Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application

Peeken JC, Wiestler B, Combs SE (2020)


Publication Type: Book chapter / Article in edited volumes

Publication year: 2020

Journal

Publisher: Springer

Series: Recent Results in Cancer Research

Book Volume: 216

Pages Range: 773-794

DOI: 10.1007/978-3-030-42618-7_24

Abstract

Medical imaging plays an imminent role in today’s radiation oncology workflow. Predominantly based on semantic image analysis, malignant tumors are diagnosed, staged, and therapy decisions are made. The field of “radiomics” promises to extract complementary, objective information from medical images. In radiomics, predefined quantitative features including intensity statistics, texture, shape, or filtering techniques are combined into statistical or machine learning models to predict clinical or biological outcomes. Alternatively, deep neural networks can directly analyze medical images and provide predictions. A large number of research studies could demonstrate that radiomics prediction models may provide significant benefits in the radiation oncology workflow including diagnostics, tumor characterization, target volume segmentation, prognostic stratification, and prediction of therapy response or treatment-related toxicities. This chapter provides an overview of techniques within the radiomics toolbox, potential clinical application, and current limitations. A literature overview of four selected malignant entities including non-small cell lung cancer, head and neck squamous cell carcinomas, soft tissue sarcomas, and gliomas is given.

Involved external institutions

How to cite

APA:

Peeken, J.C., Wiestler, B., & Combs, S.E. (2020). Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application. In (pp. 773-794). Springer.

MLA:

Peeken, Jan C., Benedikt Wiestler, and Stephanie E. Combs. "Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application." Springer, 2020. 773-794.

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