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Radiomics and radiogenomics of prostate cancer

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Abstract

Radiomics and radiogenomics are attractive research topics in prostate cancer. Radiomics mainly focuses on extraction of quantitative information from medical imaging, whereas radiogenomics aims to correlate these imaging features to genomic data. The purpose of this review is to provide a brief overview summarizing recent progress in the application of radiomics-based approaches in prostate cancer and to discuss the potential role of radiogenomics in prostate cancer.

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Reprinted from Stoyanova R et al. PMID: 27438142 with permission of Oncotarget

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References

  1. Lambin P, Rios-Velazquez E, Leijenaar R, et al. (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 48(4):441–446

    Article  PubMed  PubMed Central  Google Scholar 

  2. Kumar V, Gu Y, Basu S, et al. (2012) Radiomics: the process and the challenges. Magn Reson Imaging. 30(9):1234–1248

    Article  PubMed  PubMed Central  Google Scholar 

  3. Thompson I, Thrasher JB, Aus G, et al. (2007) Guideline for the management of clinically localized prostate cancer: 2007 update. J Urol. 177(6):2106–2131

    Article  PubMed  Google Scholar 

  4. Fernandes ET, Sundaram CP, Long R, Soltani M, Ercole CJ (1997) Biopsy Gleason score: how does it correlate with the final pathological diagnosis in prostate cancer? Br J Urol. 79(4):615–617

    Article  CAS  PubMed  Google Scholar 

  5. Kvåle R, Møller B, Wahlqvist R, et al. (2009) Concordance between Gleason scores of needle biopsies and radical prostatectomy specimens: a population-based study. BJU Int. 103(12):1647–1654

    Article  PubMed  Google Scholar 

  6. Siddiqui MM, Rais-Bahrami S, Turkbey B, et al. (2015) Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. JAMA. 313(4):390–397

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Aihara M, Wheeler TM, Ohori M, Scardino PT (1994) Heterogeneity of prostate cancer in radical prostatectomy specimens. Urology. 43(1):60–66 ((discussion 6-7))

    Article  CAS  PubMed  Google Scholar 

  8. El-Shater Bosaily A, Valerio M, Hu Y, et al. (2016) The concordance between the volume hotspot and the grade hotspot: a 3-D reconstructive model using the pathology outputs from the PROMIS trial. Prostate Cancer Prostatic Dis. 19(3):258–263

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Boutros PC, Fraser M, Harding NJ, et al. (2015) Spatial genomic heterogeneity within localized, multifocal prostate cancer. Nat Genet. 47(7):736–745

    Article  CAS  PubMed  Google Scholar 

  10. Weinreb JC, Barentsz JO, Choyke PL, et al. (2016) PI-RADS prostate imaging–reporting and data system: 2015, version 2. Eur Urol. 69(1):16–40

    Article  PubMed  Google Scholar 

  11. Greer MD, Brown AM, Shih JH, et al. (2017) Accuracy and agreement of PI-RADS v2 for prostate cancer mpMRI: a multireader study. J Magn Reson Imaging. 45(2):579–585

    Article  PubMed  Google Scholar 

  12. Langer DL, van der Kwast TH, Evans AJ, et al. (2010) Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K(trans), v(e), and corresponding histologic features. Radiology. 255(2):485–494

    Article  PubMed  Google Scholar 

  13. Zelhof B, Pickles M, Liney G, et al. (2009) Correlation of diffusion-weighted magnetic resonance data with cellularity in prostate cancer. BJU Int. 103(7):883–888

    Article  PubMed  Google Scholar 

  14. Gibbs P, Liney GP, Pickles MD, et al. (2009) Correlation of ADC and T2 measurements with cell density in prostate cancer at 3.0 Tesla. Invest Radiol. 44(9):572–576

    Article  PubMed  Google Scholar 

  15. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybernet. 6:610–621

    Article  Google Scholar 

  16. Srinivasan G, Shobha G, editors. Statistical texture analysis. Proceedings of world academy of science, engineering and technology; 2008.

