Hepatocellular Carcinoma Microvessel Density Quantitation with Image Analysis: Correlation with Prognosis
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) has a progression considered to be dependent on angiogenesis. Intratumoral microvessel density (MVD) has been associated with metastasis and recurrence risk; however, selection bias, counting errors, and lack of standardized assessment criteria have limited the clinical utility of angiogenesis quantitation. Therefore, we analyzed HCC angiogenesis with image cytometry using different methods and determined the correlation to prognosis. Tissue microarrays with 135 HCCs were CD31 and CD34 immunostained and quantitated with the Dako ACIS III Image Cytometer labeling index (LI) and Aperio Scanscope XT and MVD algorithm. LI and MVD were compared to each other and to pathologic features and prognosis (recurrence free survival). Using median cutoffs of microvesselquantitation, survival curve analysis showed a statistically significant difference between CD31 MVD algorithm measurement and prognosis (low MVD mean survival = 56.6 months and high MVD mean = 26.5 months; Log-Rank P = 0.0076). Survival was not significantly related to CD31 LI, CD34 LI or CD34 MVD. By linear regression, a direct correlation was observed between CD31 and CD34 using MVD (r = 0.45, P <0.0001), between CD31 MVD and CD31 LI (r = 0.55, P < 0.0001), and between CD31 LI and CD34 LI (r = 0.51, P < 0.0001). In addition, there was a weak but statistically significant relationship between CD31 MVD and CD34 LI (r = 0.25, P = 0.0050). Together, this data confirms previous studies linking angiogenesis to disease prognosis and suggests the utility of MVD image analysis algorithms.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".