An evaluation of rectum contours generated by artificial intelligence automatic contouring software using geometry, dosimetry and predicted toxicity
Bibliographic record
Abstract
Abstract Objective . This study assesses rectum organ at risk contours generated using a commercial deep learning auto-contouring model and compares them to clinician contours using geometry, changes in dosimetry and toxicity modelling. Approach. This retrospective study involved 308 prostate cancer patients who were treated using 3D-conformal radiotherapy. Computed tomography images were input into Limbus Contour (v1.8.0b3) to generate auto-contour structures for each patient. Auto-contours were not edited after their generation. Rectum auto-contours were compared to clinician contours geometrically and dosimetrically. Dice similarity coefficient (DSC), mean Hausdorff distance (HD) and volume difference were assessed. Dose-volume histogram (DVH) constraints (V41% - V100%) were compared, and a Wilcoxon signed-rank test was used to evaluate statistical significance of differences. Toxicity modelling to compare contours was carried out using equivalent uniform dose (EUD) and clinical factors of abdominal surgery and atrial fibrillation. Trained models were tested (80:20) in their prediction of grade 1 and above late rectal bleeding (n total = 124) using area-under the receiver operating characteristic curve (AUC). Main results . Median DSC (interquartile range (IQR)) was 0.85 (0.09), median HD was 1.38 mm (0.60 mm) and median volume difference was -1.73 cc (14.58 cc). Median DVH differences between contours were found to be small (<1.5%) for all constraints although systematically larger than clinician contours (p < 0.05). However, an IQR up to 8.0% was seen for individual patients across all dose constraints. Models using EUD alone derived from clinician or auto-contours had AUCs of 0.60 (0.10) and 0.60 (0.09). AUC for models involving clinical factors and dosimetry was 0.65 (0.09) and 0.66 (0.09) when using clinician contours and auto-contours. Significance. Although median DVH metrics were similar, variation for individual patients highlights the importance of clinician review. Rectal bleeding prediction accuracy did not depend on the contour method for this cohort. The auto-contouring model used in this study shows promise in a supervised workflow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".