PURPOSE: To determine if prostatitis and prostate cancer (PCa) can be distinguished by using apparent diffusion coefficients (ADCs) on magnetic resonance (MR) images, with specimens obtained at MR-guided biopsy as the standard of reference. MATERIALS AND METHODS: The need for institutional review board approval and informed consent was waived. MR-guided biopsies were performed in 130 consecutive patients with cancer-suspicious regions (CSRs) on multiparametric MR images obtained at 3 T. In this retrospective study, 88 patients met the inclusion criteria. During the biopsy procedure, an axial diffusion-weighted sequence was performed and ADC maps were generated (repetition time msec/echo time msec, 2000/67; section thickness, 4 mm; in-plane resolution, 1.8 × 1.8 mm; and b values of 0, 100, 500, and 800 sec/mm(2)). Subsequently, a confirmation image with the needle left in situ was acquired and projected on the ADC map. The corresponding ADCs at the biopsy location were compared with the histopathologic outcomes of the biopsy specimens. Linear mixed-model regression analyses were used to test for ADC differences between the histopathologic groups. RESULTS: The study included 116 biopsy specimens. Median ADCs of normal prostate tissue, prostatitis, low-grade PCa (Gleason grade components 2 or 3), and high-grade PCa (Gleason grade components 4 or 5) were 1.22 × 10(-3) mm(2)/sec (standard deviation, ± 0.21), 1.08 × 10(-3) mm(2)/sec (± 0.18), 0.88 × 10(-3) mm(2)/sec (± 0.15), and 0.88 × 10(-3) mm(2)/sec (± 0.13), respectively. Although the median ADCs of biopsy specimens with prostatitis were significantly higher compared with low- and high-grade PCa (P < .001), there is a considerable overlap between the tissue types. CONCLUSION: Diffusion-weighted imaging is a noninvasive technique that shows differences between prostatitis and PCa in both the peripheral zone and central gland, although its usability in clinical practice is limited as a result of significant overlap in ADCs. RSNA, 2013
PURPOSE: To determine if prostatitis and prostate cancer (PCa) can be distinguished by using apparent diffusion coefficients (ADCs) on magnetic resonance (MR) images, with specimens obtained at MR-guided biopsy as the standard of reference. MATERIALS AND METHODS: The need for institutional review board approval and informed consent was waived. MR-guided biopsies were performed in 130 consecutive patients with cancer-suspicious regions (CSRs) on multiparametric MR images obtained at 3 T. In this retrospective study, 88 patients met the inclusion criteria. During the biopsy procedure, an axial diffusion-weighted sequence was performed and ADC maps were generated (repetition time msec/echo time msec, 2000/67; section thickness, 4 mm; in-plane resolution, 1.8 × 1.8 mm; and b values of 0, 100, 500, and 800 sec/mm(2)). Subsequently, a confirmation image with the needle left in situ was acquired and projected on the ADC map. The corresponding ADCs at the biopsy location were compared with the histopathologic outcomes of the biopsy specimens. Linear mixed-model regression analyses were used to test for ADC differences between the histopathologic groups. RESULTS: The study included 116 biopsy specimens. Median ADCs of normal prostate tissue, prostatitis, low-grade PCa (Gleason grade components 2 or 3), and high-grade PCa (Gleason grade components 4 or 5) were 1.22 × 10(-3) mm(2)/sec (standard deviation, ± 0.21), 1.08 × 10(-3) mm(2)/sec (± 0.18), 0.88 × 10(-3) mm(2)/sec (± 0.15), and 0.88 × 10(-3) mm(2)/sec (± 0.13), respectively. Although the median ADCs of biopsy specimens with prostatitis were significantly higher compared with low- and high-grade PCa (P < .001), there is a considerable overlap between the tissue types. CONCLUSION: Diffusion-weighted imaging is a noninvasive technique that shows differences between prostatitis and PCa in both the peripheral zone and central gland, although its usability in clinical practice is limited as a result of significant overlap in ADCs. RSNA, 2013
Authors: Alice C Yu; Chaitra Badve; Lee E Ponsky; Shivani Pahwa; Sara Dastmalchian; Matthew Rogers; Yun Jiang; Seunghee Margevicius; Mark Schluchter; William Tabayoyong; Robert Abouassaly; Debra McGivney; Mark A Griswold; Vikas Gulani Journal: Radiology Date: 2017-02-10 Impact factor: 11.105
Authors: Lars Schimmöller; Michael Quentin; Christian Arsov; Andreas Hiester; Christian Buchbender; Robert Rabenalt; Peter Albers; Gerald Antoch; Dirk Blondin Journal: Eur Radiol Date: 2014-06-28 Impact factor: 5.315
Authors: Jennifer B Gordetsky; David Ullman; Luciana Schultz; Kristin K Porter; Maria Del Carmen Rodriguez Pena; Carli E Calderone; Jeffrey W Nix; Michael Ullman; Sejong Bae; Soroush Rais-Bahrami Journal: Hum Pathol Date: 2018-09-01 Impact factor: 3.466