Literature DB >> 29188191

Prostate cancer radiomics and the promise of radiogenomics.

Radka Stoyanova1, Mandeep Takhar2, Yohann Tschudi1, John C Ford1, Gabriel Solórzano1, Nicholas Erho2, Yoganand Balagurunathan3, Sanoj Punnen4, Elai Davicioni2, Robert J Gillies3, Alan Pollack1.   

Abstract

Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinical-pathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated.

Entities:  

Keywords:  MRI-targeted biopsies; Prostate cancer; Radiogenomics; gene expression; multiparametric MRI; radiomics

Year:  2016        PMID: 29188191      PMCID: PMC5703221          DOI: 10.21037/tcr.2016.06.20

Source DB:  PubMed          Journal:  Transl Cancer Res        ISSN: 2218-676X            Impact factor:   1.241


  64 in total

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2.  Open science--combining open data and open source software: medical image analysis with the Insight Toolkit.

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Journal:  Med Image Anal       Date:  2005-09-19       Impact factor: 8.545

Review 3.  Modeling tracer kinetics in dynamic Gd-DTPA MR imaging.

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Journal:  J Magn Reson Imaging       Date:  1997 Jan-Feb       Impact factor: 4.813

Review 4.  Boosting imaging defined dominant prostatic tumors: a systematic review.

Authors:  Glenn Bauman; Masoom Haider; Uulke A Van der Heide; Cynthia Ménard
Journal:  Radiother Oncol       Date:  2013-06-20       Impact factor: 6.280

5.  Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.

Authors:  Duc Fehr; Harini Veeraraghavan; Andreas Wibmer; Tatsuo Gondo; Kazuhiro Matsumoto; Herbert Alberto Vargas; Evis Sala; Hedvig Hricak; Joseph O Deasy
Journal:  Proc Natl Acad Sci U S A       Date:  2015-11-02       Impact factor: 11.205

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Journal:  Curr Med Res Opin       Date:  2014-03-13       Impact factor: 2.580

7.  Identification of noninvasive imaging surrogates for brain tumor gene-expression modules.

Authors:  Maximilian Diehn; Christine Nardini; David S Wang; Susan McGovern; Mahesh Jayaraman; Yu Liang; Kenneth Aldape; Soonmee Cha; Michael D Kuo
Journal:  Proc Natl Acad Sci U S A       Date:  2008-03-24       Impact factor: 11.205

8.  MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection.

Authors:  Andrew Cameron; Farzad Khalvati; Masoom A Haider; Alexander Wong
Journal:  IEEE Trans Biomed Eng       Date:  2015-10-01       Impact factor: 4.538

9.  Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy.

Authors:  Nicholas Erho; Anamaria Crisan; Ismael A Vergara; Anirban P Mitra; Mercedeh Ghadessi; Christine Buerki; Eric J Bergstralh; Thomas Kollmeyer; Stephanie Fink; Zaid Haddad; Benedikt Zimmermann; Thomas Sierocinski; Karla V Ballman; Timothy J Triche; Peter C Black; R Jeffrey Karnes; George Klee; Elai Davicioni; Robert B Jenkins
Journal:  PLoS One       Date:  2013-06-24       Impact factor: 3.240

10.  Prognostic value of a cell cycle progression signature for prostate cancer death in a conservatively managed needle biopsy cohort.

Authors:  J Cuzick; D M Berney; G Fisher; D Mesher; H Møller; J E Reid; M Perry; J Park; A Younus; A Gutin; C S Foster; P Scardino; J S Lanchbury; S Stone
Journal:  Br J Cancer       Date:  2012-02-23       Impact factor: 7.640

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  43 in total

Review 1.  Towards precision medicine: from quantitative imaging to radiomics.

