Tumor Elastography and Its Association with Collagen and the Tumor Microenvironment
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Purpose: The tumor microenvironment presents with altered extracellular matrix (ECM) and stroma composition, which may affect treatment efficacy and contribute to tissue stiffness. Ultrasound (US) elastography can visualize and quantify tissue stiffness noninvasively. However, the contributions of ECM and stromal components to stiffness are poorly understood. We therefore set out to quantify ECM and stroma density and their relation to tumor stiffness. Experimental Design: A modified clinical ultrasound system was used to measure tumor stiffness and perfusion during tumor growth in preclinical tumor models. In vivo measurements were compared with collagen mass spectroscopy and automatic analysis of matrix and stromal markers derived from immunofluorescence images. Results: US elastography estimates of tumor stiffness were positively correlated with tumor volume in collagen and myofibroblast-rich tumors, while no correlations were found for tumors with low collagen and myofibroblast content. US elastography measurements were strongly correlated with ex vivo mechanical testing and mass spectroscopy–based measurements of total collagen and immature collagen crosslinks. Registration of ultrasound and confocal microscopy data showed strong correlations between blood vessel density and T-cell density in syngeneic tumors, while no correlations were found for genetic tumor models. In contrast to collagen density, which was positively correlated with stiffness, no significant correlations were observed for hyaluronic acid density. Finally, localized delivery of collagenase led to a significant reduction in tumor stiffness without changes in perfusion 24 hours after treatment. Conclusions: US elastography can be used as a potential biomarker to assess changes in the tumor microenvironment, particularly changes affecting the ECM. Clin Cancer Res; 24(18); 4455–67. ©2018 AACR.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 it