MLTI-14. A SYSTEMATIC REVIEW OF TREATMENT PARADIGMS FOR PATIENTS WITH BREAST CANCER AND ONE OR MORE BRAIN METASTASES
Bibliographic record
Abstract
Abstract BACKGROUND: Upwards of 50% of patients with advanced breast cancer are diagnosed with brain metastases (BM). Treatment options for these patients have been rapidly evolving due to increased understanding of the tumor pathophysiology and its genetic underpinnings. This systematic review of randomized controlled trials (RCTs) aims to clarify the evidence guiding the treatment of brain metastases from breast cancer. METHODS: MEDLINE, EMBASE, Cochrane Controlled Register of Trials, ClincialTrials.gov, and Web of Science were searched from inception to October 2018 for RCTs comparing treatments for breast cancer BM. We screened studies, extracted data, and assessed risk of bias independently and in duplicate. Outcomes assessed were overall survival (OS), progression-free survival (PFS), and adverse events (Grade 3+). RESULTS: Among 3188 abstracts, only 3 RCTs (N=412; mean sample size per group N=54.7) meeting inclusion criteria were identified. The studies were phase II or III open-label parallel superiority trials. Inclusion criteria among these trials consisted of age >18 with radiologic evidence of >1 BM. Exclusion criteria consisted of poor-performance functional status (ECOG >2 or KPS < 70). The treatment groups included whole-brain radiation therapy (WBRT) vs WBRT + Temozolomide, WBRT vs WBRT + Efaproxiral, and Afatinib vs Vinorelbine vs investigators’ choice (86% of these patients received WBRT or SRS prior to study enrolment). While two trials found no significant difference in OS, one trial found significant improvement in OS with Efaproxiral in addition to WBRT compared to WBRT alone (HR 0.52; 95%CI 0.332–0.816). No significant differences were found with PFS or rate of adverse events amongst treatment groups. CONCLUSION: Considering the high prevalence of breast cancer BM and our improved understanding of genomic/molecular features of these tumors, a greater number of RCTs dedicated at this disease are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".