Role of AI in Theranostics: Towards Routine Personalized Radiopharmaceutical Therapies

TL;DR

This paper reviews how artificial intelligence can enable routine personalization of radiopharmaceutical therapies by improving image-based dosimetry and biomarker extraction, aiming to make personalized theranostics a clinical reality.

Contribution

It provides a comprehensive review of AI models for image-based dosimetry and discusses integrating biological data for personalized radiopharmaceutical therapy.

Findings

AI models facilitate quantitative imaging and dosimetry steps

Biological information from molecular images can predict outcomes

AI can enable reliable, routine personalization of RPTs

Abstract

We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of radiopharmaceutical therapies (RPTs). Personalized RPTs require patient-individual dosimetry calculations accompanying therapy. Image-based dosimetry needs: 1) quantitative imaging; 2) co-registration and organ/tumor identification on serial and multimodality images; 3) determination of the time-integrated activity; and 4) absorbed dose determination. AI models that facilitate these steps are reviewed. Additionally we discuss the potential to exploit biological information from diagnostic and therapeutic molecular images to derive biomarkers for absorbed dose and outcome prediction, towards personalization of therapies. We try to motivate the nuclear medicine community to expand and align efforts into making routine and reliable personalization of RPTs a reality.