Radiomics in radiation oncology\u2014basics, methods, and limitations

Philipp Lohmann,Mauritius Hoevels,Harald Treuer,Khaled Bousabarah

doi:10.1007/s00066-020-01663-3

Abstract

Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

Highlights

The diagnosis of brain tumors and the assessment of response to radiotherapy [1–4] are mainly based on the results of modern neuroimaging techniques and, essentially, the histomolecular examination of tissue samples collected during tumor resection or biopsy
Brain tumor patients have been diagnosed by means of structural neuroimaging techniques such as contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI)
In the field of radiotherapy, the automated detection of lesions such as brain metastases and the subsequent segmentation is of importance

Summary

Introduction

The diagnosis of brain tumors and the assessment of response to radiotherapy [1–4] are mainly based on the results of modern neuroimaging techniques and, essentially, the histomolecular examination of tissue samples collected during tumor resection or biopsy. Deep learning-based radiomics automatically identifies and extracts high-dimensional features from the input images at different levels of scaling and abstraction, resulting in models especially useful for pattern recognition or classification of high-dimensional non-linear data [32]. A neural network is used that has already been pre-trained on a different, but closely related task, e.g., a neural network for brain tumor segmentation that was originally trained on imaging data from patients with brain metastases might provide useful results for the segmentation of glioma patients [35] Hereby, both the amount of data necessary to identify a relevant feature subset and the computational demand are reduced

Limitations

Findings

Conclusion