Multiparametric MRI Maps for Detection and Grading of Dominant Prostate Tumors

Overview

Journal J Magn Reson Imaging

Date 2012 Jan 24

PMID 22267089

Citations 15

Authors

Mehdi Moradi

Septimiu E Salcudean

Silvia D Chang

Edward C Jones

Nicholas Buchan

Rowan G Casey

S Larry Goldenberg

Piotr Kozlowski

Affiliations

Soon will be listed here.

Abstract

Purpose: To develop an image-based technique capable of detection and grading of prostate cancer, which combines features extracted from multiparametric MRI into a single parameter map of cancer probability.

Materials And Methods: A combination of features extracted from diffusion tensor MRI and dynamic contrast enhanced MRI was used to characterize biopsy samples from 29 patients. Support vector machines were used to separate the cancerous samples from normal biopsy samples and to compute a measure of cancer probability, presented in the form of a cancer colormap. The classification results were compared with the biopsy results and the classifier was tuned to provide the largest area under the receiver operating characteristic (ROC) curve. Based solely on the tuning of the classifier on the biopsy data, cancer colormaps were also created for whole-mount histopathology slices from four radical prostatectomy patients.

Results: An area under ROC curve of 0.96 was obtained on the biopsy dataset and was validated by a "leave-one-patient-out" procedure. The proposed measure of cancer probability shows a positive correlation with Gleason score. The cancer colormaps created for the histopathology patients do display the dominant tumors. The colormap accuracy increases with measured tumor area and Gleason score.

Conclusion: Dynamic contrast enhanced imaging and diffusion tensor imaging, when used within the framework of supervised classification, can play a role in characterizing prostate cancer.

Citing Articles

Digital diagnostics and artificial intelligence in prostate cancer treatment in 5 years from now.

Cimadamore A, Cheng L, Scarpelli M, Lopez-Beltran A, Montironi R Transl Androl Urol. 2021; 10(3):1499-1505.

PMID: 33850784 PMC: 8039614. DOI: 10.21037/tau-2021-01.

Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI.

Gholizadeh N, Simpson J, Ramadan S, Denham J, Lau P, Siddique S J Appl Clin Med Phys. 2020; 21(10):179-191.

PMID: 32770600 PMC: 7592985. DOI: 10.1002/acm2.12992.

Artificial intelligence in cancer imaging: Clinical challenges and applications.

Bi W, Hosny A, Schabath M, Giger M, Birkbak N, Mehrtash A CA Cancer J Clin. 2019; 69(2):127-157.

PMID: 30720861 PMC: 6403009. DOI: 10.3322/caac.21552.

Predicting Gleason Score of Prostate Cancer Patients Using Radiomic Analysis.

Chaddad A, Niazi T, Probst S, Bladou F, Anidjar M, Bahoric B Front Oncol. 2019; 8:630.

PMID: 30619764 PMC: 6305278. DOI: 10.3389/fonc.2018.00630.

Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2.

Chen T, Li M, Gu Y, Zhang Y, Yang S, Wei C J Magn Reson Imaging. 2018; 49(3):875-884.

PMID: 30230108 PMC: 6620601. DOI: 10.1002/jmri.26243.

References

Ozer S, Langer D, Liu X, Haider M, van der Kwast T, Evans A . Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI. Med Phys. 2010; 37(4):1873-83. DOI: 10.1118/1.3359459. View

Bloch B, Rofsky N, Baroni R, Marquis R, Pedrosa I, Lenkinski R . 3 Tesla magnetic resonance imaging of the prostate with combined pelvic phased-array and endorectal coils; Initial experience(1). Acad Radiol. 2004; 11(8):863-7. DOI: 10.1016/j.acra.2004.04.017. View

Kozlowski P, Chang S, Meng R, Madler B, Bell R, Jones E . Combined prostate diffusion tensor imaging and dynamic contrast enhanced MRI at 3T--quantitative correlation with biopsy. Magn Reson Imaging. 2010; 28(5):621-8. PMC: 2943947. DOI: 10.1016/j.mri.2010.03.011. View

Buckley D, Roberts C, Parker G, Logue J, Hutchinson C . Prostate cancer: evaluation of vascular characteristics with dynamic contrast-enhanced T1-weighted MR imaging--initial experience. Radiology. 2004; 233(3):709-15. DOI: 10.1148/radiol.2333032098. View

