Artificial Intelligence for Simplified Patient-centered Dosimetry in Radiopharmaceutical Therapies.
Type: article
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.
DOI: 10.48620/92833
Authors: Lopez Montes, Alejandro, Yousefirizi, Fereshteh, Chen, Yizhou, Salimi, Yazdan, Seifert, Robert, Afshar-Oromieh, Ali, Uribe, Carlos, Rominger, Axel, Zaidi, Habib, Rahmim, Arman, Shi, Kuangyu
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7108474587
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