Automated prostate cancer detection using T2‐weighted and high‐b‐value diffusion‐weighted magnetic resonance imaging. Issue 5 (16th April 2015)

• Record Type: Journal Article
• Authors: Kwak, Jin Tae; Xu, Sheng; Wood, Bradford J.; Turkbey, Baris; Choyke, Peter L.; Pinto, Peter A.; Wang, Shijun; Summers, Ronald M.
• Is Part Of: Medical physics. Volume 42:Issue 5(2015)
• Page Start: 2368
• Page End: 2378
• Publication Date: 2015-04-16
• Subjects: biodiffusion -- biomedical MRI -- cancer -- feature selection -- image sequences -- image texture -- medical image processing; Computer‐aided diagnosis -- Cancer -- Magnetic resonance imaging; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Analysis of texture; prostate cancer -- multiparametric MRI -- CAD -- texture analysis -- feature selection; Cancer -- Magnetic resonance imaging -- Computer aided diagnosis -- Ultrasonography -- Rotation invariant pattern recognition -- Tissues -- Radiologists -- Mining -- Biomedical modeling; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4918318
• Languages: English
• ISSNs: 0094-2405

Abstract

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 [ T 2‐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: T 2W 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.