  17. Bovik AC, Clark M, Geisler WS (1990) Multichannel texture analysis using localized spatial filters. IEEE Trans Pattern Anal Mach Intell. 12(1):55–73

    Article  Google Scholar 

  18. Busch C (1997) Wavelet based texture segmentation of multi-modal tomographic images. Comput Gr. 21(3):347–358

    Article  Google Scholar 

  19. Tang X (1998) Texture information in run-length matrices. IEEE Trans Image Process. 7(11):1602–1609

    Article  CAS  PubMed  Google Scholar 

  20. Sun C, Wee W (1983) Neighboring gray level dependence matrix for texture classification. Comput Vis Gr Image Process 23(3):341–352

    Article  Google Scholar 

  21. Amadasun M, King R (1989) Texural features corresponding to textural properties. IEEE Trans Syst Man Cybern 23(3):1264–1274

    Article  Google Scholar 

  22. Orlhac F, Soussan M, Maisonobe JA, et al. (2014) Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med. 55(3):414–422

    Article  CAS  PubMed  Google Scholar 

  23. Chalkidou A, O’Doherty MJ, Marsden PK (2015) False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One. 10(5):e0124165

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Trabulsi EJ, Liu XS, Smith WR, Das AK (2013) Transrectal ultrasound of the prostate. In: Gilbert BR, Fulgham PF (eds) Practical urological ultrasound. New York: Springer, pp 155–170

    Chapter  Google Scholar 

  25. Shen D, Lao Z, Zeng J, et al. (2004) Optimized prostate biopsy via a statistical atlas of cancer spatial distribution. Med Image Anal. 8(2):139–150

    Article  PubMed  Google Scholar 

  26. Zhan Y, Shen D, Zeng J, et al. (2007) Targeted prostate biopsy using statistical image analysis. IEEE Trans Med Imaging. 26(6):779–788

    Article  PubMed  Google Scholar 

  27. Xu S, Kruecker J, Turkbey B, et al. (2008) Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies. Comput Aided Surg. 13(5):255–264

    Article  PubMed  PubMed Central  Google Scholar 

  28. Wu P, Liu Y, Li Y, Liu B (2015) Robust prostate segmentation using intrinsic properties of TRUS images. IEEE Trans Med Imaging. 34(6):1321–1335

    Article  PubMed  Google Scholar 

  29. Gong L, Pathak SD, Haynor DR, Cho PS, Kim Y (2004) Parametric shape modeling using deformable superellipses for prostate segmentation. IEEE Trans Med Imaging. 23(3):340–349

    Article  PubMed  Google Scholar 

  30. K. Diaz, B. Castaneda (eds) Semi-automated segmentation of the prostate gland boundary in ultrasound images using a machine learning approach. Medical Imaging (SPIE, 2008).

  31. H. Ning, D. B. Downey, A. Fenster, H. M. Ladak (eds) Prostate surface segmentation from 3D ultrasound images. Proceedings IEEE International Symposium on Biomedical Imaging (2002).

  32. Cosío FA (2008) Automatic initialization of an active shape model of the prostate. Med Image Anal. 12(4):469–483

    Article  PubMed  Google Scholar 

  33. Ghose S, Oliver A, Mitra J, et al. (2013) A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images. Med Image Anal. 17(6):587–600

    Article  PubMed  Google Scholar 

  34. A. Zaim, T. Yi, R. Keck (eds) Feature-Based Classification of Prostate Ultrasound Images using Multiwavelet and Kernel Support Vector Machines. 2007 International Joint Conference on Neural Networks (12-17 Aug 2007).

