Theranostics and artificial intelligence: new frontiers in personalized medicine

Abstract

The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.

Keywords: artificial intelligence; drug discovery; machine learning; nuclear medicine; theranostics; tumor dosimetry.

Figure 1

The use of AI to integrate multi-omics biomedical data presents a powerful method for understanding complex biological systems and diseases. Multi-omics data, encompassing genomics, proteomics, metabolomics, transcriptomics, radiomics, and more, offer a comprehensive perspective on the molecular mechanisms that underlie health and disease. AI algorithms, especially those in machine learning and deep learning, excel at analyzing and integrating these heterogeneous datasets, revealing patterns, interactions, and insights that might not be detectable by human eyes. This empowers researchers and clinicians to deepen their understanding of disease pathology, improve patient selection, aid in dosimetry and drug discovery, and develop personalized treatment strategies. Harnessing AI's power in integrating multi-omics data marks a significant leap forward for precision medicine and healthcare advancements.

Figure 2

The radiomics workflow incorporates AI-based algorithms to analyze medical imaging data. The radiomics workflow employs AI-based algorithms to extract numerous features from medical imaging data. These algorithms enhance both the patient and physician experience by reducing image acquisition time, aiding in noise reduction, and automating lesion segmentation, without compromising quality. Utilizing radiomic data, AI software transforms images into high-dimensional, mineable data, facilitating the identification of patterns and biomarkers not visible to the human eye. These features can then be correlated with clinical outcomes to enhance diagnostic accuracy, predict disease progression, and personalize treatment plans.

Figure 3

The concept of “Digital Twin”. Digital twins in healthcare are virtual models that replicate an individual's health status, integrating data from a wider variety of sources such as medical records, imaging data, lab tests, genetic data, environmental variables, prior treatments, and multi-omics. These models enable personalized treatment planning, predictive analytics for disease progression, and the optimization of healthcare delivery. By simulating different medical scenarios and capturing real-time data from medical devices, digital twins can improve patient outcomes through tailored interventions and proactive health management. In addition, digital twins support clinical decision process and transform healthcare.

Figure 4

Radiopharmaceutical Drug Development Process. The radiopharmaceutical drug development process involves identifying target molecules and suitable lead compounds, followed by the design, synthesis, and validation of radioactive compounds used for diagnosing or treating diseases. Initially, a potential therapeutic or diagnostic agent is identified and chemically bonded to a radioactive isotope. This compound undergoes rigorous preclinical testing to assess its safety, biodistribution, and efficacy in biological models. Successful candidates advance to clinical trials, where their therapeutic effectiveness, dosimetry, and safety are evaluated in patients. This meticulous process ensures that radiopharmaceuticals are effective for their intended use and safe for patient application. After receiving FDA (US Food and Drug Administration) approval, the process continues with manufacturing and post-market surveillance.