Comparing Quantitative Immunohistochemical Markers of Angiogenesis to Contrast‐Enhanced Subharmonic Imaging
Bibliographic record
Abstract
OBJECTIVES: Different methods for obtaining tumor neovascularity parameters based on immunohistochemical markers were compared to contrast-enhanced subharmonic imaging (SHI). METHODS: Eighty-five athymic nude female rats were implanted with 5 × 10(6) breast cancer cells (MDA-MB-231) in the mammary fat pad. The contrast agent Definity (Lantheus Medical Imaging, North Billerica, MA) was injected, and SHI was performed using a modified Sonix RP scanner (Analogic Ultrasound, Richmond, British Columbia, Canada) with a L9-4 linear array (transmitting/receiving frequencies, 8/4 MHz). Afterward, specimens were stained for endothelial cells (CD31), vascular endothelial growth factor (VEGF), and cyclooxygenase 2 (COX-2). Tumor neovascularity was assessed in 4 different ways using a histomorphometry system (×100 magnification: (1) over the entire tumor; (2) in small sub-regions of interest (ROIs); (3) in the tumor periphery and centrally; and (4) in 3 regions of maximum marker expression (so-called hot spots). Results from specimens and from SHI were compared by linear regression. RESULTS: Fifty-four rats (64%) showed tumor growth, and 38 were successfully imaged. Subharmonic imaging depicted the tortuous morphologic characteristics of tumor neovessels and delineated small areas of necrosis. The immunohistochemical markers did not correlate with SHI measures over the entire tumor area or over small sub-ROIs (P > .18). However, when the specimens were subdivided into central and peripheral regions, COX-2 and VEGF correlated with SHI in the periphery (r = -0.42; P = .005; and r = -0.32; P = .049, respectively). CONCLUSIONS: When comparing quantitative contrast measures of tumor neovascularity to immunohistochemical markers of angiogenesis in xenograft models, ROIs corresponding to the biologically active region should be used to account for tumor heterogeneity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".