Pharmacotherapy for leptomeningeal disease in breast cancer
Bibliographic record
Abstract
Clinical data supporting the best therapeutic approach in leptomeningeal disease (LMD; also known as leptomeningeal metastases or leptomeningeal carcinomatosis) are lacking. Despite the development of new agents and increasing incidence of central nervous system metastases, patients with LMD are often excluded from clinical trials in breast cancer, with very few conducted specifically in LMD. Consequently, current evidence may not provide an accurate reflection of real-world clinical practice. This review aims to provide further insight into the treatment strategies for patients with breast cancer and LMD. We explore differences between clinical and real-world studies, considering inclusion criteria, levels of evidence for LMD diagnosis, and time between diagnosis of LMD and LMD-specific treatment initiation. Patient prognosis is poor; median overall survival is limited to several months, with approximately 10% of patients alive at 12 months. Efficacy results have been reported for various systemic and intrathecal agents in LMD to date. Systemic therapies under investigation for LMD in breast cancer include tucatinib, trastuzumab deruxtecan, and paclitaxel trevatide; trastuzumab is the main intrathecal agent currently under investigation. Recent trials investigating systemic or intrathecal therapies are typically small, single-arm studies, and most are restricted to patients with human epidermal growth factor receptor 2-positive breast cancer. Moreover, the variability among inclusion criteria and response assessment tools makes the interpretation of results difficult. Large retrospective cohorts with various inclusion criteria and treatment regimens provide some real-world data. However, there remains an urgent need for randomised clinical trials which include patients with LMD across all breast cancer subtypes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".