Purpose: This study aims to evaluate the impact of South Korea’s national insurance coverage (NIC) expansion and the addition of genetic counselors on BRCA1/2 mutation testing rates in breast cancer patients. Materials and Methods: A retrospective review was conducted at the Samsung Medical Center (SMC), dividing patients into three groups: pre-NIC expansion, post-NIC expansion, and post-extra genetic counselor involvement. The number of BRCA1/2 tests performed and the detection rates among newly diagnosed and follow-up patients, particularly focusing on triple-negative breast cancer (TNBC) cases, were analyzed. Results: Post-NIC expansion, there was a significant increase in BRCA1/2 testing rates, with a gradual rise in detection rates while maintaining statistical significance. TNBC patients under 60 experienced substantial increases in testing rates. The number of follow-up patients recalled for testing also rose significantly after the extra genetic counselor involvement. Additionally, NIC expansion increased insurance coverage for TNBC patients, enhancing accessibility to testing. Conclusion: The study highlights the positive impact of NIC expansion and genetic counselor involvement on BRCA1/2 mutation testing rates and subsequent patient management. Addressing financial barriers to testing and incorporating genetic counseling significantly improve patient outcomes. This model provides a potential strategy for enhancing early detection and personalized treatment for breast cancer patients with BRCA1/2 mutations, contributing to global cancer management efforts.

Shin DS, et al. J Breast Cancer. 2024 Jan;27:e3. https://doi.org/10.4048/jbc.2023.0196

Background: Pure mucinous breast carcinoma (PMBC) is an uncommon histologic subtype that typically exhibits favorable biology and infrequent HER2 overexpression. HER2-positive PMBC constitutes an exceptionally uncommon and biologically distinct subset, for which evidence regarding the response to neoadjuvant systemic therapy remains scarce. Case Presentation: A 49-year-old woman was diagnosed with cT2N0M0 HER2-positive pure mucinous breast carcinoma. She received six cycles of neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab. Post-treatment imaging demonstrated only modest tumor size reduction. Despite the limited radiographic response, breast-conserving surgery with sentinel lymph node biopsy revealed minimal residual invasive carcinoma measuring less than 1 mm, with the lesion predominantly composed of acellular extracellular mucin and negative lymph nodes, consistent with a near pathologic complete response. We aimed to report the therapeutic response in this rare subtype and to highlight potential discrepancies between radiologic and pathologic findings following neoadjuvant treatment. Conclusion: This case highlights a clinically significant discordance between radiologic findings and pathologic response in HER2-positive PMBC following neoadjuvant therapy. Persistent radiologic abnormalities may represent residual mucin rather than viable tumor cells, underscoring a potential limitation of imaging-based response evaluation. This report provides insight into response interpretation in this rare entity and supports the need for cautious clinical decision-making.

Objective: Accurate prediction of breast cancer (BC) recurrence after curative treatment is essential for optimizing surveillance strategies and personalized patient management. We aimed to prospectively validate a previously developed artificial intelligence (AI)–based BC recurrence prediction model in a real-world clinical setting.Methods: This prospective validation study included BC patients who underwent curative surgery followed by adjuvant therapy at a tertiary cancer center. The AI model integrated clinicopathologic variables and longitudinal follow-up data, including laboratory and imaging findings, to estimate individualized 2-year recurrence risk. Patients were stratified into low- and high-risk groups based on a predefined risk threshold. Model performance was evaluated using recurrence outcomes during follow-up.Results: Of 829 patients initially identified, 529 met eligibility criteria, and 400 were randomly selected. Among them, 391 were classified as low-risk and 9 as high-risk. Significant differences in clinicopathologic factors (T/N stage, ER, PR, HER2, Ki-67) were observed between groups, excluding age, adjuvant radiotherapy, and targeted therapy. During follow-up, 7 patients (1.8%) experienced recurrence, 6 in the low-risk group (1.5%) and 1 in the high-risk group (11.1%).Conclusion: This prospective validation demonstrates that the AI-based model can effectively identify BC patients at low risk of recurrence in a real-world clinical setting. The model shows potential as a clinical decision support tool to guide individualized surveillance strategies, although further multi-center studies with larger high-risk populations are warranted to enhance sensitivity and generalizability.

Objective: Accurate prediction of breast cancer (BC) recurrence after curative treatment is essential for optimizing surveillance strategies and personalized patient management. We aimed to prospectively validate a previously developed artificial intelligence (AI)-based breast cancer recurrence prediction model in a real-world clinical setting.Methods: This prospective real-world validation study included BC patients who underwent curative surgery followed by adjuvant therapy at a tertiary cancer center. The AI model integrated clinicopathologic variables and longitudinal follow-up data, including laboratory and imaging findings, to estimate individualized 2-year recurrence risk. Patients were stratified into low- and high-risk groups based on a predefined risk threshold. Model performance was evaluated using recurrence outcomes during follow-up.Results: Of 829 patients initially identified, 400 were included in the final analysis after applying the eligibility criteria. The model classified 391 patients (97.8%) as low risk and 9 (2.2%) as high risk. During the predefined two-year follow-up period, 7 patients (1.8%) developed recurrence, including 6 in the low-risk group (1.5%) and 1 in the high-risk group (11.1%). The model demonstrated excellent specificity (98.0%) and negative predictive value (98.5%) for identifying patients at low risk of recurrence.Conclusion: Prospective real-world validation demonstrated that the AI-based prediction model effectively identified patients at low risk of breast cancer recurrence. These findings support the clinical feasibility of AI-assisted recurrence risk stratification for personalized post-treatment surveillance, although further external validation in larger multi-center cohorts is warranted.