Authors:  U Rajendra Acharya; Yuki Hagiwara; Vidya K Sudarshan; Wai Yee Chan; Kwan Hoong Ng
Journal:  J Zhejiang Univ Sci B       Date:  2018 Jan.       Impact factor: 3.066

2.  Classification of suspicious lesions on prostate multiparametric MRI using machine learning.

Authors:  Deukwoo Kwon; Isildinha M Reis; Adrian L Breto; Yohann Tschudi; Nicole Gautney; Olmo Zavala-Romero; Christopher Lopez; John C Ford; Sanoj Punnen; Alan Pollack; Radka Stoyanova
Journal:  J Med Imaging (Bellingham)       Date:  2018-09-06

3.  Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI.

Authors:  Prathyush Chirra; Patrick Leo; Michael Yim; B Nicolas Bloch; Ardeshir R Rastinehad; Andrei Purysko; Mark Rosen; Anant Madabhushi; Satish E Viswanath
Journal:  J Med Imaging (Bellingham)       Date:  2019-06-14

4.  Quantitative characterisation of clinically significant intra-prostatic cancer by prostate-specific membrane antigen (PSMA) expression and cell density on PSMA-11.

Authors:  Liran Domachevsky; Natalia Goldberg; Hanna Bernstine; Meital Nidam; David Groshar
Journal:  Eur Radiol       Date:  2018-05-30       Impact factor: 5.315

Review 5.  The role of radiomics in prostate cancer radiotherapy.

Authors:  Rodrigo Delgadillo; John C Ford; Matthew C Abramowitz; Alan Dal Pra; Alan Pollack; Radka Stoyanova
Journal:  Strahlenther Onkol       Date:  2020-08-21       Impact factor: 3.621

6.  MR Imaging-Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma.

Authors:  M Iv; M Zhou; K Shpanskaya; S Perreault; Z Wang; E Tranvinh; B Lanzman; S Vajapeyam; N A Vitanza; P G Fisher; Y J Cho; S Laughlin; V Ramaswamy; M D Taylor; S H Cheshier; G A Grant; T Young Poussaint; O Gevaert; K W Yeom
Journal:  AJNR Am J Neuroradiol       Date:  2018-12-06       Impact factor: 3.825

7.  Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity.

Authors:  SoHyun Han; Radka Stoyanova; Hansol Lee; Sean D Carlin; Jason A Koutcher; HyungJoon Cho; Ellen Ackerstaff
Journal:  Magn Reson Med       Date:  2017-07-20       Impact factor: 4.668

8.  Diagnostic accuracy of MRI texture analysis for grading gliomas.

Authors:  Austin Ditmer; Bin Zhang; Taimur Shujaat; Andrew Pavlina; Nicholas Luibrand; Mary Gaskill-Shipley; Achala Vagal
Journal:  J Neurooncol       Date:  2018-08-25       Impact factor: 4.130

Review 9.  The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective.

Authors:  Robert H Press; Hui-Kuo G Shu; Hyunsuk Shim; James M Mountz; Brenda F Kurland; Richard L Wahl; Ella F Jones; Nola M Hylton; Elizabeth R Gerstner; Robert J Nordstrom; Lori Henderson; Karen A Kurdziel; Bhadrasain Vikram; Michael A Jacobs; Matthias Holdhoff; Edward Taylor; David A Jaffray; Lawrence H Schwartz; David A Mankoff; Paul E Kinahan; Hannah M Linden; Philippe Lambin; Thomas J Dilling; Daniel L Rubin; Lubomir Hadjiiski; John M Buatti
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-06-30       Impact factor: 7.038

10.  A pilot study on dosimetric and radiomics analysis of urethral strictures following HDR brachytherapy as monotherapy for localized prostate cancer.

Authors:  Yat Man Tsang; Dinesh Vignarajah; Alan Mcwilliam; Hannah Tharmalingam; Gerry Lowe; Ananya Choudhury; Peter Hoskin
Journal:  Br J Radiol       Date:  2019-12-02       Impact factor: 3.039

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