Quo Vadis, Molecular Imaging?

Perspectives of Evidence-Based Therapy Management

BACKGROUND

Therapeutics that specifically address biological processes often require a much finer selection of patients and subclassification of diseases. Thus, diagnostic procedures must describe the diseases in sufficient detail to allow selection of appropriate therapy and to sensitively track therapy response. Anatomical features are often not sufficient for this purpose and there is a need to image molecular and pathophysiological processes.

METHOD

Two imaging strategies can be pursued: molecular imaging attempts to image a few biomarkers that play key roles in pathological processes. Alternatively, patterns describing a biological process can be identified from the synopsis of multiple (non-specific) imaging markers, possibly in combination with omics and other clinical findings. Here, AI-based methods are increasingly being used.

RESULTS

Both strategies of evidence-based therapy management are explained in this review article and examples and clinical successes are presented. In this context, reviews of clinically approved molecular diagnostics and decision support systems are listed. Furthermore, since reliable, representative, and sufficiently large datasets are further important prerequisites for AI-assisted multiparametric analyses, concepts are presented to make data available in a structured way, e. g., using Generative Adversarial Networks to complement databases with virtual cases and to build completely anonymous reference databases.

CONCLUSION

Molecular imaging and computer-assisted cluster analysis of diagnostic data are complementary methods to describe pathophysiological processes. Both methods have the potential to improve (evidence-based) the future management of therapies, partly on their own but also in combined approaches.

KEY POINTS

· Molecular imaging and radiomics provide valuable complementary disease biomarkers.. · Data-driven, model-based, and hybrid model-based integrated diagnostics advance precision medicine.. · Synthetic data generation may become essential in the development process of future AI methods..

Advances in PET imaging of cancer

Molecular imaging has experienced enormous advancements in the areas of imaging technology, imaging probe and contrast development, and data quality, as well as machine learning-based data analysis. Positron emission tomography (PET) and its combination with computed tomography (CT) or magnetic resonance imaging (MRI) as a multimodality PET-CT or PET-MRI system offer a wealth of molecular, functional and morphological data with a single patient scan. Despite the recent technical advances and the availability of dozens of disease-specific contrast and imaging probes, only a few parameters, such as tumour size or the mean tracer uptake, are used for the evaluation of images in clinical practice. Multiparametric in vivo imaging data not only are highly quantitative but also can provide invaluable information about pathophysiology, receptor expression, metabolism, or morphological and functional features of tumours, such as pH, oxygenation or tissue density, as well as pharmacodynamic properties of drugs, to measure drug response with a contrast agent. It can further quantitatively map and spatially resolve the intertumoural and intratumoural heterogeneity, providing insights into tumour vulnerabilities for target-specific therapeutic interventions. Failure to exploit and integrate the full potential of such powerful imaging data may lead to a lost opportunity in which patients do not receive the best possible care. With the desire to implement personalized medicine in the cancer clinic, the full comprehensive diagnostic power of multiplexed imaging should be utilized.

Perspectives of Evidence-Based Therapy Management

BACKGROUND

 Therapeutics that specifically address biological processes often require a much finer selection of patients and subclassification of diseases. Thus, diagnostic procedures must describe the diseases in sufficient detail to allow selection of appropriate therapy and to sensitively track therapy response. Anatomical features are often not sufficient for this purpose and there is a need to image molecular and pathophysiological processes.

METHOD

 Two imaging strategies can be pursued: molecular imaging attempts to image a few biomarkers that play key roles in pathological processes. Alternatively, patterns describing a biological process can be identified from the synopsis of multiple (non-specific) imaging markers, possibly in combination with omics and other clinical findings. Here, AI-based methods are increasingly being used.

RESULTS

 Both strategies of evidence-based therapy management are explained in this review article and examples and clinical successes are presented. In this context, reviews of clinically approved molecular diagnostics and decision support systems are listed. Furthermore, since reliable, representative, and sufficiently large datasets are further important prerequisites for AI-assisted multiparametric analyses, concepts are presented to make data available in a structured way, e. g., using Generative Adversarial Networks to complement databases with virtual cases and to build completely anonymous reference databases.

