Bridging the Gap: First Clinical Experience With Linear Accelerator (LINAC) Radiosurgery Using Automated HyperARC for Brain Metastasis in a Low-Resource Setting in Pakistan
Bibliographic record
Abstract
Introduction Brain metastasis is becoming increasingly common in cancer patients. Over the past decade, stereotactic radiosurgery (SRS) and fractionated stereotactic radiotherapy (fSRT) have become the cornerstone of treatment for limited brain metastases. Sophisticated linear accelerator (LINAC)-based HyperArc volumetric modulated arc therapy (HA-VMAT) systems offer the unique advantage of high-precision radiotherapy. The purpose of this study is to present our first clinical experience with the HA-VMAT planning approach for brain metastases treated at our hospital. Methods This retrospective study included 72 patients with 1-5 brain metastases treated at our institution with SRS/SRT using HA-VMAT. A dosimetric evaluation of clinical characteristics and outcomes was carried out. Results From April 2020 until June 2021, a total of 86 brain metastases in 72 patients were treated. The median age was 45 (range 18-75). The most common histology was breast cancer (n = 48 (66.7%)). Sixty-two (86.1%) patients had a single brain metastasis. Thirty-two (44%) patients were treated with single-fraction SRS, while 40 (65%) received fSRT. The radiobiological equivalent dose in 2 Gy fractions (EQD2) ranged from 50.4 to 81.6 Gy for SRS and from 37.25 to 60.0 Gy for fSRT. The median GTV and PTV volumes for a single fraction were 1.98 cc (range 0.13-13.3) and 4.49 cc (range 0.64-21.20), respectively. The mean PTV was 10.96 cc (range 2.25-34) and 19.04 cc (4.40-75.88). Complete response was observed in 12.5% of the patients. The median follow-up was 17 months (range 1-38 months). The progression-free survival at 1 year was 54.6%. Only one patient developed radiation necrosis. Conclusion Brain metastases can be safely and effectively treated with SRS/fSRT using HA-VMAT, which decreases the likelihood of local complications and improves local control.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".