Vascular invasion in hepatocellular carcinoma: is there a correlation with MRI?
Bibliographic record
Abstract
OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the commonest malignancies worldwide. Prognosis is predicted by size at diagnosis, vascular invasion and tumour proliferation markers. This study investigates if MRI features of histologically proven HCCs correlate with vascular invasion. METHODS: Between 2006 and 2008, 18 consecutive patients, with a total of 27 HCCs, had comprehensive MRI studies performed at our institution within a median of 36 days of histology sampling. Each lesion was evaluated independently on MRI by 3 radiologists (blinded to both the radiology and histopathology reports) using a 5-point confidence scale for 23 specific imaging features. The mean of the rating scores across readers was calculated to determine interobserver consistency. The most consistent features were then used to examine the value of features in predicting vascular invasion, using a χ(2 )test for trend, having eliminated those features without sufficient variability. RESULTS: 22 of the 23 imaging features showed sufficient variability across lesions. None of these significantly correlated with the presence of vascular invasion, although a trend was identified with the presence of washout in the portal venous phase on MRI and the median size of lesions, which was greater with vascular invasion. CONCLUSION: This study suggests that no single MRI feature accurately predicts the presence of vascular invasion in HCCs, although a trend was seen with the presence of washout in the portal venous phase post gadolinium. Larger prospective studies are required to investigate this further.
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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".