Radiomics features measured with multiparametric magnetic resonance imaging predict prostate cancer aggressiveness

Stefanie J. Hectors, Mathew Cherny, Kamlesh K. Yadav, Alp Tuna Beksaç, Hari Thulasidass, Sara Lewis, Elai Davicioni, Pei Wang, Ashutosh K. Tewari, Bachir Taouli

Research output: Contribution to journalArticlepeer-review

81 Scopus citations

Abstract

Purpose: We sought to 1) assess the association of radiomics features based on multiparametric magnetic resonance imaging with histopathological Gleason score, gene signatures and gene expression levels in prostate cancer and 2) build machine learning models based on radiomics features to predict adverse histopathological scores and the Decipher genomics metastasis risk score. Materials and Methods: We retrospectively analyzed the records of 64 patients with prostate cancer with a mean age of 64 years (range 41 to 76) who underwent magnetic resonance imaging between January 2016 and January 2017 before radical prostatectomy. A total of 226 magnetic resonance imaging radiomics features, including histogram and texture features in addition to lesion size and the PI-RADS (Prostate Imaging Reporting and Data System) score, were extracted from T2-weighted, apparent diffusion coefficient and diffusion kurtosis imaging maps. Radiomics features were correlated with the pathological Gleason score, 40 gene expression signatures, including Decipher, and 698 prostate cancer related gene expression levels. Cross-validated, lasso regularized, logistic regression machine learning models based on radiomics features were built and evaluated for the prediction of Gleason score 8 or greater and Decipher score 0.6 or greater. Results: A total of 14 radiomics features significantly correlated with the Gleason score (highest correlation r [ 0.39, p [ 0.001). A total of 31 texture and histogram features significantly correlated with 19 gene signatures, particularly with the PORTOS (Post-Operative Radiation Therapy Outcomes Score) signature (strongest correlation r [ e0.481, p [ 0.002). A total of 40 diffusion-weighted imaging features correlated significantly with 132 gene expression levels. Machine learning prediction models showed fair performance to predict a Gleason score of 8 or greater (AUC 0.72) and excellent performance to predict a Decipher score of 0.6 or greater (AUC 0.84) Conclusions: Magnetic resonance imaging radiomics features are promising markers of prostate cancer aggressiveness on the histopathological and genomics levels..

Original languageEnglish
Pages (from-to)498-504
Number of pages7
JournalJournal of Urology
Volume202
Issue number3
DOIs
StatePublished - Sep 2019

Keywords

  • Genomics
  • Machine learning
  • Magnetic resonance imaging
  • Prostatectomy
  • Prostatic neoplasms

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