Outcome of Small (10-20 mm) Arterial Phase-Enhancing Nodules Seen on Triphasic Liver CT in Patients with Cirrhosis or Chronic Liver Disease
Bibliographic record
Abstract
OBJECTIVE: To determine the outcome of small arterial phase-enhancing nodules, 10-20 mm, seen on serial triphasic liver CT scans in a hepatocellular cancer-screening population. METHODS: Of 58 patients referred for triphasic liver CT, 20 (18 men, 2 women) with 32 nodules formed the study group. Each patient in the study group had at least two CT scans, a minimum of 3 months follow-up, at least one nodule measuring 10-20 mm, no prior diagnosis of hepatocellular carcinoma, and no nodule greater than 20 mm typical of hepatocellular carcinoma at the time of the first CT. Serial CT scans were reviewed by an abdominal imaging radiologist who classified the nodules as stable, decreasing, or increasing in size. RESULTS: A mean of six CT studies (range 2-10) were performed for each patient with a mean follow-up of 25 months (range 4-47 months). Of 32 nodules, 14 (44%) were stable, 9 (28%) decreased, and 9 (28%) increased in size. Nodules that increased in size were treated as hepatocellular carcinoma: six were hepatocellular carcinoma, two were biopsy negative but showed recurrent tumor after radiofrequency ablation, and one was a high-grade dysplastic nodule. Mean doubling time for these nine nodules was 5.7 months (range 2.3-10.8 months). CONCLUSIONS: Most small (10-20 mm) arterial phase-enhancing nodules seen on triphasic liver CT are not hepatocellular carcinoma. Serial CT is useful to guide management in these patients. Growth of small arterial phase-enhancing nodules can be used as an indicator that the nodule should be treated as hepatocellular carcinoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".