Assessing Therapeutic Nanoparticle Accumulation in Tumors Using Nanobubble-Based Contrast-Enhanced Ultrasound Imaging
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide This study explores the challenges associated with nanoparticle-based drug delivery to the tumor parenchyma, focusing on the widely utilized enhanced permeability and retention effect (EPR). While EPR has been a key strategy, its inconsistent clinical success lacks clear mechanistic understanding and is hindered by limited tools for studying relevant phenomena. This work introduces an approach that employs multiparametric dynamic contrast-enhanced ultrasound (CEUS) with a nanoscale contrast agent for noninvasive, real-time examination of tumor microenvironment characteristics. We demonstrate that CEUS imaging can: (1) evaluate tumor microenvironment features, (2) be used to help predict the distribution of doxorubicin-loaded liposomes in the tumor parenchyma, and (3) be used to predict nanotherapeutic efficacy. CEUS using nanobubbles (NBs) was carried out in two tumor types of high (LS174T) and low (U87) vascular permeability. LS174T tumors consistently showed significantly different time intensity curve (TIC) parameters, including area under the rising curve (AUC R, 2.7×) and time to peak intensity (TTP, 1.9×) compared to U87 tumors. Crucially, a recently developed decorrelation time (DT) parameter specific to NB CEUS dynamics successfully predicted the distribution of doxorubicin-loaded liposomes within the tumor parenchyma ( r = 0.86 ± 0.13). AUC R, TTP, and DT were used to correlate imaging findings to nanotherapeutic response with 100% accuracy in SKOV-3 tumors. These findings suggest that NB-CEUS parameters can effectively discern tumor vascular permeability, serving as a biomarker for identifying tumor characteristics and predicting the responsiveness to nanoparticle-based therapies. The observed differences between LS174T and U87 tumors and the accurate prediction of nanotherapeutic efficacy in SKOV-3 tumors indicate the potential utility of this method in predicting treatment efficacy and evaluating EPR in diseases characterized by pathologically permeable vasculature. Ultimately, this research contributes valuable insights into refining drug delivery strategies and assessing the broader applicability of EPR-based approaches.
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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 | 0.001 | 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) | 0.002 | 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".