Dynamic Tumor Tracking (DTT) for Hepatocellular Carcinoma Using the Vero4DRT Gimbaled Linac Stereotactic Body Radiation Therapy (SBRT) System
Bibliographic record
Abstract
Background/Objectives: Stereotactic body radiation therapy (SBRT) is a therapeutic option for hepatocellular carcinoma (HCC). This study reviewed outcomes and toxicities of SBRT for HCC using a gimbal-mounted linear accelerator and real-time monitoring system. Methods: A single-institution, retrospective review of SBRT for HCC using DTT between January 2018 and December 2020 was undertaken. Endpoints included local control (LC) and overall survival (OS). Results: A total of 74 patients with 82 tumors treated were identified. Median follow-up was 40.8 months. LC at 1, 3, and 5 years was 89.6%, 71.0%, and 59.9%, respectively. Median time to local failure was not reached. Median OS was 41.3 months (95% CI 30.7–51.8 months). OS at 1, 3, and 5 years was 89.2%, 60.6%, and 33.9%, respectively. On UVA, GTV ≥ 30 cm3 (p = 0.038), and PTV ≥ 150 cm3 (p = 0.010) were associated with an absolute drop in platelet count by ≥50,000/mm3 within six weeks of SBRT, while prior focal liver treatment (p = 0.097) showed a trend toward significance. Underlying viral cirrhosis (p = 0.033), A6 or higher pre-SBRT Child–Pugh score (p = 0.010), and pre-SBRT platelet count <100,000/mm3 (p = 0.017) were significant for a rise in Child–Pugh score of 2 points or more, and the volume of liver-GTV <1000 cm3 (p = 0.093) approached significance. Conclusions: SBRT using DTT is an effective therapeutic option for selected patients with HCC, providing acceptable local control and toxicity.
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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 | 0.001 |
| 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) | 0.001 | 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".