Role of Artificial Intelligence in Theranostics:: Toward Routine Personalized Radiopharmaceutical Therapies.

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-specific dosimetry calculations accompanying therapy. Additionally we discuss the potential to exploit biological information from diagnostic and therapeutic molecular images to derive biomarkers for absorbed dose and outcome prediction; toward 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.

Brosch-Lenz J ，Yousefirizi F ，Zukotynski K ，Beauregard JM ，Gaudet V ，Saboury B ，Rahmim A ，Uribe C ... -  《-》

On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies.

Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.

Brosch-Lenz JF ，Delker A ，Schmidt F ，Tran-Gia J ... -  《-》

Dosimetry for Radiopharmaceutical Therapy: The European Perspective.

This review presents efforts in Europe over the last few years with respect to standardization of quantitative imaging and dosimetry and comprises the results of several European research projects on practices regarding radiopharmaceutical therapies (RPTs). Because the European Union has regulatory requirements concerning dosimetry in RPTs, the European Association of Nuclear Medicine released a position paper in 2021 on the use of dosimetry under these requirements. The importance of radiobiology for RPTs is elucidated in another position paper by the European Association of Nuclear Medicine. Furthermore, how dosimetry interacts with clinical requirements is described, with several clinical examples. In the future, more efforts need to be undertaken to increase teaching and standardization efforts and to incorporate radiobiology for further individualizing patient treatment, with the aim of improving the outcome and safety of RPTs.

Lassmann M ，Eberlein U ，Gear J ，Konijnenberg M ，Kunikowska J ... -  《-》

Theranostics and artificial intelligence: new frontiers in personalized medicine.

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.

Belge Bilgin G ，Bilgin C ，Burkett BJ ，Orme JJ ，Childs DS ，Thorpe MP ，Halfdanarson TR ，Johnson GB ，Kendi AT ，Sartor O ... -  《Theranostics》

Theranostic digital twins for personalized radiopharmaceutical therapies: Reimagining theranostics via computational nuclear oncology.

This work emphasizes that patient data, including images, are not operable (clinically), but that digital twins are. Based on the former, the latter can be created. Subsequently, virtual clinical operations can be performed towards selection of optimal therapies. Digital twins are beginning to emerge in the field of medicine. We suggest that theranostic digital twins (TDTs) are amongst the most natural and feasible flavors of digitals twins. We elaborate on the importance of TDTs in a future where 'one-size-fits-all' therapeutic schemes, as prevalent nowadays, are transcended in radiopharmaceutical therapies (RPTs). Personalized RPTs will be deployed, including optimized intervention parameters. Examples include optimization of injected radioactivities, sites of injection, injection intervals and profiles, and combination therapies. Multi-modal multi-scale images, combined with other data and aided by artificial intelligence (AI) techniques, will be utilized towards routine digital twinning of our patients, and will enable improved deliveries of RPTs and overall healthcare.

Rahmim A ，Brosch-Lenz J ，Fele-Paranj A ，Yousefirizi F ，Soltani M ，Uribe C ，Saboury B ... -  《Frontiers in Oncology》