Hosseinzadeh K, Schwarz S . Endorectal diffusion-weighted imaging in prostate cancer to differentiate malignant and benign peripheral zone tissue. J Magn Reson Imaging. 2004; 20(4):654-61. DOI: 10.1002/jmri.20159. View

Jayram G, Decastro G, Large M, Razmaria A, Zagaja G, Shalhav A . Robotic radical prostatectomy in patients with high-risk disease: a review of short-term outcomes from a high-volume center. J Endourol. 2011; 25(3):455-7. DOI: 10.1089/end.2010.0349. View

Issa B . In vivo measurement of the apparent diffusion coefficient in normal and malignant prostatic tissues using echo-planar imaging. J Magn Reson Imaging. 2002; 16(2):196-200. DOI: 10.1002/jmri.10139. View

Augustin H, Fritz G, Ehammer T, Auprich M, Pummer K . Accuracy of 3-Tesla magnetic resonance imaging for the staging of prostate cancer in comparison to the Partin tables. Acta Radiol. 2009; 50(5):562-9. DOI: 10.1080/02841850902889846. View

Parker G, Roberts C, MacDonald A, Buonaccorsi G, Cheung S, Buckley D . Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med. 2006; 56(5):993-1000. DOI: 10.1002/mrm.21066. View

10.

Gibbs P, Pickles M, Turnbull L . Diffusion imaging of the prostate at 3.0 tesla. Invest Radiol. 2006; 41(2):185-8. DOI: 10.1097/01.rli.0000192418.30684.14. View

11.

Padhani A, Gapinski C, Macvicar D, Parker G, Suckling J, Revell P . Dynamic contrast enhanced MRI of prostate cancer: correlation with morphology and tumour stage, histological grade and PSA. Clin Radiol. 2000; 55(2):99-109. DOI: 10.1053/crad.1999.0327. View

12.

Oto A, Kayhan A, Jiang Y, Tretiakova M, Yang C, Antic T . Prostate cancer: differentiation of central gland cancer from benign prostatic hyperplasia by using diffusion-weighted and dynamic contrast-enhanced MR imaging. Radiology. 2010; 257(3):715-23. PMC: 6939960. DOI: 10.1148/radiol.10100021. View

13.

Arora R, Koch M, Eble J, Ulbright T, Li L, Cheng L . Heterogeneity of Gleason grade in multifocal adenocarcinoma of the prostate. Cancer. 2004; 100(11):2362-6. DOI: 10.1002/cncr.20243. View

14.

Engelbrecht M, Huisman H, Laheij R, Jager G, van Leenders G, Hulsbergen-van de Kaa C . Discrimination of prostate cancer from normal peripheral zone and central gland tissue by using dynamic contrast-enhanced MR imaging. Radiology. 2003; 229(1):248-54. DOI: 10.1148/radiol.2291020200. View

15.

Kurhanewicz J, Vigneron D, Hricak H, Narayan P, Carroll P, Nelson S . Three-dimensional H-1 MR spectroscopic imaging of the in situ human prostate with high (0.24-0.7-cm3) spatial resolution. Radiology. 1996; 198(3):795-805. DOI: 10.1148/radiology.198.3.8628874. View

16.

Mazaheri Y, Bokacheva L, Kroon D, Akin O, Hricak H, Chamudot D . Semi-automatic deformable registration of prostate MR images to pathological slices. J Magn Reson Imaging. 2010; 32(5):1149-57. PMC: 2975408. DOI: 10.1002/jmri.22347. View

17.

Jayram G, Eggener S . Patient selection for focal therapy of localized prostate cancer. Curr Opin Urol. 2009; 19(3):268-73. DOI: 10.1097/MOU.0b013e328329eb3c. View

18.

Xu J, Humphrey P, Kibel A, Snyder A, Narra V, Ackerman J . Magnetic resonance diffusion characteristics of histologically defined prostate cancer in humans. Magn Reson Med. 2009; 61(4):842-50. PMC: 3080096. DOI: 10.1002/mrm.21896. View

19.

Jemal A, Bray F, Center M, Ferlay J, Ward E, Forman D . Global cancer statistics. CA Cancer J Clin. 2011; 61(2):69-90. DOI: 10.3322/caac.20107. View

20.

Chan I, Wells 3rd W, Mulkern R, Haker S, Zhang J, Zou K . Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier. Med Phys. 2003; 30(9):2390-8. DOI: 10.1118/1.1593633. View