Late Gadolinium Hyperintensity of Suspected Colorectal Liver Metastases on Gadofosveset-Enhanced Magnetic Resonance Imaging: A Predictor of Benignity and a Potential Problem-Solving Tool
Bibliographic record
Abstract
PURPOSE: Late gadolinium hyperintensity (LGH) is sometimes seen in colorectal liver metastasis (CRLM) and represents a significant diagnostic pitfall due to overlap with LGH in benign hemangiomas; therefore, the objective of this study was to determine the prevalence of LGH and the ability of LGH to differentiate between CRLM and benign lesions with intravascular (gadofosveset) vs extracellular contrast agents (gadobutrol). METHODS: Patients with known colorectal cancer and suspected liver lesions were prospectively recruited into this institutional review board-approved, single institution study and received magnetic resonance imaging of the liver with gadofosveset and gadobutrol. The prevalence of LGH for CRLMs and solid benign lesions was determined. Receiver operating characteristics curves were determined for the presence of LGH as a predictor of benignity. The utility of LGH to differentiate between CRLM and solid benign lesions using gadofosveset vs gadobutrol was compared using the generalized estimating equation. RESULTS: Twenty-five patients with 131 solid focal liver lesions were recruited. The prevalence of LGH of CRLMs was 11.2% (95% confidence interval [CI]: 0.5%-21.8%) with gadofosveset vs 63.7% (95% CI: 45.7%-81.7%) with gadobutrol. The area under the receiver operating characteristic curve for the presence of LGH as a predictor of benignity was 0.86 using gadofosveset vs 0.75 using gadobutrol. Both LGH (P = .003) and the interaction of contrast agent and LGH (P = .003) statistically significantly differentiated CRLM from benign lesions. CONCLUSION: LGH is more common with extracellular than with intravascular contrast agents and is statistically significantly associated with benign lesions rather than metastases.
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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 | 0.005 |
| 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 | 0.000 | 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".