ArticleBiomedical optics express2018
Automatic interstitial photodynamic therapy planning via convex optimization.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Robustness of interstitial photodynamic therapy treatment planning under power and positional uncertainties in light delivery.Scientific reports · 2026Article
- Photosensitizer spatial heterogeneity and its impact on personalized interstitial photodynamic therapy treatment planning.Journal of biomedical optics · 2025Article
- Article
- Interstitial Photodynamic Therapy of Glioblastomas: A Long-Term Follow-up Analysis of Survival and Volumetric MRI Data.Cancers · 2023Article
- Integrating clinical access limitations into iPDT treatment planning with PDT-SPACE.Biomedical optics express · 2023Article
- Scalable and accessible personalized photodynamic therapy optimization with FullMonte and PDT-SPACE.Journal of biomedical optics · 2022Article
- Optical property recovery with spatially-resolved diffuse reflectance at short source-detector separations using a compact fiber-optic probe.Biomedical optics express · 2021Article
- Photodynamic therapy outcome modelling for patients with spinal metastases: a simulation-based study.Scientific reports · 2021Article
- Machine learning for real-time optical property recovery in interstitial photodynamic therapy: a stimulation-based study.Biomedical optics express · 2021Article
- Singlet oxygen model evaluation of interstitial photodynamic therapy with 5-aminolevulinic acid for malignant brain tumor.Journal of biomedical optics · 2019Article
- FullMonteCUDA: a fast, flexible, and accurate GPU-accelerated Monte Carlo simulator for light propagation in turbid media.Biomedical optics express · 2019Article
- Validation of combined Monte Carlo and photokinetic simulations for the outcome correlation analysis of benzoporphyrin derivative-mediated photodynamic therapy on mice.Journal of biomedical optics · 2019Article
- 5-aminolevulinic acid photodynamic therapy for the treatment of high-grade gliomas.Journal of neuro-oncology · 2019Review
- A Monte Carlo simulation for Moving Light Source in Intracavity PDT.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Treatment planning evolution: Comparing approaches in photodynamic and radiation therapies.Photochemistry and photobiologyReview
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Finding a high-quality treatment plan is an essential, yet difficult, stage of Photodynamic therapy (PDT) as it will determine the therapeutic efficacy in eradicating malignant tumors. A high-quality plan is patient-specific, and provides clinicians with the number of fiber-based spherical diffusers, their powers, and their interstitial locations to deliver the required light dose to destroy the tumor while minimizing the damage to surrounding healthy tissues. In this work, we propose a general convex light source power allocation algorithm that, given light source locations, guarantees optimality of the resulting solution in minimizing the over/under-dosage of volumes of interest. Furthermore, we provide an efficient framework for source selection with concomitant power reallocation to achieve treatment plans with a clinically feasible number of sources and comparable quality. We demonstrate our algorithms on virtual test cases that model glioblastoma multiforme tumors, and evaluate the performance of four different photosensitizers with different activation wavelengths and specific tissue uptake ratios. Results show an average reduction of the damage to organs-at-risk (OAR) by 29% to 31% with comparable runtime to existing power allocation techniques.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.