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Automated Prostate Cancer Detection Using T2-weighted and High-b-value Diffusion-weighted Magnetic Resonance Imaging

Overview
Journal Med Phys
Specialty Biophysics
Date 2015 May 17
PMID 25979032
Citations 45
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Abstract

Purpose: The authors propose a computer-aided diagnosis (CAD) system for prostate cancer to aid in improving the accuracy, reproducibility, and standardization of multiparametric magnetic resonance imaging (MRI).

Methods: The proposed system utilizes two MRI sequences [T2-weighted MRI and high-b-value (b = 2000 s/mm(2)) diffusion-weighted imaging (DWI)] and texture features based on local binary patterns. A three-stage feature selection method is employed to provide the most discriminative features. The authors included a total of 244 patients. Training the CAD system on 108 patients (78 MR-positive prostate cancers and 105 benign MR-positive lesions), two validation studies were retrospectively performed on 136 patients (68 MR-positive prostate cancers, 111 benign MR-positive lesions, and 117 MR-negative benign lesions).

Results: In distinguishing cancer from MR-positive benign lesions, an area under receiver operating characteristic curve (AUC) of 0.83 [95% confidence interval (CI): 0.76-0.89] was achieved. For cancer vs MR-positive or MR-negative benign lesions, the authors obtained an AUC of 0.89 AUC (95% CI: 0.84-0.93). The performance of the CAD system was not dependent on the specific regions of the prostate, e.g., a peripheral zone or transition zone. Moreover, the CAD system outperformed other combinations of MRI sequences: T2W MRI, high-b-value DWI, and the standard apparent diffusion coefficient (ADC) map of DWI.

Conclusions: The novel CAD system is able to detect the discriminative texture features for cancer detection and localization and is a promising tool for improving the quality and efficiency of prostate cancer diagnosis.

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References
1.
Tiwari P, Viswanath S, Kurhanewicz J, Sridhar A, Madabhushi A . Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection. NMR Biomed. 2011; 25(4):607-19. PMC: 3298634. DOI: 10.1002/nbm.1777. View

2.
Turkbey B, Mani H, Shah V, Rastinehad A, Bernardo M, Pohida T . Multiparametric 3T prostate magnetic resonance imaging to detect cancer: histopathological correlation using prostatectomy specimens processed in customized magnetic resonance imaging based molds. J Urol. 2011; 186(5):1818-24. PMC: 5540658. DOI: 10.1016/j.juro.2011.07.013. View

3.
Niaf E, Rouviere O, Mege-Lechevallier F, Bratan F, Lartizien C . Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI. Phys Med Biol. 2012; 57(12):3833-51. DOI: 10.1088/0031-9155/57/12/3833. View

4.
Kitajima K, Takahashi S, Ueno Y, Yoshikawa T, Ohno Y, Obara M . Clinical utility of apparent diffusion coefficient values obtained using high b-value when diagnosing prostate cancer using 3 tesla MRI: comparison between ultra-high b-value (2000 s/mm²) and standard high b-value (1000 s/mm²). J Magn Reson Imaging. 2012; 36(1):198-205. DOI: 10.1002/jmri.23627. View

5.
Jiang Q, Xia S . Zonal differences in prostate diseases. Chin Med J (Engl). 2012; 125(9):1523-8. View