Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy

Abstract

The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, artificial intelligence, and radiomics. Herein, we reexamine the previously identified studies, performing a subgroup analysis on articles specifically utilizing AI and PET imaging for drug discovery purposes in immunotherapy-treated oncology patients. Of the 87 original studies identified, 15 met our updated search criteria. In these studies, radiomics features were primarily extracted from PET/CT images in combination (n = 9, 60.0%) rather than PET imaging alone (n = 6, 40.0%), and patient cohorts were mostly recruited retrospectively and from single institutions (n = 10, 66.7%). AI models were used primarily for prognostication (n = 6, 40.0%) or for assisting in tumor phenotyping (n = 4, 26.7%). About half of the studies stress-tested their models using validation sets (n = 4, 26.7%) or both validation sets and test sets (n = 4, 26.7%), while the remaining six studies (40.0%) either performed no validation at all or used less stringent methods such as cross-validation on the training set. Overall, the integration of AI and PET imaging represents a paradigm shift in drug discovery, offering new avenues for more efficient development of therapeutics. By leveraging AI algorithms and PET imaging analysis, researchers could gain deeper insights into disease mechanisms, identify new drug targets, or optimize treatment regimens. However, further research is needed to validate these findings and address challenges such as data standardization and algorithm robustness.

Keywords: PET; PET/CT; artificial intelligence; drug discovery; immunotherapy; radiomics.

Figure 1

Study selection process for the original 2022 review and the present PET and PET/CT second-look analysis.

Figure 2

Summary of 15 articles that met our inclusion criteria. (A) Imaging modality employed (PET indicates ¹⁸F-FDG-PET imaging, CT indicates computed tomography); (B) data collection strategy classified by time course (retrospective or prospective) and center involvement for patient recruitment (single or multiple institutions); (C) primary predictive aim of the AI model trained in the study: prognosis (e.g., measures of overall or progression-free survival, or durable clinical benefit), tumor phenotype (e.g., PD-L1 receptor expression), treatment response (e.g., predictions of clinical endpoints as defined by RECIST 1.1 or similar criteria), tumor immune microenvironment (e.g., tumoral infiltration of CD8+ T-cells); (D) validation method used for AI models.