Photodynamic therapy outcome modelling for patients with spinal metastases: a simulation-based study
Bibliographic record
Abstract
Spinal metastases often occur in the advanced stages of breast, lung or prostate cancer, resulting in a significant impact on the patient's quality of life. Current treatment modalities for spinal metastases include both systemic and localized treatments that aim to decrease pain, improve mobility and structural stability, and control tumour growth. With the development of non-toxic photosensitizer drugs, photodynamic therapy (PDT) has shown promise as a minimally invasive non-thermal alternative in oncology, including for spinal metastases. To apply PDT to spinal metastases, predictive algorithms that optimize tumour treatment and minimize the risk of spinal cord damage are needed to assess the feasibility of the treatment and encourage a broad acceptance of PDT in clinical trials. This work presents a framework for PDT modelling and planning, and simulates the feasibility of using a BPD-MA mediated PDT to treat bone metastases at two different wavelengths (690 nm and 565 nm). An open-source software for PDT planning, PDT-SPACE, is used to evaluate different configurations of light diffusers (cut-end and cylindrical) fibres with optimized power allocation in order to minimize the damage to spinal cord or maximize tumour destruction. The work is simulated on three CT images of metastatically involved vertebrae acquired from three patients with spinal metastases secondary to colorectal or lung cancer. Simulation results show that PDT at a 565 nm wavelength has the ability to treat 90% of the metastatic lesion with less than 17% damage to the spinal cord. However, the energy required, and hence treatment time, to achieve this outcome with the 565 nm is infeasible. The energy required and treatment time for the longer wavelength of 690 nm is feasible ([Formula: see text] min), but treatment aimed at 90% of the metastatic lesion would severely damage the proximal spinal cord. PDT-SPACE provides a simulation platform that can be used to optimize PDT delivery in the metastatic spine. While this work serves as a prospective methodology to analyze the feasibility of PDT for tumour ablation in the spine, preclinical studies in an animal model are ongoing to elucidate the spinal cord damage extent as a function of PDT dose, and the resulting short and long term functional impairments. These will be required before there can be any consideration of clinical trials.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".