CNSM-05 CENTRAL NERVOUS TUMORS METASTASIS: CLINICAL EXPERIENCE IN THE SINGLE RADIOTHERAPY UNIT IN THE DR CONGO
Bibliographic record
Abstract
Abstract Introduction Central Nervous system metastasis constitutes a healthcare challenge globally and the burden is especially pronounced in Low-, Middle- Income Countries (LMICs) where most of the cancer patients present with advanced disease. Radiotherapy is still a huge challenge in LMICs despite its role for local control. This study assesses the pattern of patients referred to a radiotherapy unit in the DRCongo for local control of brain metastasis the last two years. Methodology The REB approved retrospective study included 19 with CNS metastasis referred to the radiotherapy Unit of Nganda Center Hospital for palliative radiotherapy from June 2022 to December 2024. Clinical information was collected from the archive. Results The patients’age ranged from 38 to 74 years old, the median follow up of 138 days (51-235), female patients being more affected than male (74 vs 26 %), the majority had access either to MRI or CT scan imaging before treatment (58% and 39 % respectively). The most frequent histological subtype tumor was invasive ductal carcinoma (IDC) of the breast in 68% (Hormone receptor positive IDC 40%, HER2-positive IDC 30% and Triple negative IDC 30%) followed by non-small cell lung cancer (15%) then small cell lung cancer, renal cell carcinoma and melanoma. Radiotherapy (RT) was given as palliative treatment to the whole brain (74%), hippocampal sparing RT (11%) and stereotactic RT (15%); the mean duration of treatment was 12.28±6.27 days (95% CI of the Mean 9.16-15.39). The number of fractions ranged between 4.00-10.00 and dose per fraction between 3.00-5.00. The median progression free survival was 4 months (SE 0.6, 95% CI of the Median, 2.79-5.20). Conclusion This retrospective study shows the reality of CNS metastasis and the fundamental role of palliative radiotherapy for local control of the brain metastasis in the LMICs where the limited access to this technology is still noted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".