Recent Advances in Photodynamic Imaging and Therapy in Hepatobiliary Malignancies: Clinical and Experimental Aspects
Bibliographic record
Abstract
The therapeutic and diagnostic modalities of light are well known, and derivative photodynamic reactions with photosensitizers (PSs), specific wavelengths of light exposure and the existence of tissue oxygen have been developed since the 20th century. Photodynamic therapy (PDT) is an effective local treatment for cancer-specific laser ablation in malignancies of some organs, including the bile duct. Although curability for extrahepatic cholangiocarcinoma is expected with surgery alone, patients with unresectable or remnant biliary cancer need other effective palliative therapies, including PDT. The effectiveness of PDT for cholangiocarcinoma has been reported experimentally or clinically, but it is not the standard option now due to problems with accompanied photosensitivity, limited access routes of irradiation, tumor hypoxia, etc. Novel derivative treatments such as photoimmunotherapy have not been applied in the field hepatobiliary system. Photodynamic diagnosis (PDD) has been more widely applied in the clinical diagnoses of liver malignancies or liver vascularization. At present, 5-aminolevulinic acid (ALA) and indocyanine green (ICG) dyes are mainly used as PSs in PDD, and ICG has been applied for detecting liver malignancies or vascularization. However, no ideal tools for combining both PDD and PDT for solid tumors, including hepatobiliary malignancies, have been clinically developed. To proceed with experimental and clinical trials, it is necessary to clarify the effective photosensitive drugs that are feasible for photochemical diagnosis and local treatment.
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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 | 0.001 |
| 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 | 0.001 | 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".