CT and 3 Tesla MRI in the TN Staging of Colon Cancer: A Prospective, Blind Study
Bibliographic record
Abstract
(1) Background: Computer tomography (CT) scanning is currently the standard method for staging of colon cancer; however, the CT based preoperative local staging is far from optimal. The purpose of this study was to investigate the sensitivity and specificity of magnetic resonance imaging (MRI) compared to CT in the T- and N-staging of colon cancer. (2) Methods: Patients underwent a standard contrast-enhanced CT examination. For the abdominal MRI scan, a 3 Tesla unit was used, including diffusion weighted imaging (DWI). Experienced radiologists reported the CT and MRI scans blinded to each other and the endpoint of the pathological report. (3) Results: From 2018 to 2021, 134 patients received CT and MRI scans. CT identified 118 of the 134 tumors, whereas MRI identified all tumors. For discriminating between stage T3ab and T3cd, the sensitivity of CT was 51.1% and of MRI 80.0% (p = 0.02). CT and MRI showed a sensitivity of 21.4% and 46.4% in detecting pT4 tumors and a specificity of 79.0% and 85.0%, respectively. (4) Conclusion: Compared to CT, the sensitivity of MRI was statistically significantly higher in staging advanced T3cd and T4 tumors. MRI has the potential to be used in the treatment planning of colon cancer.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".