Tumor Elastography and Its Association with Collagen and the Tumor Microenvironment
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".