A Review of the Neurosurgical Management of Brain Metastases During Pregnancy
Bibliographic record
Abstract
OBJECTIVE: Patients with pregnancy-associated secondary brain tumors (PASBT) are challenging to manage. Because no guidelines for the management of such patients currently exist, we performed a systematic review of the literature using PRISMA guidelines with a discussion of management from a neurosurgeon's perspective. METHOD: Systematic review of the literature using PRISMA guidelines from 1999 to 2018. RESULTS: We identified 301 studies of which 16 publications (22 patients reporting 25 pregnancies, 20 deliveries, 5 early terminations) were suitable for final analysis. The most frequent primary cancers were breast (8/22, 36.36%), skin (6/22, 27.27%), and lung (5/22, 22.73%). Four patients (18.18%) had neurosurgical procedures during their pregnancies. Five patients (22.73%) received neurosurgical resection after their pregnancies. Nine patients (40.91%) received radiation therapy and seven patients (31.82%) received chemotherapy during pregnancy while seven patients (31.82%) received chemotherapy and radiation after pregnancy. There was 1 fetal death (5%) out of 20 healthy deliveries. Five pregnancies (20%) were terminated in the first trimester due to a need for urgent neurosurgical intervention. CONCLUSION: Management of PASBT remains a challenging issue. Maternal and fetal risks associated with surgical resection and teratogenicity due to adjuvant therapy should be discussed in the context of a multidisciplinary team. Timing of surgery and the use of systemic chemoradiation depends on the gestational age (GA) of the fetus, extent, and control of the mother's primary and metastatic disease. Guidelines need to be established to help neuro-oncology teams safely and effectively manage this group of patients.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".