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Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
Published on: April 11, 2018
Artificial Intelligence for Simplified Patient-centered Dosimetry in Radiopharmaceutical Therapies
Alejandro Lopez Montes1, Fereshteh Yousefirizi2, Yizhou Chen1
1Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern. Rosenbühlgasse 25CH-3010 Bern. Switzerland.
Abstract:
Patient-specific dosimetry is currently a clinical need to evaluate lesion and organs at risk evolution in radiopharmaceutical therapy (RPT). Conventional dosimetry protocols are often time and/or computationally intensive, which dampers the applicability or real personalized dosimetry. Deep learning solutions for time-integrated activity to dose conversion present alternatives to costly Monte Carlo simulations while not relying on generic anthropomorphic models that are agnostic of the patient's anatomy. Artificial intelligence-enabled segmentation strategies support the evolution of personalized, image-guided RPT planning and monitoring. Quantification of radiopharmaceutical uptake and response at the lesion level enable clinicians to assess therapeutic efficacy and adapt treatment accordingly.
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