Assessing Tumor Microenvironment Characteristics and Stratifying EPR with a Nanobubble Companion Nanoparticle via Contrast-Enhanced Ultrasound Imaging
Bibliographic record
Abstract
The tumor microenvironment is characterized by dysfunctional endothelial cells, resulting in heightened vascular permeability. Many nanoparticle-based drug delivery systems attempt to use this enhanced permeability combined with impaired lymphatic drainage (a concept known as the 'enhanced permeability and retention effect' or EPR effect) as the primary strategy for drug delivery, but this has not proven to be as clinically effective as anticipated. The specific mechanisms behind the inconsistent clinical outcomes of nanotherapeutics have not been clearly articulated, and the field has been hampered by a lack of accessible tools to study EPR-associated phenomena in clinically relevant scenarios. While medical imaging has tremendous potential to contribute to this area, it has not been broadly explored. This work examines, for the first time, the use of multiparametric dynamic contrast-enhanced ultrasound (CEUS) with a novel nanoscale contrast agent to examine tumor microenvironment characteristics noninvasively and in real-time. We demonstrate that CEUS imaging can: (1) evaluate tumor microenvironment features and (2) be used to help predict the distribution of doxorubicin-loaded liposomes in the tumor parenchyma. CEUS using nanobubbles (NBs) was carried out in two tumor types of high (LS174T) and low (U87) vascular permeability, and time-intensity curve (TIC) parameters were evaluated in both models prior to injection of doxorubicin liposomes. Consistently, LS174T tumors showed significantly different TIC parameters, including area under the rising curve (2.7x), time to peak intensity (1.9x) and decorrelation time (DT, 1.9x) compared to U87 tumors. Importantly, the DT parameter successfully predicted tumoral nanoparticle distribution (r = 0.86 ± 0.13). Ultimately, substantial differences in NB-CEUS generated parameters between LS174T and U87 tumors suggest that this method may be useful in determining tumor vascular permeability and could be used as a biomarker for identifying tumor characteristics and predicting sensitivity to nanoparticle-based therapies. These findings could ultimately be applied to predicting treatment efficacy and to evaluating EPR in other diseases with pathologically permeable vasculature.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".