Single-Fraction Stereotactic Radiosurgery Versus Hippocampal-Avoidance Whole Brain Radiation Therapy for Patients With 10 to 30 Brain Metastases: A Dosimetric Analysis
Bibliographic record
Abstract
PURPOSE: To compare normal tissue dosimetry between hippocampal-avoidance whole brain radiation therapy (HA-WBRT) and stereotactic radiosurgery (SRS) in patients with 10 to 30 brain metastases, and to describe a novel SRS strategy we term Spatially Partitioned Adaptive RadiosurgEry (SPARE). METHODS AND MATERIALS: A retrospective review identified SRS treatment plans with >10 brain metastases located >5 mm from the hippocampi. Our Gamma Knife Icon (GKI) SPARE (GKI-Spr) technique treats multiple metastases with single-fraction SRS partitioned over consecutive days while limiting the total treatment time to ≤60 minutes per day. Hippocampal and normal brain dosimetry were compared among GKI-Spr, single-fraction single-day GKI (GKI-Sfr), and 30 Gy in 10 fractions HA-WBRT. Dose metrics were converted to equivalent dose in 2 Gy fractions. RESULTS: Ten cases were analyzed. Compared with HA-WBRT, GKI-Spr significantly reduced the median equivalent dose in 2 Gy fractions hippocampal maximum point dose, mean dose, and dose to 40% of the hippocampi (D40%) by 86%, 93%, and 93%, respectively, and similarly for GKI-Sfr by 81%, 92%, and 91%, respectively. The normal brain median mean dose was reduced by 95% with GKI-Spr and 94% with GKI-Sfr. Compared with GKI-Sfr, GKI-Spr further reduced all normal brain and hippocampal dose metrics (P ≤ .014). CONCLUSIONS: GKI yields superior hippocampal and normal brain dosimetry compared with HA-WBRT, and GKI-Spr results in further dosimetric advantages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".