Delayed MRI Enhancement of Colorectal Cancer Liver Metastases Is Associated With Metastatic Mutational Profile
Bibliographic record
Abstract
BACKGROUND/AIM: Individual tumor genomics plays a key role in determining patient prognosis, response to chemotherapy and in guiding therapy. In prior studies, it was shown that the degree of late enhancement of colorectal liver metastases (CRCLM) target tumor enhancement (TTE) as seen on magnetic resonance imaging (MRI) was associated with overall survival. In order to better understand the relationship between MRI enhancement and survival, the aim of this study was to characterize genomic profiles of tumors clustered by MRI TTE, and investigate the association between TTE and genetic mutations. MATERIALS AND METHODS: Matched tumor and normal tissue samples from patients with weak TTE and strong TTE were analyzed by Next-generation sequencing (NGS) technology using a custom colorectal cancer panel. RESULTS: We discovered a total of 42 non-synonymous somatic mutations from 10 patients with weak TTE and 26 with 10 patients with strong TTE. Adenomatosis Polyposis Coli (APC) was the most commonly altered gene, 18 of those APC mutations were found in the weak TTE and 9 in the strong TTE group. CONCLUSION: An association exists between TTE and mutational status of CRCLM, which may offer some explanation as to why TTE is associated with overall survival in patients with CRCLM.
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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 | 0.000 | 0.001 |
| 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".