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“Radio-oncomics”

The potential of radiomics in radiation oncology

„Radio-oncomics“

Das Potenzial von Radiomics in der Strahlenonkologie

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Abstract

Introduction

Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow.

Methods

After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created.

Results

Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information (“radiogenomics”) and could be used for tumor characterization.

Discussion

Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches.

Conclusion

This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.

Zusammenfassung

Einleitung

Radiomics beschreibt eine algorithmusbasierte Berechnung von Merkmalen auf Basis von Bilddatensätzen einschließlich Computertomographie (CT), Magnetresonanztomographie (MRT) und Positronenemissionstomographie (PET). Radiomics hat das Potenzial, die klinische Entscheidungsfindung, die Therapieplanung sowie die Nachsorge signifikant zu beeinflussen.

Methoden

Nach der Bildgebung erfolgt die Prozessierung der Daten sowie die Segmentierung von Zielstrukturen. Anschließend werden durch Algorithmen Merkmale berechnet, welche die Form, Intensität, Textur und multiskalierte Filter abbilden. Prognostische Modelle können durch Kombination und Korrelation relevanter Merkmale mit klinischen Daten erzeugt werden.

Ergebnis

In retrospektiven Studien wurden Radiomics-basierte prognostische Modelle entwickelt, die eine Vorhersagekraft u. a. für Gesamtüberleben, Therapieansprechen, Fernmetastasierung und radiogene Nebenwirkung zeigten. Radiomics-Merkmale können außerdem mit zugrundeliegenden genetischen Informationen korreliert werden (Radiogenomics) und zur Tumoridentifikation verwendet werden.

Diskussion

In der Präzisionsmedizin der Zukunft wird die Strahlentherapie maßgeblich durch Bildgebungs- und genombasierte Informationen beeinflusst werden. Behandlungskonzepte werden durch Individualisierung von Strahlendosis, Zielvolumendefinition und anderen therapieentscheidenden Faktoren auf den Patienten zugeschnitten werden. Dabei wird Radiomics durch Integration multipler Datensätze und dadurch bedingter Optimierung der prognostischen Aussagekraft möglicherweise eine herausragende Rolle einnehmen.

Schlussfolgerung

Dieser Übersichtsartikel fasst die aktuelle Literatur über die Anwendung des Radiomics-Konzepts in der Strahlentherapie zusammen. Die Notwendigkeit von klinischen Studien zur Integration von Radiomics-Modellen wird hervorgehoben.

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Acknowledgements

We acknowledge Prof. Multhoff for supplying figure-relevant images.

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Authors and Affiliations

  1. Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München (TUM), Ismaninger Straße 22, 81675, München, Germany

    Jan Caspar Peeken, Fridtjof Nüsslin & Stephanie E. Combs

  2. Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany

    Stephanie E. Combs

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  1. Jan Caspar Peeken
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Correspondence to Jan Caspar Peeken.

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Conflict of interest

J.C. Peeken, F. Nüsslin, and S.E. Combs declare that they have no competing interests.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Peeken, J.C., Nüsslin, F. & Combs, S.E. “Radio-oncomics”. Strahlenther Onkol 193, 767–779 (2017). https://doi.org/10.1007/s00066-017-1175-0

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