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Record W4414786745 · doi:10.1093/neuonc/noaf193.353

P11.14.A STEREOTACTIC RADIOSURGERY FOR OVARIAN CANCER BRAIN METASTASES: AN INTERNATIONAL RADIOSURGERY RESEARCH FOUNDATION RETROSPECTIVE STUDY

2025· article· en· W4414786745 on OpenAlexaff
D Mathieu, Mathilde Djeneba Billau, Antoine Hamel, Elizabeth Adam, Christian Iorio‐Morin, Jaromír May, Zheng Wei, L. Dade Lunsford, Diego D. Luy, São José, Samantha Scanlon, Joshua B. Silverman, Russell Mullen, Kenneth Bernstein, Douglas Kondziolka, Selçuk Peker, Yavuz Samanci, Ali Haluk Düzkalır, Steve Braunstein, Christina Phuong, Jason Sheehan, Styllianos Pikis, Jacob Kosyakovsky, Rajendra Prasad, Joshua D. Palmer, David M. Bailey, Brad E. Zacharia, Christopher P. Cifarelli, Daniel T. Cifarelli, Denisse Arteaga Icaza, Rodney E. Wegner, M J Shepard, Greg Bowden, Narine Wandrey, Chad G. Rusthoven, Eric Hintz, Michael Schulder, Anuj Goenka, Jeffrey J. Peterson

Bibliographic record

VenueNeuro-Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of AlbertaUniversité de Sherbrooke
Fundersnot available
KeywordsRadiosurgeryRetrospective cohort studyRadiation therapyProportional hazards modelOvarian cancerCancerBrain metastasisStage (stratigraphy)

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Ovarian cancer rarely metastasises to the brain, thus studies reporting the outcomes of affected patients are lacking. Stereotactic radiosurgery (SRS) is now the mainstay of management for patients with brain metastases (BM) from most primary sites, but little evidence of its efficacy in ovarian cancer is available. The current study was undertaken to provide guidance for SRS management using pooled data from multiple institutions. MATERIAL AND METHODS Centers participating in the IRRF (International Radiosurgery Research Foundation) were asked to provide outcome data for patients who had SRS for ovarian cancer brain metastases between 2020 and 2024 and at least one clinical and imaging follow-up after the procedure. Primary endpoints included survival from SRS, local tumor response according to RANO-BM criteria and occurrence of adverse radiation effects (ARE). Cox regression analyses were performed to identify variables impact each endpoint. RESULTS 128 patients had SRS for a total of 532 BM treated. Epithelial histology was the most common (91%). Median age at SRS was 62 years (IQR 56-70). Median KPS was 80% (IQR 80-90%) and 71.9% of patients had neurological symptoms at presentation. Other active systemic metastases were present in 41.4%. The median number of treated BM was 2 (IQR1-3) and the median cumulative treatment volume was 6 cc (IQR 2-12.9). The median margin dose was 18 Gy (IQR 16-20). At last follow-up, 19.5% of patients were still alive. The median overall survival (OS) after SRS was 27 months, and 6-, 12- and 24-month OS was 83.8%, 74.2% and 52.2%, respectively. Multivariate analyses revealed that increasing age at SRS (HR 1.02, p=0.05), active systemic metastases (HR 2, p=0.005) and increasing number of brain metastases (HR 1.05, p=0.04) were associated with worse survival, while repeating SRS (HR 0.4, p=0.002) led to improved survival. Local failure occurred in 12.8% of treated BM. Actuarial progression-free survival (PFS) at 6, 12 and 24 months was 92.3%, 86.6% and 70.3%, respectively. Only prior WBRT (HR 4.36, p=0.03) led to worse local control on multivariate analyses. New remote BM appeared in 49.5% of patients and 14.5% suffered from leptomeningeal dissemination. ARE were seen in 12.1% of BM but were symptomatic in only 3.2%. On multivariate analyses, prior SRS (HR 3.13, p=0.002) was associated with increased risk of ARE. CONCLUSION Ovarian cancer brain metastases can be safely and effectively managed with SRS as the primary treatment modality in most patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.077
GPT teacher head0.434
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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