Photodynamic therapy in the treatment of intracranial gliomas: A review of current practice and considerations for future clinical directions
Bibliographic record
Abstract
Invasive grade III and IV malignant gliomas remain difficult to treat with a typical survival time post-diagnosis hovering around 16 months with only minor extension thereof seen in the past decade, whereas some improvements have been obtained towards five-year survival rates for which completeness of resection is a prerequisite. Optical techniques such as fluorescence guided resection (FGR) and photodynamic therapy (PDT) are promising adjuvant techniques to increase the tumor volume reduction fraction. PDT has been used in combination with surgical resection or alternatively as standalone treatment strategy with some success in extending the median survival time of patients compared to surgery alone and the current standard of care. This document reviews the outcome of past clinical trials and highlights the general shift in PDT therapeutic approaches. It also looks at the current approaches for interstitial PDT and research options into increasing PDT's glioma treatment efficacy through exploiting both physical and biological-based approaches to maximize PDT selectivity and therapeutic index, particularly in brain adjacent to tumor (BAT). Potential reasons for failing to demonstrate a significant survival advantage in prior PDT clinical trials will become evident in light of the improved understanding of glioma biology and PDT dosimetry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".