RADT-09. TYPE AND TIMING OF SYSTEMIC THERAPY USE PREDICT SURVIVAL IN PATIENTS WITH BRAIN METASTASES TREATED WITH RADIATION THERAPY
Bibliographic record
Abstract
Abstract PURPOSE We aimed to investigate whether systemic therapy (ST) use around the time of brain radiotherapy (RT) predicts overall survival for patients with brain metastases (BM). We also aimed to validate the Diagnosis-Specific Graded Prognostic Assessment (DS-GPA) in a population-based cohort. METHODS We used provincial RT and pharmacy databases to retrospectively review all adult patients in British Columbia, Canada, who received a first course of RT for BMs between 2012 and 2016. We used a randomly selected subset with complete baseline data to develop a multivariate analysis (MVA)-based nomogram including ST use to predict survival after RT and to validate the DS-GPA. RESULTS In our 3095-patient cohort, the median overall survival (OS) of the 999 recipients of ST after RT was 5.0 months (CI 4.1-6.0) longer than the OS of the 2096 non-recipients of ST after RT (p< 0.0001): targeted therapy (HR 0.42, CI 0.37-0.48), hormone therapy (HR 0.45, CI 0.36-0.55) and cytotoxic chemotherapy (HR 0.71, CI 0.64-0.79). The OS of patients who discontinued ST after RT was 0.9 months (CI 0.3-1.4) shorter than the OS of those who did not receive ST before nor after RT (p< 0.0001). A MVA in the 200-patient subset demonstrated that the traditional baseline variables: cancer diagnosis, age, performance status, presence of extracranial disease, and number of BMs predicted survival, as did the novel variables: ST use before RT and ST use after RT. The MVA-based nomogram had a bootstrap-corrected Harrell’s Concordance Index of 0.70. In the 179 patients within this subset with DS-GPA-compatible diagnoses, the DS-GPA overestimated OS by 6.3 months (CI 5.3- 9.8) (p= 0.0006). CONCLUSIONS The type and timing of ST use around RT predict survival for patients with BMs. A novel baseline variable “ST planned after RT” should be prospectively collected to validate these findings in other cohorts.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".