Comparing subharmonic imaging and immunohistochemical markers of angiogenesis
Bibliographic record
Abstract
This work examines angiogenesis associated with the growth of tumors and metastasis. The process of pathologic angiogenesis (neovascularization) can be broken down into three main steps: 1) activation 2) migration and 3) proliferation of endothelial cells. Development of techniques that can measure the functional and structural properties of tumor vascularity at different stages during progression of the tumor, is therefore, imperative for monitoring and controlling the spread of cancer. Contrast enhanced ultrasound imaging has the capability of scanning in real-time without fear of radiation. Contrast agents are composed of gas-filled microbubbles, which exhibit nonlinear scattering when insonified by an ultrasound beam. This leads to the emission of harmonic components of the fundamental signal in the received echoes. The component emitted at half of the fundamental frequency (f₀/2) is called the subharmonic component, and is used in this study as it provides an improved contrast-to-tissue-ratio for ultrasound imaging of tumor angiogenesis. Pre-clinical and clinical ultrasound imaging studies of tumor angiogenesis are often conducted with pathological markers as the reference standard. There is, however, no established standard for how to analyze specimen markers and compare them to imaging parameters. Hence, the purpose of this study was to compare different methods for obtaining tumor neovascularity parameters based on immunohistochemical markers of angiogenesis and compare those to contrast-enhanced subharmonic ultrasound imaging (SHI) in a murine xenograft model. Eighty-five athymic, nude, female rats were implanted with 5 x 10⁶ breast cancer cells (MDA-MB-231) in the mammary fat pad. The ultrasound contrast agent Defmity (Lantheus Medical Imaging, N Billerica, MA) was injected in a tail vein (dose: 36 [mu]l) and pulse-inversion SHI was performed using a modified (able to perform pulse-inversion subharmonic imaging) Sonix RP scanner (Analogic Ultrasound, Richmond, BC, Canada) with a L9-4 linear array. Transmitting and receiving frequencies used were 8 and 4 MHz, respectively. These frequencies were selected as they fall in the frequency band of the transducer and can be used clinically for breast imaging, since the ultimate goal is to use the monitoring of angiogenesis for human breast cancer studies. After the experiments, tumor specimens were sliced corresponding to the imaging planes and 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 histomophometry system based on a Labophot-2 microscope (Nikon, Melville, NJ) at 100x magnification 1) over the entire tumor, 2) in small sub-regions of interest (ROIs), 3) in the tumor periphery (within 2 mm of the margin) and centrally, and 4) in regions of maximum marker expression (so called hotspots). Results from the specimens and from SHI were compared using linear regression analysis. Of the 85 rats implanted 54 (64 %) exhibited tumor growth and 38 were successfully imaged. SHI depicted the tortuous morphology of tumor neovessels and delineated small areas of necrosis. SHI measures of tumor vascularity did not correlate with the immunohistochemical markers when assessed over the entire tumor area (p > 0.60) or over the small sub-ROIs (p > 0.18). However, when the specimens were sub-dived into a central and a peripheral region, COX-2 and VEGF was found to correlate with SHI in the periphery (r = -0.42 with p = 0.005 and r = -0.32 with p = 0.049, respectively). Another correlation in this breast cancer model was seen between SHI and COX-2 in the hotspots of the tumors (r = 0.25; p = 0.007). The results indicate that, when comparing quantitative contrast measures of tumor neovascularity to immunohistochemical markers of angiogenesis in xenograft models it appears that sub-ROIs corresponding to the biologically active region (i.e., the tumor periphery) should be used to account for tumor heterogeneity and development.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".