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Weather-informed Light–tissue Model–Based Dose Planning for Indoor Daylight Photodynamic Therapy
Authors:Ethan P M LaRochelle  Michael Shane Chapman  Edward V Maytin  Tayyaba Hasan  Brian W Pogue
Institution:1. Thayer School of Engineering, Dartmouth College, Hanover, NH;2. Department of Surgery, Geisel School of Medicine, Dartmouth College, Lebanon, NH;3. Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH;4. Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA;5. Thayer School of Engineering, Dartmouth College, Hanover, NH

Department of Surgery, Geisel School of Medicine, Dartmouth College, Lebanon, NH

Abstract:Daylight activation for photodynamic therapy (PDT) of skin lesions is now widely adopted in many countries as a less painful and equally effective treatment mechanism, as compared to red or blue light activation. However, seasonal daylight availability and transient weather conditions complicate light dose estimations. A method is presented for dose planning without placing a large burden on clinical staff, by limiting spectral measurements to a one-time site assessment, and then using automatically acquired weather reports to track transient conditions. The site assessment tools are used to identify appropriate treatment locations for the annual and daily variations in sunlight exposure for clinical center planning. The spectral information collected from the site assessment can then be integrated with real-time daily electronic weather data. It was shown that a directly measured light exposure has strong correlation (R2: 0.87) with both satellite cloud coverage data and UV index, suggesting that the automated weather indexes can be surrogates for daylight PDT optical dose. These updated inputs can be used in a dose-planning treatment model to estimate photodynamic dose at depth in tissue. A simple standardized method for estimating light dose during daylight-PDT could help improve intersite reproducibility while minimizing treatment times.
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