Magnetic Resonance Imaging for Breast Tumor Bed Delineation: Computed Tomography Comparison and Sequence Variation
Bibliographic record
Abstract
Purpose Our purpose was to investigate the interobserver variability in breast tumor bed delineation using magnetic resonance (MR) compared with computed tomography (CT) at baseline and to quantify the change in tumor bed volume between pretreatment and end-of-treatment MR for patients undergoing whole breast radiation therapy. Methods and Materials Forty-eight patients with breast cancer planned for whole breast radiation therapy underwent CT and MR (T1, T1 fat-suppression [T1fs], and T2) simulation in the supine treatment position before radiation therapy and MR (T1, T1fs, and T2) at the end of treatment in the same position. Two observers delineated 50 tumor beds on the CT and all MR sequences and assigned cavity visualization scores to the images. The primary endpoint was interobserver variability, measured using the conformity index (CI). Results The mean cavity visualization scores at baseline were 3.14 (CT), 3.26 (T1), 3.41 (T1fs), and 3.58 (T2). The mean CIs were 0.65, 0.65, 0.72, and 0.68, respectively. T1fs significantly improved interobserver variability compared with CT, T1, or T2 ( P < .001, P < .001, and P = .011, respectively). The CI for T1fs was significantly higher than T1 and T2 at the end of treatment (mean 0.72, 0.64, and 0.66, respectively; P < .001). The mean tumor bed volume on the T1fs sequence decreased from 18 cm 3 at baseline to 13 cm 3 at the end of treatment ( P < .01). Conclusions T1fs reduced interobserver variability on both pre- and end-of-treatment scans and measured a reduction in tumor bed volume during whole breast radiation therapy. This rapid sequence could be easily used for adaptive boost or partial breast irradiation, especially on MR linear accelerators.
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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 | 0.005 | 0.016 |
| 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".