Abstract
Prostate MRI scans for pre-biopsied patients are important. However, fewer radiologists are available for MRI diagnoses, which requires multi-sequential interpretations of multi-slice images. To reduce such a burden, artificial intelligence (AI)-based, computer-aided diagnosis is expected to be a critical technology. We present an AI-based method for pinpointing prostate cancer location and determining tumor morphology using multiparametric MRI. The study enrolled 15 patients who underwent radical prostatectomy between April 2008 and August 2017 at our institution. We labeled the cancer area on the peripheral zone on MR images, comparing MRI with histopathological mapping of radical prostatectomy specimens. Likelihood maps were drawn, and tumors were divided into morphologically distinct regions using the superpixel method. Likelihood maps consisted of pixels, which utilize the cancer likelihood value computed from the T2-weighted, apparent diffusion coefficient, and diffusion-weighted MRI-based texture features. Cancer location was determined based on the likelihood maps. We evaluated the diagnostic performance by the area under the receiver operating characteristic (ROC) curve according to the Chi-square test. The area under the ROC curve was 0.985. Sensitivity and specificity for our approach were 0.875 and 0.961 (p < 0.01), respectively. Our AI-based procedures were successfully applied to automated prostate cancer localization and shape estimation using multiparametric MRI.
Highlights
Technological progress in medical imaging has enabled more sophisticated diagnosis using clinical images through the use of various modalities and protocols with thinner slices at multiple time points
Guided prostate biopsy, multiparametric magnetic resonance imaging is a critical modality for the detection of prostate cancer [1,2]; especially when the cancer is limited to the ventral region or transitional zone of the prostate, where cancer is rarely detected by systematic biopsy [3]
Real-time MRI-transrectal ultrasonography (TRUS) fusion has been put into practical use for targeted prostate biopsy, and the findings have led the European Association of Urology and National Comprehensive Cancer Network guidelines to demonstrate the superiority of diagnosis via targeted biopsy compared with systematic biopsy [5,6]
Summary
Technological progress in medical imaging has enabled more sophisticated diagnosis using clinical images through the use of various modalities and protocols with thinner slices at multiple time points. A targeted biopsy can avoid overdetection of clinically insignificant cancer [4]. Real-time MRI-TRUS fusion has been put into practical use for targeted prostate biopsy, and the findings have led the European Association of Urology and National Comprehensive Cancer Network guidelines to demonstrate the superiority of diagnosis via targeted biopsy compared with systematic biopsy [5,6]. Recent review supported the fundamental role of targeted biopsy, complementary with systemic biopsy in enhancing detection rates and reducing the risk of missing clinically significant cancer [7]. Recent review supported the fundamental role of targeted biopsy, complementary with systemic biopsy in enhancing detection rates and reducing the risk of missing clinically significant cancer [7]. 4.0/).
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