WHO Cancer Profiles and National Cancer Registry data, identified 20 studies published between 2010 and 2025. After screening, 10studies
(including one meta-analysis) were selected for final analysis. Diagnostic modalities evaluated included core needle biopsy (CNB), fine-needle aspiration cytology with immunocytochemistry (FNAC-ICC), flow cytometry, [18F]FES-PET imaging, and artificial intelligence-based digital pathology. Sensitivity, specificity, cost, and clinical feasibility were comparatively analyzed.
Results: Core needle biopsy demonstrated the highest diagnostic performance, achieving sensitivities up to 97% and specificities nearing 100% for ER and PR detection, with an overall clinical efficacy exceeding 94%. FNAC-ICC offered a cost-effective alternative, demonstrating an overall efficacy of 85–90% for ER detection, although slightly lower sensitivity was observed for PR. Functional imaging with [18F]FES-PET exhibited 95% sensitivity for ER-positive metastases but was constrained by high cost and limited availability. Flow cytometry achieved moderate accuracy but lacked tissue architecture assessment. Emerging deep learning models on H&E slides demonstrated promising predictive accuracy (AUC 0.92) but remain largely experimental. Cost analysis positioned FNAC-ICC as the most affordable method per test, followed by CNB,
while [18F]FES-PET and AI approaches were the most resource-intensive .Conclusion: Core needle biopsy remains the gold standard for hormonal receptor status determination in breast carcinoma, offering high diagnostic accuracy, broad availability, and general clinical efficacy exceeding 94%. FNAC-ICC continues to serve as a pragmatic, affordable alternative with moderate-to-high efficacy, particularly valuable in resource-constrained settings. Advanced modalities such as [18F]FES-PET and deep learning models represent promising innovations but require further validation and infrastructure development. Tailoring diagnostic strategies to healthcare system capacities will be essential for optimizing breast cancer outcomes globally.