The role of radiomics in prostate cancer radiotherapy
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REVIEW ARTICLE
The role of radiomics in prostate cancer radiotherapy Rodrigo Delgadillo1 · John C. Ford1 · Matthew C. Abramowitz1 · Alan Dal Pra1 · Alan Pollack1 · Radka Stoyanova1 Received: 3 July 2020 / Accepted: 7 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract “Radiomics,” as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.
Keywords Radiogenomics · Delta-radiomics · Multiparametric MRI · Cone beam CT
Introduction Contemporary radiation treatment (RT) of prostate cancer is associated with large-scale image acquisition. Multiparametric magnetic resonance imaging (mpMRI), computed tomography (CT), and cone beam CT (CBCT) are acquired at multiple steps of the course of RT, starting with patient diagnosis and then for treatment planning, delivery, and follow-up. The sheer amount of data requires automated methods for extraction of quantitative imaging features and analysis for informing clinical decisions. Radiomics, as it refers to the extraction and analysis of advanced quantitative information, converts imaging data into a high-dimensional mineable feature space using a large number of automatically extracted data-characterization algorithms [1]. The main hypothesis behind the radiomics effort is that the imaging features capture distinct phenotypic differences of
Radka Stoyanova
[email protected] 1
Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, Miami, FL 33136, USA
the tumors and may have diagnostic, prognostic, and predictive power [2]. Among the chief questions in the treatment of prostate cancer are the choice of RT fractionation schedule (hypo- or conventional fractionation) and the use, timing, and duration of androgen deprivation therapy (ADT). Depending on the risk category,
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