Data fusion for planning target volume and isodose prediction in prostate brachytherapy
Bibliographic record
Abstract
In low-dose prostate brachytherapy treatment, a large number of radioactive seeds is implanted in and adjacent to the prostate gland. Planning of this treatment involves the determination of a Planning Target Volume (PTV), followed by defining the optimal number of seeds, needles and their coordinates for implantation. The two major planning tasks, i.e. PTV determination and seed definition, are associated with inter- and intra-expert variability. Moreover, since these two steps are performed in sequence, the variability is accumulated in the overall treatment plan. In this paper, we introduce a model based on a data fusion technique that enables joint determination of PTV and the minimum Prescribed Isodose (mPD) map. The model captures the correlation between different information modalities consisting of transrectal ultrasound (TRUS) volumes, PTV and isodose contours. We take advantage of joint Independent Component Analysis (jICA) as a linear decomposition technique to obtain a set of joint components that optimally describe such correlation. We perform a component stability analysis to generate a model with stable parameters that predicts the PTV and isodose contours solely based on a new patient TRUS volume. We propose a framework for both modeling and prediction processes and evaluate it on a dataset of 60 brachytherapy treatment records. We show PTV prediction error of 10:02±4:5% and the V100 isodose overlap of 97±3:55% with respect to the clinical gold standard.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".