Pretreatment rectal tumors quantitative dynamic contrast-enhanced MRI characterization and predictability of outcome: Pilot study.
Bibliographic record
Abstract
416 Background: Treatment of rectal cancer is currently a one-size-fits-all approach. Unfortunately, some rectal tumors do not respond well to treatment and/or give rise to latent metastases. Therefore, a need exists for biomarkers which can better characterize tumor aggressiveness and help guide treatment decisions. We investigated the feasibility and performance of dynamic contrast-enhanced (DCE) MRI (performed pre-treatment) as a potential biomarker to predict rectal cancer response to treatment. Methods: 47 patients with rectal masses underwent DCE-MRI at 3T pre-treatment. DCE-MRI was performed with 3D FLASH (TR/TE/flip 6ms/1.6 ms/25 deg). Gadolinium concentration-versus-time in tissue was estimated with the aid of a variable-flip-angle T1 mapping procedure performed pre-DCE. The arterial input function was estimated by measuring phase-versus-time in major arteries. Tracer kinetic modeling was used to obtain maps of the quantitative perfusion parameters Ktrans and Kep. Treatment outcome was categorized as successful (regression) or unsuccessful (progression, stable, or recurrence). We also dichotomized latent metastases as absent or present. The pathology outcome of the lesions was categorized as cancerous or non-cancerous. A support vector machine classifier was constructed from the perfusion parameters, and the accuracy of each classifier was determined by 10-fold cross validation. Results: Clinical characteristics were 31 successful outcomes, 8 unsuccessful; 25 with latent metastases absent, 5 with latent metastases present; 40 cancerous, 7 non-cancerous. The accuracies of quantitative DCE-MRI parameters for predicting treatment outcome, latent metastases, and pathology outcome were 78%, 83%, and 84%, respectively. Conclusions: Pre-treatment DCE-MRI appears to be a useful predictor of rectal tumor malignancy, treatment outcome, and latent metastases. Perfusion parameters have very promising potential to predict rectal cancer outcome and may serve as an added tool for rectal cancer treatment planning.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".