Immunotherapeutic Strategies for Intra-cranial Metastatic Melanoma - a Meta-analysis and Systematic Review
Bibliographic record
Abstract
Immune-activating anti-CTLA4 and anti-PD1 monoclonal antibodies (alone or in combination) are being used to treat advanced melanoma patients and can lead to durable remissions, and long-term overall survival may be achieved in between 50-60% of patients. Although intracranial metastases are very common in melanoma (about 50-75% of all patients with advanced disease), most of the pivotal prospective clinical trials exclude patients with intra-cranial metastases, certainly if their lesions are symptomatic and steroid-requiring and the degree of sensitivity of intra-cranial melanoma to immunotherapy remains uncertain, and requires further investigation especially in view of the demonstrable activity of RAF-MEK inhibitors in this clinical setting and the emergence of stereotactic radiotherapy. Our study aimed to evaluate the efficacy and toxicity of immunotherapy against advanced melanoma patients with brain metastases. In terms of comparative studies, only retrospective analyses could be identified. Based on 3 retrospective studies, treatment of patients with melanoma brain metastases with immunotherapeutic approaches improves overall survival substantially compared with supportive measures alone (no active anticancer treatment). The efficacy of targeted therapy appeared to be comparable to that of immune therapy in terms of overall survival, based on a small number of patients. The combination of concurrent radiation therapy to the brain and systemic immunotherapy led to improved overall survival compared to radiotherapy alone, suggesting potential synergism between the approaches, and combination treatment could be delivered safely. Our review supports the use of immunotherapeutic strategies for these patients although treatment efficacy appears to be lower for symptomatic lesions. In view of the extremely high efficacy of stereotactic radiotherapy approaches in the brain, understanding the interaction between radiotherapy and immunotherapy is vital and should be an area of active investigation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".