  35. Yan P, Xu S, Turkbey B, Kruecker J (2010) Discrete deformable model guided by partial active shape model for TRUS image segmentation. IEEE Trans Biomed Eng. 57(5):1158–1166

    Article  PubMed  Google Scholar 

  36. Murphy G, Haider M, Ghai S, Sreeharsha B (2013) The expanding role of MRI in prostate cancer. AJR Am J Roentgenol. 201(6):1229–1238

    Article  PubMed  Google Scholar 

  37. Toth R, Bloch BN, Genega EM, et al. (2011) Accurate prostate volume estimation using multifeature active shape models on T2-weighted MRI. Acad Radiol. 18(6):745–754

    Article  PubMed  Google Scholar 

  38. Chowdhury N, Toth R, Chappelow J, et al. (2012) Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning. Med Phys. 39(4):2214–2228

    Article  PubMed  PubMed Central  Google Scholar 

  39. Shiradkar R, Podder TK, Algohary A, et al. (2016) Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI. Radiat Oncol. 11(1):148

    Article  PubMed  PubMed Central  Google Scholar 

  40. Pasquier D, Lacornerie T, Vermandel M, et al. (2007) Automatic segmentation of pelvic structures from magnetic resonance images for prostate cancer radiotherapy. Int J Radiat Oncol Biol Phys. 68(2):592–600

    Article  PubMed  Google Scholar 

  41. Costa MJ, Delingette H, Novellas S, Ayache N (2007) Automatic segmentation of bladder and prostate using coupled 3D deformable models. Med Image Comput Comput Assist Interv. 10(Pt 1):252–260

    PubMed  Google Scholar 

  42. Klein S, van der Heide UA, Lips IM, et al. (2008) Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med Phys. 35(4):1407–1417

    Article  PubMed  Google Scholar 

  43. Makni N, Puech P, Lopes R, et al. (2009) Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI. Int J Comput Assist Radiol Surg. 4(2):181–188

    Article  PubMed  Google Scholar 

  44. Chandra SS, Dowling JA, Shen KK, et al. (2012) Patient specific prostate segmentation in 3-d magnetic resonance images. IEEE Trans Med Imaging. 31(10):1955–1964

    Article  PubMed  Google Scholar 

  45. Litjens G, Toth R, van de Ven W, et al. (2014) Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med Image Anal. 18(2):359–373

    Article  PubMed  Google Scholar 

  46. Makni N, Iancu A, Colot O, et al. (2011) Zonal segmentation of prostate using multispectral magnetic resonance images. Med Phys. 38(11):6093–6105

    Article  CAS  PubMed  Google Scholar 

  47. Litjens G, Debats O, van de Ven W, Karssemeijer N, Huisman H (2012) A pattern recognition approach to zonal segmentation of the prostate on MRI. Lect Notes Comput Sc. 7511:413–420

    Article  Google Scholar 

  48. Maan B, van der Heijden F, Futterer JJ (2012) A new prostate segmentation approach using multispectral Magnetic Resonance Imaging and a statistical pattern classifier. Proc Spie. 8314:83142Q

    Article  Google Scholar 

  49. Chilali O, Puech P, Lakroum S, et al. (2016) Gland and zonal segmentation of prostate on T2W MR images. J Digit Imaging. 29(6):730–736

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Padgett K, Swallen A, Nelson A, Pollack A, Stoyanova R (2016) Robust atlas based segmentation of the prostate and peripheral zone regions on MRI utilizing multiple MRI system vendors. Med Phys. 43(6):3447

    Article  Google Scholar 

  51. Cameron A, Khalvati F, Haider MA, Wong A (2016) MAPS: a quantitative radiomics approach for prostate cancer detection. IEEE Trans Biomed Eng. 63(6):1145–1156

    Article  PubMed  Google Scholar 

  52. Ginsburg SB, Algohary A, Pahwa S, et al. (2017) Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: preliminary findings from a multi-institutional study. J Magn Reson Imaging. 46(1):184–193

    Article  PubMed  Google Scholar 

  53. Han SM, Lee HJ, Choi JY (2008) Computer-aided prostate cancer detection using texture features and clinical features in ultrasound image. J Digit Imaging. 21(Suppl 1):S121–S133

