Chromosomal abnormalities determined by comparative genomic hybridization are helpful in the diagnosis of atypical hepatocellular neoplasms
Bibliographic record
Abstract
AIMS: To explore the utility of cytogenetic abnormalities in the distinction of hepatic adenoma (HA) and well-differentiated hepatocellular carcinoma (HCC). METHODS AND RESULTS: Array-based comparative genomic hybridization (CGH) was used to determine chromosomal abnormalities in 39 hepatocellular neoplasms: 12 HA, 15 atypical hepatocellular neoplasms (AHN) and 12 well-differentiated HCC. The designation of AHN was used in two situations: (i) adenoma-like neoplasms (n = 8) in male patients (any age) and women >50 years and <15 years old; (ii) adenoma-like neoplasms with focal atypical features (n = 7). CGH abnormalities were seen in none of the HAs (0/12), eight (53%) AHNs and 11 (92%) HCCs. The number and nature of abnormalities in AHN was similar to HCC with gains in 1q, 8q and 7q being the most common. Although follow-up information was limited, recurrence and/or metastasis were observed in three AHNs (two with abnormal, one with normal CGH). CONCLUSIONS: Adenoma-like neoplasms with focal atypical morphological features or unusual clinical settings such as male gender or women outside the 15-50 year age group can show chromosomal abnormalities similar to well-differentiated HCC. Even though these tumours morphologically mimic adenoma, they can recur and metastasize. Determination of chromosomal abnormalities can be useful in the diagnosis of AHN.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".