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Background: To investigate outcomes of salvage whole-brain radiotherapy (WBRT) for recurrent brain metastases (BM) from breast cancer (BC), to identify prognostic factors of overall survival (OS), and to propose a novel prognostic classification for OS in these patients. Materials and methods: We identified 54 patients who had received salvage WBRT as the second brain-focused treatment for recurrent BM from BC (2000–2014). The median follow-up duration was 4.9 months. A recursive partitioning analysis (RPA) was conducted to develop a model to predict OS at the time of salvage WBRT. Results: The median OS was 6.8 months. OS according to BC-specific graded prognostic assessment (breast-GPA), modified breast-GPA, and updated breast-GPA did not represent our cohort. In the multivariate analysis, a long time before salvage WBRT (≥16 months), control of primary BC or extracranial metastases, systemic treatment after salvage WBRT, and administration of a biologically effective dose for an α/β of 10 Gy (BED10) of salvage WBRT >37.5 Gy showed superior OS. We proposed three RPA classes based on the control of both primary BC and extracranial metastasis and BED10 of salvage WBRT: class I, class II, and class III. In this model, patients with class I experienced the best OS (34.6 months; class II, 5.0 months; class III, 2.4 months; P

Purpose: This study evaluated the influence of prognostic factors and whole brain radiotherapy (WBRT) on overall survival (OS) of breast cancer (BC) patients with brain metastases (BM). Methods and materials: Medical records of 730 BC patients diagnosed with BM from 2000 to 2014 at 17 institutions were retrospectively reviewed. OS was calculated from BM diagnosis. Median follow-up duration was 11.9 months (range, 0.1–126.2). Results: Median OS was 15.0 months (95% CI: 14.0–16.9). Patients with different BC-specific graded prognostic assessment (GPA) scores showed significant differences (p 4; p = 0.002), hormone receptor negativity (p = 0.005), HER2-negativity (p = 0.003), and shorter time interval (0.673) subgroups; the high-risk subgroup had poorer median OS (10.1 months, 95% CI: 7.9–11.9 vs. 21.9 months, 95% CI: 19.5–27.1, p

Purpose: To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potential IOV. Methods and materials: In phase 1, two breast cancer cases were randomly selected and distributed to multiple institutions for contouring six clinical target volumes (CTVs) and eight OAR. In Phase 2, auto-contour sets were generated using a previously published DL Breast segmentation model and were made available for all participants. The difference in IOV of submitted contours in phases 1 and 2 was investigated quantitatively using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). The qualitative analysis involved using contour heat maps to visualize the extent and location of these variations and the required modification. Results: Over 800 pairwise comparisons were analysed for each structure in each case. Quantitative phase 2 metrics showed significant improvement in the mean DSC (from 0.69 to 0.77) and HD (from 34.9 to 17.9 mm). Quantitative analysis showed increased interobserver agreement in phase 2, specifically for CTV structures (5–19 %), leading to fewer manual adjustments. Underlying IOV differences causes were reported using a questionnaire and hierarchical clustering analysis based on the volume of CTVs. Conclusion: DL-based auto-contours improved the contour agreement for OARs and CTVs significantly, both qualitatively and quantitatively, suggesting its potential role in minimizing radiation therapy protocol deviation.