    Article  PubMed  Google Scholar 

  54. Hussain L, Ahmed A, Saeed S, et al. (2018) Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies. Cancer Biomark. 21(2):393–413

    Article  PubMed  Google Scholar 

  55. Khalvati F, Wong A, Haider MA (2015) Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models. BMC Med Imaging. 15:27

    Article  PubMed  PubMed Central  Google Scholar 

  56. Kwak JT, Xu S, Wood BJ, et al. (2015) Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging. Med Phys. 42(5):2368–2378

    Article  PubMed  PubMed Central  Google Scholar 

  57. Lay N, Tsehay Y, Greer MD, et al. (2017) Detection of prostate cancer in multiparametric MRI using random forest with instance weighting. J Med Imaging (Bellingham). 4(2):024506

    Article  Google Scholar 

  58. Lopes R, Ayache A, Makni N, et al. (2011) Prostate cancer characterization on MR images using fractal features. Med Phys. 38(1):83–95

    Article  CAS  PubMed  Google Scholar 

  59. Madabhushi A, Feldman MD, Metaxas DN, Tomaszeweski J, Chute D (2005) Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI. IEEE Trans Med Imaging. 24(12):1611–1625

    Article  PubMed  Google Scholar 

  60. Metzger GJ, Kalavagunta C, Spilseth B, et al. (2016) Detection of prostate cancer: quantitative multiparametric MR imaging models developed using registered correlative histopathology. Radiology. 279(3):805–816

    Article  PubMed  PubMed Central  Google Scholar 

  61. Mohamed SS, Li J, Salama MM, Freeman G (2009) Prostate tissue texture feature extraction for suspicious regions identification on TRUS images. J Digit Imaging. 22(5):503–518

    Article  CAS  PubMed  Google Scholar 

  62. Stember JN, Deng FM, Taneja SS, Rosenkrantz AB (2014) Pilot study of a novel tool for input-free automated identification of transition zone prostate tumors using T2- and diffusion-weighted signal and textural features. J Magn Reson Imaging. 40(2):301–305

    Article  PubMed  Google Scholar 

  63. Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol. 59(12):1061–1069

    Article  CAS  PubMed  Google Scholar 

  64. Lv D, Guo X, Wang X, Zhang J, Fang J (2009) Computerized characterization of prostate cancer by fractal analysis in MR images. J Magn Reson Imaging. 30(1):161–168

    Article  PubMed  Google Scholar 

  65. Wang J, Wu CJ, Bao ML, et al. (2017) Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol. 27(10):4082–4090

    Article  PubMed  Google Scholar 

  66. Smith AD, Shah SN, Rini BI, Lieber ML, Remer EM (2010) Morphology, Attenuation, Size, and Structure (MASS) criteria: assessing response and predicting clinical outcome in metastatic renal cell carcinoma on antiangiogenic targeted therapy. AJR Am J Roentgenol. 194(6):1470–1478

    Article  PubMed  Google Scholar 

  67. Fehr D, Veeraraghavan H, Wibmer A, et al. (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci USA 112(46):E6265–E6273

    Article  CAS  PubMed  Google Scholar 

  68. Nketiah G, Elschot M, Kim E, et al. (2017) T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol. 27(7):3050–3059

    Article  PubMed  Google Scholar 

  69. Muller BG, Shih JH, Sankineni S, et al. (2015) Prostate Cancer: Interobserver Agreement and Accuracy with the Revised Prostate Imaging Reporting and Data System at Multiparametric MR Imaging. Radiology. 277(3):741–750

    Article  PubMed  PubMed Central  Google Scholar 

  70. Mazurowski MA (2015) Radiogenomics: what it is and why it is important. J Am Coll Radiol. 12(8):862–866

    Article  PubMed  Google Scholar 

  71. Incoronato M, Aiello M, Infante T, et al. (2017) Radiogenomic analysis of oncological data: a technical survey. Int J Mol Sci. 18(4):805

