Recent Advances and Future Directions in Sonodynamic Therapy for Cancer Treatment
Bibliographic record
Abstract
Deep-tissue solid cancer treatment has a poor prognosis, resulting in a very low 5-year patient survival rate. The primary challenges facing solid tumor therapies are accessibility, incomplete surgical removal of tumor tissue, the resistance of the hypoxic and heterogeneous tumor microenvironment to chemotherapy and radiation, and suffering caused by off-target toxicities. Here, sonodynamic therapy (SDT) is an evolving therapeutic approach that uses low-intensity ultrasound to target deep-tissue solid tumors. The ability of ultrasound to deliver energy safely and precisely into small deep-tissue (>10 cm) volumes makes SDT more effective than conventional photodynamic therapy. While SDT is currently in phase 1/2 clinical trials for glioblastoma multiforme, its use for other solid cancer treatments, such as breast, pancreatic, liver, and prostate cancer, is still in the preclinical stage, with further investigation required to improve its therapeutic efficacy. This review, therefore, focuses on recent advances in SDT cancer treatments. We describe the interaction between ultrasound and sonosensitizer molecules and the associated energy transfer mechanism to malignant cells, which plays a central role in SDT-mediated cell death. Different sensitizers used in clinical and preclinical trials of various cancer treatments are listed, and the critical ultrasound parameters for SDT are reviewed. We also discuss approaches to improve the efficacies of these sonosensitizers, the role of the 3-dimensional spheroid in vitro investigations, ultrasound-controlled CAR-T cell and SDT-based multimodal therapy, and machine learning for sonosensitizer optimization, which could facilitate clinical translation of SDT.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".