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.

Zusammenfassung

Hintergrund Therapeutika, die spezifisch biologische Prozesse adressieren, erfordern oft eine wesentlich feinere Auswahl von Patienten und Subklassifizierung der Erkrankungen. Diagnostische Verfahren müssen die Erkrankungen daher in ausreichender Detailtiefe beschreiben, um die Auswahl der geeigneten Therapie zu ermöglichen und das Ansprechen auf die Therapie sensitiv verfolgen zu können. Anatomische Merkmale sind hierfür oftmals nicht ausreichend. Die Abbildung molekularer und pathophysiologischer Prozesse ist daher notwendig.

Methode Man kann 2 Strategien bei der Bildgebung verfolgen: Molekulare Bildgebung versucht wenige Biomarker darzustellen, die Schlüsselfunktionen in pathologischen Prozessen einnehmen. Alternativ kann man aus der Zusammenschau multipler (unspezifischer) Bildgebungs- und Omics-Marker sowie anderer klinischer Auffälligkeiten Muster erkennen, die biologische Prozesse beschreiben. Hierbei werden zunehmend AI-unterstützte Verfahren eingesetzt.

Ergebnisse Beide Strategien der evidenzbasierten Therapiesteuerung werden in dem Übersichtsartikel erläutert und Beispiele sowie klinische Erfolge aufgeführt. Es werden Übersichten zu klinisch zugelassenen molekularen Diagnostika und Entscheidungsunterstützungssystemen gegeben. Da zuverlässige, repräsentative und ausreichend große Datensätze weitere wichtige Voraussetzungen für AI-unterstützte, multiparametrische Analysen sind, werden ferner Konzepte präsentiert, um Daten strukturiert verfügbar zu machen, z. B. mittels Generative Adversarial Networks Datenbanken mit virtuellen Fällen zu ergänzen, bzw. vollständig anonyme Referenzdatenbanken aufzubauen.

Schlussfolgerung Die molekulare Bildgebung und die computerunterstützte Clusteranalyse von multiplen diagnostischen Daten sind komplementäre Verfahren, um pathophysiologische Prozesse zu beschreiben. Beide Verfahren haben das Potenzial, teilweise eigenständig aber auch in kombinierten Ansätzen die zukünftige Steuerung von Therapien evidenzbasiert zu verbessern.

Kernaussagen:

1. Molekulare Bildgebung und Radiomics liefern wertvolle ergänzende Krankheits-Biomarker.

2. Datengesteuerte, modellbasierte und hybride modellbasierte integrierte Diagnostik fördert die Präzisionsmedizin.

3. Die synthetische Datengenerierung spielt im Entwicklungsprozess zukünftiger KI-Methoden eine wichtige Rolle.

Key words

radiomics - artificial intelligence - deep learning - precision medicine - molecular imaging

Publication History

Received: 27 October 2021

Accepted: 08 January 2022

Article published online:
06 October 2023

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