Investigation of irradiated volume in linac-based brain hypo-fractionated stereotactic radiotherapy
Bibliographic record
Abstract
BACKGROUND: Emerging techniques such as brain hypo-fractionated radiotherapy (HF-RT) involve complex cases with limited guidelines for plan quality and normal tissue tolerances. The purpose of the present study was to statistically parameterize irradiated volume independently of dose prescription, or margin to determine what spread in achievable irradiated volume one may expect for a given case. METHODS: We defined EXT as the total tissue within the external contour of the patient (including the target) and we defined BMP as the contour of the brain minus PTV. Irradiated volumes of EXT and BMP at specific doses (i.e. 50, 60%, etc., of the prescribed dose) were extracted from 135 single-target HF-RT clinical cases, each planned with a single-arc, homogeneous (SAHO) approach in which target maximum dose (Dmax) was constrained to <130% of the prescribed dose. Irradiated volumes were subsequently measured for cases involving 2 targets (N = 29), 3 targets (N = 7) and >3 targets (N = 10) to investigate the effect of target number. We also examined the effect of shape complexity. A series of best fit curves with confidence and prediction intervals were generated for irradiated volume versus total target volume and the resulting model was subsequently validated on a subsequent set of 23 consecutive prospective cases not originally used in curve-fitting. A subset of 30 HF-RT cases were re-planned with a well-published four-arc, heterogeneous (FAHE) radiosurgery planning approach (Dmax could exceed 130%) to demonstrate how technique affects irradiated volume. RESULTS: > 0.98) was found for predicting irradiated volumes. For a given total target volume, irradiated-volume increased by a range of 1.4-2.9× for >3 versus single-targets depending on isodose level. Shape complexity had minor impact on irradiated volume. There was no statistical difference in irradiated volumes between validation and input data (p > 0.2). The FAHE-generated irradiated volumes yielded curves and prediction and confidence bands that agreed well with published data indicating that the proposed approach is feasible for cross-institutional comparisons. CONCLUSIONS: A description of irradiated volume for linac-based HF-RT is proposed based on population data. We have demonstrated that the proposed approach is feasible for inter and intra-institutional comparisons.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".