Hypervascular Lesion in a Cirrhotic Liver: A Case Report
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is the leading cause of death among patients with cirrhosis. Therefore, a focal hepatic lesion in a patient with cirrhosis must always be investigated for its high risk of cancer. However, when hepatic lesions in an imaging exam do not present the typical characteristics of a malignant or a benignant tumor, diagnosis may be a challenge. The biopsy can be used in these circumstances, but, as shown by this case, even that can be misleading. A 54-year-old male patient with cirrhosis presented with abdominal pain, jaundice, nausea and vomits. He performed a biopsy at another service, with the result being focal nodular hyperplasia (FNH). He presented adequate hepatic function, and alpha-fetoprotein level was 6.4. Upon first consultation, we required the slides to be brought to our service and reviewed. Our revision also showed no signs of malignancy. Magnetic resonance imaging showed a large hepatic tumor in segments V and VI, predominantly exofitic, with a central scar. The tumor was surgically removed, and its dimensions were 14 × 10 × 9 cm. Microscopic examination revealed an HCC. Even though histological diagnosis was not necessary to indicate surgery, due to its exofitic nature and adequate hepatic function, we discuss the diagnostic characteristics of both HCC and FNH that could help other medical groups in cases where the position of a liver tumor could make the decision to operate more difficult.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".