Artificial Intelligence for Computational Theranostics: Integrating Molecular Imaging, Nanomedicine, and Precision Oncology

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. The convergence of molecular imaging, nanomedicine, and artificial intelligence (AI) has given rise to computational theranostics, an emerging paradigm that combines diagnosis, therapy, therapeutic monitoring, and personalized decision-making within unified computational frameworks. Molecular imaging modalities—including positron emission tomography (PET), single-photon emission computed tomography (SPECT), computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and optical imaging—provide noninvasive visualization of tumor biology, while nanomedicine enables targeted drug delivery, molecular imaging enhancement, and precision therapeutics. Artificial intelligence, through machine learning, deep learning, multimodal learning, transformer architectures, graph neural networks, generative AI, and foundation models, integrates these diverse biomedical datasets with genomics, digital pathology, laboratory biomarkers, and longitudinal clinical information to optimize diagnosis, treatment selection, therapeutic response prediction, and personalized cancer care. AI-driven computational theranostics has demonstrated remarkable potential in radiomics, radiopharmaceutical development, nanoparticle engineering, immunotherapy prediction, adaptive treatment planning, digital twins, and clinical decision support. Despite substantial progress, important challenges remain regarding multimodal data integration, explainability, computational scalability, regulatory validation, cybersecurity, and ethical implementation. This review provides a comprehensive overview of computational theranostics, highlighting current innovations, clinical applications, implementation challenges, and future perspectives for precision oncology.[1]