Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
Abdominal Radiology
Published in: Abdominal Radiology 6/2019

01-06-2019

Radiomics and radiogenomics of prostate cancer

Published in: Abdominal Radiology | Issue 6/2019

Login to get access

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.
Literature
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):441–446CrossRefPubMedPubMedCentral
2.
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):2106–2131CrossRefPubMed
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):615–617CrossRefPubMed
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):1647–1654CrossRefPubMed
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):390–397CrossRefPubMedPubMedCentral
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))CrossRefPubMed
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):258–263CrossRefPubMedPubMedCentral
9.
Boutros PC, Fraser M, Harding NJ, et al. (2015) Spatial genomic heterogeneity within localized, multifocal prostate cancer. Nat Genet. 47(7):736–745CrossRefPubMed
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):16–40CrossRefPubMed
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):579–585CrossRefPubMed
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):485–494CrossRefPubMed
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):883–888CrossRefPubMed
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):572–576CrossRefPubMed
15.
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybernet. 6:610–621CrossRef
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):55–73CrossRef
18.
Busch C (1997) Wavelet based texture segmentation of multi-modal tomographic images. Comput Gr. 21(3):347–358CrossRef
19.
Tang X (1998) Texture information in run-length matrices. IEEE Trans Image Process. 7(11):1602–1609CrossRefPubMed
20.
Sun C, Wee W (1983) Neighboring gray level dependence matrix for texture classification. Comput Vis Gr Image Process 23(3):341–352CrossRef
21.
Amadasun M, King R (1989) Texural features corresponding to textural properties. IEEE Trans Syst Man Cybern 23(3):1264–1274CrossRef
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):414–422CrossRefPubMed
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):e0124165CrossRefPubMedPubMedCentral
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 155–170CrossRef
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):139–150CrossRefPubMed
26.
Zhan Y, Shen D, Zeng J, et al. (2007) Targeted prostate biopsy using statistical image analysis. IEEE Trans Med Imaging. 26(6):779–788CrossRefPubMed
27.
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):1321–1335CrossRefPubMed
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):340–349CrossRefPubMed
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):469–483CrossRefPubMed
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):587–600CrossRefPubMed
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):1158–1166CrossRefPubMed
36.
Murphy G, Haider M, Ghai S, Sreeharsha B (2013) The expanding role of MRI in prostate cancer. AJR Am J Roentgenol. 201(6):1229–1238CrossRefPubMed
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):745–754CrossRefPubMed
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):2214–2228CrossRefPubMedPubMedCentral
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):148CrossRefPubMedPubMedCentral
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):592–600CrossRefPubMed
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):252–260PubMed
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):1407–1417CrossRefPubMed
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):181–188CrossRefPubMed
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):1955–1964CrossRefPubMed
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):359–373CrossRefPubMed
46.
Makni N, Iancu A, Colot O, et al. (2011) Zonal segmentation of prostate using multispectral magnetic resonance images. Med Phys. 38(11):6093–6105CrossRefPubMed
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. 7511:413–420CrossRef
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. 8314:83142QCrossRef
49.
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):3447CrossRef
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):1145–1156CrossRefPubMed
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):184–193CrossRefPubMed
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–S133CrossRefPubMed
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):393–413CrossRefPubMed
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. 15:27CrossRefPubMedPubMedCentral
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):2368–2378CrossRefPubMedPubMedCentral
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):024506CrossRef
58.
Lopes R, Ayache A, Makni N, et al. (2011) Prostate cancer characterization on MR images using fractal features. Med Phys. 38(1):83–95CrossRefPubMed
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):1611–1625CrossRefPubMed
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):805–816CrossRefPubMedPubMedCentral
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):503–518CrossRefPubMed