    Article  CAS  PubMed Central  Google Scholar 

  72. Rutman AM, Kuo MD (2009) Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol. 70(2):232–241

    Article  PubMed  Google Scholar 

  73. Stoyanova R, Takhar M, Tschudi Y, et al. (2016) Prostate cancer radiomics and the promise of radiogenomics. Transl Cancer Res. 5(4):432–447

    Article  PubMed  PubMed Central  Google Scholar 

  74. Thawani R, McLane M, Beig N, et al. (2018) Radiomics and radiogenomics in lung cancer: A review for the clinician. Lung Cancer. 115:34–41

    Article  PubMed  Google Scholar 

  75. Wu J, Tha KK, Xing L, Li R (2018) Radiomics and radiogenomics for precision radiotherapy. J Radiat Res. 59(Suppl 1):i25–i31

    Article  PubMed  PubMed Central  Google Scholar 

  76. Pinker K, Shitano F, Sala E, et al. (2017) Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging. 47(3):604–620

    Article  PubMed  PubMed Central  Google Scholar 

  77. Lambin P, Leijenaar RTH, Deist TM, et al. (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 14(12):749–762

    Article  PubMed  Google Scholar 

  78. Valdora F, Houssami N, Rossi F, Calabrese M, Tagliafico AS (2018) Rapid review: radiomics and breast cancer. Breast Cancer Res Treat. 169(2):217–229

    Article  PubMed  Google Scholar 

  79. Zhou M, Scott J, Chaudhury B, et al. (2018) Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol. 39(2):208–216

    Article  CAS  PubMed  Google Scholar 

  80. Litjens GJ, Elliott R, Shih NN, et al. (2016) Computer-extracted features can distinguish noncancerous confounding disease from prostatic adenocarcinoma at multiparametric MR imaging. Radiology. 278(1):135–145

    Article  PubMed  Google Scholar 

  81. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology. 278(2):563–577

    Article  PubMed  Google Scholar 

  82. Vignati A, Mazzetti S, Giannini V, et al. (2015) Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness. Phys Med Biol. 60(7):2685–2701

    Article  CAS  PubMed  Google Scholar 

  83. Wibmer A, Hricak H, Gondo T, et al. (2015) Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol. 25(10):2840–2850

    Article  PubMed  PubMed Central  Google Scholar 

  84. Ahmed HU, El-Shater Bosaily A, Brown LC, et al. (2017) Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 389(10071):767–768

    Article  Google Scholar 

  85. Arsov C, Rabenalt R, Blondin D, et al. (2015) Prospective randomized trial comparing magnetic resonance imaging (MRI)-guided In-bore Biopsy to MRI-ultrasound fusion and transrectal ultrasound-guided prostate biopsy in patients with prior negative biopsies. Eur Urol. 68(4):713–720

    Article  PubMed  Google Scholar 

  86. Bergdahl AG, Wilderang U, Aus G, et al. (2016) Role of magnetic resonance imaging in prostate cancer screening: a pilot study within the goteborg randomised screening trial. Eur Urol. 70(4):566–573

    Article  Google Scholar 

  87. Porpiglia F, Manfredi M, Mele F, et al. (2016) Diagnostic pathway with multiparametric magnetic resonance imaging versus standard pathway: results from a randomized prospective study in biopsy-naive patients with suspected prostate cancer. Eur Urol. 72(2):282–288

    Article  PubMed  Google Scholar 

  88. Tonttila PP, Lantto J, Paakko E, et al. (2016) Prebiopsy multiparametric magnetic resonance imaging for prostate cancer diagnosis in biopsy-naive men with suspected prostate cancer based on elevated prostate-specific antigen values: results from a randomized prospective blinded controlled trial. Eur Urol. 69(3):419–425

