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Molecular imaging in nuclear cardiology: Pathways to individual precision medicine.

ABSTRACT: Growth of molecular imaging bears potential to transform nuclear cardiology from a primarily diagnostic method to a precision medicine tool. Molecular targets amenable for imaging and therapeutic intervention are particularly promising to facilitate risk stratification, patient selection and exquisite guidance of novel therapies, and interrogation of systems-based interorgan communication. Non-invasive visualization of pathobiology provides valuable insights into the progression of disease and response to treatment. Specifically, inflammation, fibrosis, and neurohormonal signaling, central to the progression of cardiovascular disease and emerging therapeutic strategies, have been investigated by molecular imaging. As the number of radioligands grows, careful investigation of the binding properties and added-value of imaging should be prioritized to identify high-potential probes and facilitate translation to clinical applications. In this review, we discuss the current state of molecular imaging in cardiovascular medicine, and the challenges and opportunities ahead for cardiovascular molecular imaging to navigate the path from diagnosis to prognosis to personalized medicine.

SUBMITTER: Glasenapp A

PROVIDER: S-EPMC7749093 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Molecular imaging in nuclear cardiology: Pathways to individual precision medicine.

Glasenapp A A Hess A A Thackeray J T JT

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology 20200906 6

Growth of molecular imaging bears potential to transform nuclear cardiology from a primarily diagnostic method to a precision medicine tool. Molecular targets amenable for imaging and therapeutic intervention are particularly promising to facilitate risk stratification, patient selection and exquisite guidance of novel therapies, and interrogation of systems-based interorgan communication. Non-invasive visualization of pathobiology provides valuable insights into the progression of disease and r ...[more]

PMID: 32893320

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Project description:'See what you treat and treat what you see, at a molecular level', could be the motto of theranostics. The concept implies diagnosis (imaging) and treatment of cells (usually cancer) using the same molecule, thus guaranteeing a targeted cytotoxic approach of the imaged tumor cells while sparing healthy tissues. As the brilliant late Sam Gambhir would say, the imaging agent acts like a 'molecular spy' and reveals where the tumoral cells are located and the extent of disease burden (diagnosis). For treatment, the same 'molecular spy' docks to the same tumor cells, this time delivering cytotoxic doses of radiation (treatment). This duality represents the concept of a 'theranostic pair', which follows the scope and fundamental principles of targeted precision and personalized medicine. Although the term theranostic was noted in medical literature in the early 2000s, the principle is not at all new to nuclear medicine. The first example of theranostic dates back to 1941 when Dr. Saul Hertz first applied radioiodine for radionuclide treatment of thyroid cells in patients with hyperthyroidism. Ever since, theranostics has been an integral element of nuclear medicine and molecular imaging. The more we understand tumor biology and molecular pathology of carcinogenesis, including specific mutations and receptor expression profiles, the more specific these 'molecular spies' can be developed for diagnostic molecular imaging and subsequent radionuclide targeted therapy (radiotheranostics). The appropriate selection of the diagnostic and therapeutic radionuclide for the 'theranostic pair' is critical and takes into account not only the type of cytotoxic radiation emission, but also the linear energy transfer (LET), and the physical half-lives. Advances in radiochemistry and radiopharmacy with new radiolabeling techniques and chelators are revolutionizing the field. The landscape of cytotoxic systemic radionuclide treatments has dramatically expanded through the past decades thanks to all these advancements. This article discusses present and promising future theranostic applications for various types of diseases such as thyroid disorders, neuroendocrine tumors (NET), pediatric malignancies, and prostate cancer (PC), and provides an outlook for future perspectives.

Project description:BackgroundOne component of precision medicine is to construct prediction models with their predicitve ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases like coronary artery disease, rheumatoid arthritis, and type 2 diabetes, have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under consideration via interactions. In the case of omics data, the use of standard approaches such as generalized linear models may be suboptimal and machine learning methods are appealing to make individual predictions. However, most of these algorithms focus mostly on main or marginal effects of the single features in a dataset. On the other hand, the detection of interacting features is an active area of research in the realm of genetic epidemiology. One big class of algorithms to detect interacting features is based on the multifactor dimensionality reduction (MDR). Here, we further develop the model-based MDR (MB-MDR), a powerful extension of the original MDR algorithm, to enable interaction empowered individual prediction.ResultsUsing a comprehensive simulation study we show that our new algorithm (median AUC: 0.66) can use information hidden in interactions and outperforms two other state-of-the-art algorithms, namely the Random Forest (median AUC: 0.54) and Elastic Net (median AUC: 0.50), if interactions are present in a scenario of two pairs of two features having small effects. The performance of these algorithms is comparable if no interactions are present. Further, we show that our new algorithm is applicable to real data by comparing the performance of the three algorithms on a dataset of rheumatoid arthritis cases and healthy controls. As our new algorithm is not only applicable to biological/genetic data but to all datasets with discrete features, it may have practical implications in other research fields where interactions between features have to be considered as well, and we made our method available as an R package ( https://github.com/imbs-hl/MBMDRClassifieR ).ConclusionsThe explicit use of interactions between features can improve the prediction performance and thus should be included in further attempts to move precision medicine forward.

Dataset Information

Molecular imaging in nuclear cardiology: Pathways to individual precision medicine.

Publications

Molecular imaging in nuclear cardiology: Pathways to individual precision medicine.

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