Photodynamic Therapy for Colorectal Cancer: A Systematic Review of Clinical Research
Bibliographic record
Abstract
Background Photodynamic therapy (PDT) is a therapeutic modality that can be used to ablate tumors using the localized generation of reactive oxygen species by combining a photosensitizer, light, and molecular oxygen. This modality holds promise as an adjunctive therapy in the management of colorectal cancer and could be incorporated into neoadjuvant treatment plans under the auspices of prospective clinical trials. Methods We conducted a search of primary literature published until January 2021, based on PRISMA guidelines. Primary clinical studies of PDT for the management of colorectal cancer were included. Screening, inclusion, quality assessment, and data collection were performed in duplicate. Analyses were descriptive or thematic. Results Nineteen studies were included, most of which were case series. The total number of patients reported to have received PDT for colorectal cancer was 137, almost all of whom received PDT with palliative intent. The most common photosensitizer was hematoporphyin derivative or Photofrin. The light dose used varied from 32 J/cm 2 to 500 J/cm 2 . Complete tumor response (cure) was reported in 40%, with partial response reported in 43.2%. Symptomatic improvement was reported in 51.9% of patients. In total, 32 complications were reported, the most common of which was a skin photosensitivity reaction. Conclusions PDT for the management of colorectal cancer has not been well studied, despite promising results in early clinical case series. New, well designed, prospective clinical trials are required to establish and define the role of PDT in the management of colorectal cancer.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".