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):301–305CrossRefPubMed
63.
Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol. 59(12):1061–1069CrossRefPubMed
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):161–168CrossRefPubMed
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):4082–4090CrossRefPubMed
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):1470–1478CrossRefPubMed
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–E6273CrossRefPubMed
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):3050–3059CrossRefPubMed
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):741–750CrossRefPubMedPubMedCentral
70.
Mazurowski MA (2015) Radiogenomics: what it is and why it is important. J Am Coll Radiol. 12(8):862–866CrossRefPubMed
71.
Incoronato M, Aiello M, Infante T, et al. (2017) Radiogenomic analysis of oncological data: a technical survey. Int J Mol Sci. 18(4):805CrossRefPubMedCentral
72.
Rutman AM, Kuo MD (2009) Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol. 70(2):232–241CrossRefPubMed
73.
74.
Thawani R, McLane M, Beig N, et al. (2018) Radiomics and radiogenomics in lung cancer: A review for the clinician. Lung Cancer. 115:34–41CrossRefPubMed
75.
76.
Pinker K, Shitano F, Sala E, et al. (2017) Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging. 47(3):604–620CrossRefPubMedPubMedCentral
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):749–762CrossRefPubMed
78.
Valdora F, Houssami N, Rossi F, Calabrese M, Tagliafico AS (2018) Rapid review: radiomics and breast cancer. Breast Cancer Res Treat. 169(2):217–229CrossRefPubMed
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):208–216CrossRefPubMed
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):135–145CrossRefPubMed
81.
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology. 278(2):563–577CrossRefPubMed
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):2685–2701CrossRefPubMed
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):2840–2850CrossRefPubMedPubMedCentral
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):767–768CrossRef
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):713–720CrossRefPubMed
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):566–573CrossRef
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):282–288CrossRefPubMed
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):419–425CrossRefPubMed
89.
Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 270(5235):467–470CrossRefPubMed
90.
Marzancola MG, Sedighi A, Li PC (2016) DNA microarray-based diagnostics. Methods Mol Biol. 1368:161–178CrossRefPubMed
91.
Geybels MS, Wright JL, Bibikova M, et al. (2016) Epigenetic signature of Gleason score and prostate cancer recurrence after radical prostatectomy. Clin Epigenet. 8:97CrossRef
92.
Poustka A, Pohl T, Barlow DP, et al. (1986) Molecular approaches to mammalian genetics. Cold Spring Harb Symp Quant Biol. 51(Pt 1):131–139CrossRefPubMed
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):183–192CrossRefPubMedPubMedCentral
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):1598–1606CrossRefPubMedPubMedCentral
95.
el Bahassi M, Stambrook PJ (2014) Next-generation sequencing technologies: breaking the sound barrier of human genetics. Mutagenesis. 29(5):303–310CrossRef
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):559–565CrossRefPubMed
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):53362–53376CrossRefPubMedPubMedCentral
98.
Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2(12):1636–1642CrossRefPubMed
99.
100.
Metadata
Title
Radiomics and radiogenomics of prostate cancer
Publication date
01-06-2019
Published in
Abdominal Radiology / Issue 6/2019
Print ISSN: 2366-004X
Electronic ISSN: 2366-0058
DOI
https://doi.org/10.1007/s00261-018-1660-7

Other articles of this Issue 6/2019

Abdominal Radiology 6/2019 Go to the issue

Peritoneum

Extended abdominopelvic MRI versus CT at the time of adnexal mass characterization for assessing radiologic peritoneal cancer index (PCI) prior to cytoreductive surgery

Special Section: Radiogenomics

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT

Review

Complications of endoscopic retrograde cholangiopancreatography: an imaging review

Pelvis

MR venography using time-resolved imaging in interventional management of pelvic venous insufficiency

Kidneys, Ureters, Bladder, Retroperitoneum

The effect of tumor size and location on efficacy and safety of US- and CT- guided percutaneous microwave ablation in renal cell carcinomas

Classics in Abdominal Radiology

Teardrop sign

ACC 2024 Congress Coverage

Heart Failure Video

Year in Review: Heart failure and cardiomyopathies

Watch now

Watch this official video from ACC.24. Dr. Biykem Bozkurt discuss last year's major advances in heart failure and cardiomyopathies.

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

16-04-2024 Type 2 Diabetes News

Incentivised to exercise

11-04-2024 Lifestyle Modifications News

New intervention for STEMI-related cardiogenic shock

10-04-2024 Myocardial Infarction News

» View more highlights on the ACC 2024 congress hub page

Image Credits