Imaging Spectrum of Intrahepatic Mass-Forming Cholangiocarcinoma and Its Mimickers: How to Differentiate Them Using MRI
Bibliographic record
Abstract
Intrahepatic cholangiocarcinoma (ICC) is the second most common primary hepatic malignancy, with mass-forming growth pattern being the most common. The typical imaging appearance of mass-forming ICC (mICC) consists of irregular ring enhancement in the arterial phase followed by the progressive central enhancement on portal venous and delayed phases. However, atypical imaging presentation in the form of hypervascular mICC might also be seen, which can be attributed to distinct pathological characteristics. Ancillary imaging features such as lobular shape, capsular retraction, segmental biliary dilatation, and vascular encasement favor the diagnosis of mICC. Nevertheless, these radiological findings may also be present in certain benign conditions such as focal confluent fibrosis, sclerosing hemangioma, organizing hepatic abscess, or the pseudosolid form of hydatid disease. In addition, a few malignant lesions including primary liver lymphoma, hemangioendothelioma, solitary hypovascular liver metastases, and atypical forms of hepatocellular carcinoma (HCC), such as scirrhous HCC, infiltrative HCC, and poorly differentiated HCC, may also pose a diagnostic dilemma by simulating mICC in imaging studies. Diffusion-weighted imaging and the use of hepatobiliary contrast agents might be helpful for differential diagnosis in certain cases. The aim of this manuscript is to provide a comprehensive overview of mICC imaging features and to describe useful tips for differential diagnosis with its potential mimickers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".