Photothermal Therapy: From Encouraging Lab Results to Lackluster Clinical Translation
Bibliographic record
Abstract
Abstract Cancer is a pervasive and complex disease that poses a significant threat to public health worldwide. The prevalent therapeutic options, including chemotherapy and radiotherapy, pose detrimental side effects. Consequently, non‐invasive and selective therapeutic strategies are sought, such as nanoparticle‐mediated photothermal therapy (PTT). This technique employs benign photothermal agents that gather within tumors post‐injection. Under near‐infra‐red light exposure, these agents induce localized hyperthermia, killing tumor cells. Here, the laboratory development, recent advances, and clinical status of photothermal therapy are examined. Despite two decades of development, photothermal therapy has yielded few clinical trials. A standout agent, the gold nanoshell, holds promise for prostate cancer treatment as the only one in human clinical trials. To provide context, PTT is compared to photodynamic therapy, which has over 250 human trials in 40 years, highlighting the need to bridge the gap for effective photothermal therapy translation. Therefore, we delve into the gap of clinical implementation between photothermal therapy and similar technologies, such as photodynamic therapy, laser interstitial thermal therapy, and cancer nanomedicines, offering insights and potential solutions.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".