Pathologic Complete Response and Its Impact on Breast Cancer Recurrence and Patient’s Survival after Neoadjuvant Therapy: A Comprehensive Meta-Analysis

Objective. Earlier research has illustrated prognostic significance of pathologic complete response (pCR) in neoadjuvant therapy (NAT) for breast cancer, whereas correlation between treatment after achieving pCR and survival improvement remains underexplored. We attempted to measure the relation between pCR achieved after NAT and breast cancer recurrence or patient’s survival. Methods. We searched PubMed, EMBASE, Web of Science, and The Cochrane Library databases to find relevant articles from their inception to November 2020. According to eligibility criteria, studies were selected and basic data were extracted. The primary endpoint was the correlation between pCR achieved after NAT and event-free survival (EFS) or overall survival (OS). The results were obtained by directly extracting specific information from the literature or estimating individual data by survival curves on DigitizeIt software, presented with HR and 95% CI. All data were processed on Stata 14.0 software. Results. Among 4338 articles, there were 25 eligible articles involving 8767 patients. The EFS of patients achieved pCR after NAT improved obviously ( HR = 0.27 ; 95% CI, 0.24-0.31), especially in triple negative ( HR = 0.17 ; 95% CI, 0.12-0.24) and HER2 positive ( HR = 0.24 ; 95% CI, 0.20-0.30) breast cancer patients. As such, pCR after NAT was implicated in significantly increased OS ( HR = 0.32 ; 95% CI, 0.27–0.37). Conclusion. Achieving pCR after NAT was notably related to the improvement of EFS and OS, especially for patients with triple-negative and HER2-positive breast cancer. pCR can be a surrogate indicator for outcome of breast cancer patients after NAT, as well as a predictor of treatment efficacy after NAT. Besides, well-designed studies are still warranted for confirmation.

Privacy-preserving breast cancer recurrence prediction based on homomorphic encryption and secure two party computation

Protecting patients’ privacy is one of the most important tasks when developing medical artificial intelligence models since medical data is the most sensitive personal data. To overcome this privacy protection issue, diverse privacy-preserving methods have been proposed. We proposed a novel method for privacy-preserving Gated Recurrent Unit (GRU) inference model using privacy enhancing technologies including homomorphic encryption and secure two party computation. The proposed privacy-preserving GRU inference model validated on breast cancer recurrence prediction with 13,117 patients’ medical data. Our method gives reliable prediction result (0.893 accuracy) compared to the normal GRU model (0.895 accuracy). Unlike other previous works, the experiment on real breast cancer data yields almost identical results for privacy-preserving and conventional cases. We also implement our algorithm to shows the realistic end-to-end encrypted breast cancer recurrence prediction.

Rezidivrisiko durch hormonale Kontrazeptiva bei Brustkrebsüberlebenden überprüft

Ostroot MK et al. Breast cancer recurrence risk after hormonal contraceptive use in survivors of reproductive age. Eur J Obstet Gynecol Reprod Biol 2021; 258: 174–178. doi: 10.1016/j.ejogrb.2020.12.035