CONCLUSION

 Molecular imaging and computer-assisted cluster analysis of diagnostic data are complementary methods to describe pathophysiological processes. Both methods have the potential to improve (evidence-based) the future management of therapies, partly on their own but also in combined approaches.

KEY POINTS

· Molecular imaging and radiomics provide valuable complementary disease biomarkers.. · Data-driven, model-based, and hybrid model-based integrated diagnostics advance precision medicine.. · Synthetic data generation may become essential in the development process of future AI methods..

CITATION FORMAT

· Kiessling F, Schulz V, . Perspectives of Evidence-Based Therapy Management. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1752-0839.

68Ga-WRWWWW Is a Potential Positron Emission Tomography Probe for Imaging Inflammatory Diseases by Targeting Formyl Peptide Receptor 2

Inflammation plays a significant role in many physiological and pathological processes. Molecular imaging could provide functional as well as anatomical information for visualizing various inflammatory diseases. Advancements in imaging tracers for inflammation would improve the accuracy of diagnosis and monitoring, thus facilitating patient care. The positron emission tomography (PET) imaging tracer, ⁶⁸Ga-labeled antagonist peptide Trp-Arg-Trp-Trp-Trp-Trp (WRWWWW, WRW⁴), targets formyl peptide receptor 2 (FPR2), which is in turn widely distributed in a variety of tissues and is associated with many inflammatory diseases. In the current study, we aimed to investigate the potential of ⁶⁸Ga-WRW⁴ for detecting and monitoring inflammatory lesions in mice. We established an inflammation mouse model by the intramuscular injection of turpentine oil into the left thigh. WRW⁴ was labeled with ⁶⁸Ga with an overall radiochemical yield >90% and radiochemical purity >99%. ⁶⁸Ga-WRW⁴ uptake in inflamed muscle peaked on day 2 (1.14 ± 0.01 percentage of the injected dose per gram of tissue (%ID/g)) and the uptake ratio of inflammatory/normal muscle also reached a maximum (12.36 ± 2.35). Strong PET signals were detected in the left thigh at 60 min after the injection of ⁶⁸Ga-WRW⁴ in experimental mice, but weak or no signals were detected in mice in the blocking and control groups. ⁶⁸Ga-WRW⁴ uptake was in agreement with the dynamics of immune cell infiltration during the inflammatory reaction. These results suggest that ⁶⁸Ga-WRW⁴ is a promising PET tracer suitable for the noninvasive detection of FPR2 expression and for monitoring inflammatory activity in inflammation-bearing mice.

Enzymatic Methods for the Site-Specific Radiolabeling of Targeting Proteins

Site-specific conjugation of proteins is currently required to produce homogenous derivatives for medicine applications. Proteins derivatized at specific positions of the polypeptide chain can actually show higher stability, superior pharmacokinetics, and activity in vivo, as compared with conjugates modified at heterogeneous sites. Moreover, they can be better characterized regarding the composition of the derivatization sites as well as the conformational and activity properties. To this aim, several site-specific derivatization approaches have been developed. Among these, enzymes are powerful tools that efficiently allow the generation of homogenous protein-drug conjugates under physiological conditions, thus preserving their native structure and activity. This review will summarize the progress made over the last decade on the use of enzymatic-based methodologies for the production of site-specific labeled immunoconjugates of interest for nuclear medicine. Enzymes used in this field, including microbial transglutaminase, sortase, galactosyltransferase, and lipoic acid ligase, will be overviewed and their recent applications in the radiopharmaceutical field will be described. Since nuclear medicine can benefit greatly from the production of homogenous derivatives, we hope that this review will aid the use of enzymes for the development of better radio-conjugates for diagnostic and therapeutic purposes.