Perspectives of Evidence-Based Therapy Management.

Abstract

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..