Quantifying Intra- and Inter-Observer Variabilities in Manual Contours for Radiotherapy: Evaluation of an MR Tumor Autocontouring Algorithm for Liver, Prostate, and Lung Cancer Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Real-time tumor-tracked radiotherapy with a linear accelerator-magnetic resonance (linac-MR) hybrid system requires accurate tumor delineation at a fast MR imaging rate. Various autocontouring methods have been previously evaluated against “gold standard” manual contours by experts. However, manually drawn contours have inherent intra- and inter-observer variations. We aim to quantify these variations and evaluate our tumor-autocontouring algorithm against the manual contours. Ten liver, ten prostate, and ten lung cancer patients were scanned using a 3 tesla (T) magnetic resonance imaging (MRI) scanner with a 2D balanced steady-state free precession (bSSFP) sequence at 4 frames/s. Three experts manually contoured the tumor in two sessions. For autocontouring, an in-house built U-Net-based autocontouring algorithm was used, whose hyperparameters were optimized for each patient, expert, and session (PES). For evaluation, (A) Automatic vs. Manual and (B) Manual vs. Manual contour comparisons were performed. For (A) and (B), three types of comparisons were performed: (a) same expert same session, (b) same expert different session, and (c) different experts, using Dice coefficient (DC), centroid displacement (CD), and the Hausdorff distance (HD). For (A), the algorithm was trained using one expert’s contours and its autocontours were compared to contours from (a)–(c). For Automatic vs. Manual evaluations (Aa–Ac), DC = 0.91, 0.86, 0.78, CD = 1.3, 1.8, 2.7 mm, and HD = 3.1, 4.6, 7.0 mm averaged over 30 patients were achieved, respectively. For Manual vs. Manual evaluations (Ba–Bc), DC = 1.00, 0.85, 0.77, CD = 0.0, 2.1, 2.8 mm, and HD = 0.0, 4.9, 7.2 mm were achieved, respectively. We have quantified the intra- and inter-observer variations in manual contouring of liver, prostate, and lung patients. Our PES-specific optimized algorithm generated autocontours with agreement levels comparable to these manual variations, but with high efficiency (54 ms/autocontour vs. 9 s/manual contour).
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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 | 0.001 | 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 it