Microvessel Density and Clinicopathologic Characteristics in Hepatocellular Carcinoma with and without Cirrhosis
Bibliographic record
Abstract
Angiogenesis is essential to the survival, growth, invasion, and metastasis of various human solid tumors. We compared the microvessel density (MVD) and clinicopathologic features of two different groups of hepatocellular carcinoma (HCC), namely HCC with cirrhosis (HCC-C) and without cirrhosis (HCC-NC). A tissue microarray composed of 20 normal livers, 20 cirrhotic livers, tumor and adjacent background non-neoplastic liver tissues from 20 HCC-C and 20 HCC-NC were constructed and stained immunohistochemically with antibodies against the antigen CD34. The MVD was determined by the measurement of the area and density of CD34 positive sinusoidal endothelial cells using the Image Pro Plus software. There was a trend of increased MVD in cirrhotic liver compared to normal liver and in cirrhotic background non-neoplastic liver adjacent to the tumor compared to the non-cirrhotic background non-neoplastic liver. Tumor tissue of HCC-C and HCC-NC both showed significantly higher MVD than their adjacent background non-neoplastic liver tissue. There was no statistical difference in MVD between HCC-C and HCC-NC. A higher value of MVD was seen in tumors of intermediate size (5–10 cm), high histologic grade, the presence of lymphvascular space invasion, and the underlying etiology of hepatitis C and alcoholic steatohepatitis. This data indicates that MVD may play an important role in liver carcinogenesis and neoplastic progression. The difference in clinical behavior between HCC-C and HCC-NC does not seem to be associated with differences in tumor MVD. Objective measurement of MVD using standardized computer software could potentially be used as a clinical marker to predict patients’ prognosis.
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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".