Detection of cholangiocarcinoma with magnetic resonance spectroscopy of bile in patients with and without primary sclerosing cholangitis
Bibliographic record
Abstract
BACKGROUND: Early detection of cholangiocarcinoma (CC) is very difficult, especially in patients with primary sclerosing cholangitis (PSC) who are at increased risk of developing CC. PURPOSE: To evaluate 1H magnetic resonance spectroscopy ((1)H-MRS) of bile as a diagnostic marker for CC in patients with and without PSC. MATERIAL AND METHODS: The institutional review board approved the study, and all patients gave informed consent. Bile from 49 patients was sampled and investigated using 1H-MRS. MR spectra of bile samples from 45 patients (18 female; age range 22-87 years, mean age 57 years) were analyzed both conventionally and using computerized multivariate analysis. Sixteen of the patients had CC, 18 had PSC, and 11 had other benign findings. RESULTS: The spectra of bile from CC patients differed from the benign group in the levels of phosphatidylcholine, bile acids, lipid, and cholesterol. It was possible to distinguish CC from benign conditions in all patients with malignancy. Two benign non-PSC patients were misclassified as malignant. The sensitivity, specificity, and accuracy were 88.9%, 87.1%, and 87.8%, respectively. CONCLUSION: With 1H-MRS of bile, cholangiocarcinoma could be discriminated from benign biliary conditions with or without PSC.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".