PURPOSE: To compare the accuracy of standard and revised monoexponential models of diffusion-weighted magnetic resonance imaging (DW-MRI) data for differentiating malignant from benign prostate tissue, using surgical pathology as the reference standard. MATERIALS AND METHODS: The Institutional Review Board waived informed consent for this Health Insurance Portability and Accountability Act (HIPAA)-compliant, retrospective study of 46 patients (median age = 61 years; range: 42-85 years) who underwent DW-MRI between May and December 2008 before radical prostatectomy for biopsy-proven prostate cancer, had no prior treatment, and had whole-mount step-section pathology maps available showing at least one peripheral zone (PZ) lesion >0.1 cm(3) . DW-MRI data were obtained for b-values of 0, 400, and 700 s/mm(2) . Apparent diffusion coefficients (ADCs) were estimated from PZ regions of interest (ROIs) on b = 0, 700 and b = 0, 400 s/mm(2) images, using a standard monoexponential model. The true diffusion coefficient (D) and perfusion fraction (f) were measured using a revised monoexponential model incorporating all three b-values. Areas under receiver operating characteristic curves (AUCs) were calculated to assess the accuracy of individual parameters and a logistic regression model combining D and f (D+f) in distinguishing malignant ROIs; P < 0.05 denoted significance. RESULTS: ADC(400) (AUC = 0.81, P < 0.0001), ADC(700) (AUC = 0.79, P < 0.0001), D (AUC = 0.71, P = 0.0001) and D + f distinguished malignant from benign ROIs (AUC = 0.82, P < 0.0001), but f did not (AUC = 0.56, P = 0.28); D + f was significantly more accurate than D (P = 0.016) but not more accurate than ADC(400) (P = 0.26) or ADC(700) (P = 0.12). CONCLUSION: The true diffusion coefficient provides an additional DW-MRI parameter for distinguishing prostate cancer that is less influenced than the ADC by b-value selection.
PURPOSE: To compare the accuracy of standard and revised monoexponential models of diffusion-weighted magnetic resonance imaging (DW-MRI) data for differentiating malignant from benign prostate tissue, using surgical pathology as the reference standard. MATERIALS AND METHODS: The Institutional Review Board waived informed consent for this Health Insurance Portability and Accountability Act (HIPAA)-compliant, retrospective study of 46 patients (median age = 61 years; range: 42-85 years) who underwent DW-MRI between May and December 2008 before radical prostatectomy for biopsy-proven prostate cancer, had no prior treatment, and had whole-mount step-section pathology maps available showing at least one peripheral zone (PZ) lesion >0.1 cm(3) . DW-MRI data were obtained for b-values of 0, 400, and 700 s/mm(2) . Apparent diffusion coefficients (ADCs) were estimated from PZ regions of interest (ROIs) on b = 0, 700 and b = 0, 400 s/mm(2) images, using a standard monoexponential model. The true diffusion coefficient (D) and perfusion fraction (f) were measured using a revised monoexponential model incorporating all three b-values. Areas under receiver operating characteristic curves (AUCs) were calculated to assess the accuracy of individual parameters and a logistic regression model combining D and f (D+f) in distinguishing malignant ROIs; P < 0.05 denoted significance. RESULTS: ADC(400) (AUC = 0.81, P < 0.0001), ADC(700) (AUC = 0.79, P < 0.0001), D (AUC = 0.71, P = 0.0001) and D + f distinguished malignant from benign ROIs (AUC = 0.82, P < 0.0001), but f did not (AUC = 0.56, P = 0.28); D + f was significantly more accurate than D (P = 0.016) but not more accurate than ADC(400) (P = 0.26) or ADC(700) (P = 0.12). CONCLUSION: The true diffusion coefficient provides an additional DW-MRI parameter for distinguishing prostate cancer that is less influenced than the ADC by b-value selection.
Authors: Ofer Yossepowitch; Kanishka Sircar; Peter T Scardino; Makoto Ohori; Michael W Kattan; Thomas M Wheeler; Victor E Reuter Journal: J Urol Date: 2002-11 Impact factor: 7.450
Authors: Filippo Pesapane; Francesca Patella; Enrico Maria Fumarola; Silvia Panella; Anna Maria Ierardi; Giovanni Guido Pompili; Giuseppe Franceschelli; Salvatore Alessio Angileri; Alberto Magenta Biasina; Gianpaolo Carrafiello Journal: Med Oncol Date: 2017-01-31 Impact factor: 3.064
Authors: Marc A Bjurlin; Xiaosong Meng; Julien Le Nobin; James S Wysock; Herbert Lepor; Andrew B Rosenkrantz; Samir S Taneja Journal: J Urol Date: 2014-04-21 Impact factor: 7.450
Authors: Yuxi Pang; Baris Turkbey; Marcelino Bernardo; Jochen Kruecker; Samuel Kadoury; Maria J Merino; Bradford J Wood; Peter A Pinto; Peter L Choyke Journal: Magn Reson Med Date: 2012-04-09 Impact factor: 4.668
Authors: Kinzya Grant; Maria L Lindenberg; Haytham Shebel; Yuxi Pang; Harsh K Agarwal; Marcelino Bernardo; Karen A Kurdziel; Baris Turkbey; Peter L Choyke Journal: Eur J Nucl Med Mol Imaging Date: 2013-05-07 Impact factor: 9.236
Authors: L Bernardin; N H M Douglas; D J Collins; S L Giles; E A M O'Flynn; M Orton; N M deSouza Journal: Eur Radiol Date: 2013-11-26 Impact factor: 5.315