Radiotheranostics Volume 2 – Innovations in Imaging, Therapy and AI Integration

Radiotheranostics is a transformative approach in nuclear medicine that integrates diagnostic imaging and targeted radiotherapy using radiopharmaceuticals. This dual-function strategy enables clinicians not only to visualize disease with high precision but also to deliver therapeutic radiation to pathological tissues, making it especially impactful in oncology. Over the past decade, the field has evolved rapidly, driven by advances in radiochemistry, radiopharmaceutical development, and imaging technologies. Volume 2 of this special issue continues to capture this momentum, highlighting cutting-edge research that is shaping the future of personalized medicine.

At the heart of radiotheranostics are radiopharmaceuticals—molecular constructs that combine a radioactive isotope with a targeting moiety, such as a small molecule, peptide, or antibody. These agents enable selective accumulation in disease sites, thereby offering both diagnostic imaging and therapeutic efficacy. Prominent examples include prostate-specific membrane antigen (PSMA)-targeted compounds like [68Ga]PSMA-11 and [177Lu]PSMA-617, which have revolutionized prostate cancer management, and radiolabeled somatostatin analogs such as [68Ga]DOTATATE and [177Lu]DOTATATE for neuroendocrine tumors.

In addition to radiopharmaceutical innovation, artificial intelligence (AI) and machine learning (ML) are emerging as powerful tools in nuclear medicine, complementing the development of radiotheranostic agents. AI-driven image analysis has improved lesion detection, segmentation, and quantification, enhancing diagnostic accuracy and workflow efficiency. Machine learning algorithms can also predict treatment response and patient outcomes by integrating imaging data with clinical and molecular profiles, facilitating personalized treatment planning. In radiotheranostics, AI is being explored to automate dosimetry calculations, optimize radiopharmaceutical dosing, and identify new imaging biomarkers, thereby accelerating the transition from bench to bedside.

Radiotheranostics—combining diagnostic imaging and targeted radionuclide therapy—continues to transform personalized medicine, especially in oncology. The growing clinical impact of radiotheranostic agents has spurred rapid advances in radiopharmaceutical design, preclinical evaluation, and patient-specific treatment protocols. This Volume 2 of the special issue presents the latest developments across this dynamic field.

Topics include innovations in the synthesis and application of theranostic agents, imaging technologies such as PET, SPECT, and MRI, and the growing role of hybrid imaging systems. Importantly, this volume highlights the integration of artificial intelligence (AI) and machine learning (ML) in nuclear medicine. These tools enhance image interpretation, optimize treatment planning, and support decision-making through predictive modeling.

The issue also explores novel applications of radiotheranostics in neurology, cardiology, and inflammatory diseases. Contributing authors—leaders in radiochemistry, imaging sciences, and clinical medicine—offer diverse insights into the current state and future directions of the field.

Together, these articles underscore the multidisciplinary nature of radiotheranostics and its potential to reshape precision healthcare. This volume serves as a valuable resource for researchers, clinicians, and technologists working at the intersection of imaging, therapy, and data science.

Volume 2 welcomes original research articles, reviews, and perspectives across a broad spectrum of radiotheranostic applications. Topics of interest include, but are not limited to:

1. Development of novel radiotheranostic agents and targeting strategies

2 Theranostic pairs for companion imaging and therapy

3 Clinical and preclinical evaluation of radiopharmaceuticals

4 Optimization of treatment protocols and dosimetry

5 AI and machine learning in image analysis, dosimetry, and biomarker discovery

6 Molecular imaging techniques and precision oncology applications

7 Radiotheranostics in cancer, neurology, cardiology, and beyond

All submitted manuscripts will undergo rigorous peer review by field experts. Contributors are encouraged to follow the journal’s formatting and submission guidelines. Manuscripts should be submitted through the journal’s online submission portal.

By showcasing the synergy between traditional radiochemistry and modern computational techniques, this special issue aims to highlight how radiotheranostics is not only advancing therapeutic precision but also redefining the future of nuclear medicine.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review

Keywords: Radiotheranostics Nuclear medicine Radiopharmaceuticals Personalized medicine Cancer therapy Diagnostic imaging, AI and Machine learning Therapeutic radiopharmaceuticals Preclinical evaluation Radionuclide therapy Targeted therapy Molecular imaging Preci

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.