Identifying the optimal time point for adaptive re-planning in prostate cancer radiotherapy to minimise rectal toxicity using normal tissue imaging biomarkers
Bibliographic record
Abstract
Background and purpose: Adaptive radiotherapy (ART) in prostate cancer (PCa), although not yet standard practice, is typically triggered by inter-fractional anatomical changes that emerge progressively during treatment. This study investigates whether radiomics extracted before and during treatment can identify the optimal time point for re-planning, with the goal of reducing late rectal bleeding. Materials and methods: This study included 187 PCa patients from the single-centre, prospectively collected VoxTox dataset (UK-CRN-ID-13716), treated with image-guided radiotherapy using TomoTherapy and daily MVCT. Patients received either 74 Gy in 37 fractions (N = 110) or 60 Gy in 20 fractions (N = 77). Radiomic features were extracted from pre-treatment planning CTs and daily MVCTs. Grade ≥ 1 rectal bleeding was assessed at 2 years post-treatment using CTCAE v4.03. Two analysis strategies were employed: a separate analysis, where weekly features were evaluated independently; and a cumulative analysis, which progressively incorporated features from previous weeks. Logistic regression models with elastic net penalty were trained and evaluated using AUC. Results: In both groups, week 1 provided the highest standalone predictive performance (test AUC = 0.766 for 74 Gy; 0.734 for 60 Gy). In the cumulative analysis, week 3 was optimal for the 74 Gy group (test AUC = 0.767), balancing performance and timing. For the 60 Gy group, week 1 remained optimal but suffered from reduced generalisability (test AUC = 0.643). Conclusions: Radiomic analysis of daily imaging can support early, proactive ART in PCa, offering a personalised strategy to reduce late rectal bleeding beyond conventional anatomy-based approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".