    Article  PubMed  Google Scholar 

  89. Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 270(5235):467–470

    Article  CAS  PubMed  Google Scholar 

  90. Marzancola MG, Sedighi A, Li PC (2016) DNA microarray-based diagnostics. Methods Mol Biol. 1368:161–178

    Article  CAS  PubMed  Google Scholar 

  91. Geybels MS, Wright JL, Bibikova M, et al. (2016) Epigenetic signature of Gleason score and prostate cancer recurrence after radical prostatectomy. Clin Epigenet. 8:97

    Article  Google Scholar 

  92. Poustka A, Pohl T, Barlow DP, et al. (1986) Molecular approaches to mammalian genetics. Cold Spring Harb Symp Quant Biol. 51(Pt 1):131–139

    Article  CAS  PubMed  Google Scholar 

  93. Wei L, Wang J, Lampert E, et al. (2016) Intratumoral and intertumoral genomic heterogeneity of multifocal localized prostate cancer impacts molecular classifications and genomic prognosticators. Eur Urol. 71(2):183–192

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Wyatt AW, Azad AA, Volik SV, et al. (2016) Genomic alterations in cell-free dna and enzalutamide resistance in castration-resistant prostate cancer. JAMA Oncol. 2(12):1598–1606

    Article  PubMed  PubMed Central  Google Scholar 

  95. el Bahassi M, Stambrook PJ (2014) Next-generation sequencing technologies: breaking the sound barrier of human genetics. Mutagenesis. 29(5):303–310

    Article  CAS  Google Scholar 

  96. McCann SM, Jiang Y, Fan X, et al. (2016) Quantitative multiparametric MRI features and PTEN expression of peripheral zone prostate cancer: a pilot study. AJR Am J Roentgenol. 206(3):559–565

    Article  PubMed  Google Scholar 

  97. Stoyanova R, Pollack A, Takhar M, et al. (2016) Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies. Oncotarget. 7(33):53362–53376

    Article  PubMed  PubMed Central  Google Scholar 

  98. Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2(12):1636–1642

    Article  PubMed  Google Scholar 

  99. The Cancer Genome Atlas (TCGA). Available from: https://cancergenome.nih.gov/. Accessed 12 March 2018

  100. The Cancer Imaging Archive (TCIA). Available from: http://www.cancerimagingarchive.net/. Accessed 12 March 2018

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Acknowledgements

This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This research was also made possible through the NIH Medical Research Scholars Program, a public-private partnership supported jointly by the NIH and generous contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, the American Association for Dental Research, the Colgate-Palmolive Company, Genentech, Elsevier, and other private donors.

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Authors and Affiliations

  1. Molecular Imaging Program, National Cancer Institute, NIH, 10 Center drive, room B3B85, Bethesda, MD, 20892, USA

    Clayton P. Smith, Marcin Czarniecki, Sherif Mehralivand, Peter L. Choyke & Baris Turkbey

  2. Georgetown University School of Medicine, Washington, DC, USA

    Clayton P. Smith

  3. Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA

    Sherif Mehralivand

  4. Department of Urology and Pediatric Urology, University Medical Center Mainz, Mainz, Germany

    Sherif Mehralivand

  5. Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA

    Radka Stoyanova

  6. Clinical Research Directorate/Clinical Monitoring Research Program, Leidos Biomedical Research, Inc, NCI Campus at Frederick, Frederick, MD, USA

    Stephanie Harmon

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This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This research was also made possible through the NIH Medical Research Scholars Program, a public-private partnership supported jointly by the NIH and generous contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, the American Association for Dental Research, the Colgate-Palmolive Company, Genentech, Elsevier, and other private donors.

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None of the authors (CPS, MC, SM, SH, RS, PLC, BT) of this manuscript have any conflict of interest.

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Peter L. Choyke, Stephanie Harmon and Baris Turkbey share the senior authorship.

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Smith, C.P., Czarniecki, M., Mehralivand, S. et al. Radiomics and radiogenomics of prostate cancer. Abdom Radiol 44, 2021–2029 (2019). https://doi.org/10.1007/s00261-018-1660-7

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