OBJECTIVE: To evaluate histogram analysis of intravoxel incoherent motion (IVIM) for discriminating the Gleason grade of prostate cancer (PCa). METHODS: A total of 48 patients pathologically confirmed as having clinically significant PCa (size > 0.5 cm) underwent preoperative DW-MRI (b of 0-900 s/mm(2)). Data was post-processed by monoexponential and IVIM model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analysis was performed by outlining entire-tumour regions of interest (ROIs) from histological-radiological correlation. The ability of imaging indices to differentiate low-grade (LG, Gleason score (GS) ≤6) from intermediate/high-grade (HG, GS > 6) PCa was analysed by ROC regression. RESULTS: Eleven patients had LG tumours (18 foci) and 37 patients had HG tumours (42 foci) on pathology examination. HG tumours had significantly lower ADCs and D in terms of mean, median, 10th and 75th percentiles, combined with higher histogram kurtosis and skewness for ADCs, D and f, than LG PCa (p < 0.05). Histogram D showed relatively higher correlations (ñ = 0.641-0.668 vs. ADCs: 0.544-0.574) with ordinal GS of PCa; and its mean, median and 10th percentile performed better than ADCs did in distinguishing LG from HG PCa. CONCLUSION: It is feasible to stratify the pathological grade of PCa by IVIM with histogram metrics. D performed better in distinguishing LG from HG tumour than conventional ADCs. KEY POINTS: • GS had relatively higher correlation with tumour D than ADCs. • Difference of histogram D among two-grade tumours was statistically significant. • D yielded better individual features in demonstrating tumour grade than ADC. • D* and f failed to determine tumour grade of PCa.
OBJECTIVE: To evaluate histogram analysis of intravoxel incoherent motion (IVIM) for discriminating the Gleason grade of prostate cancer (PCa). METHODS: A total of 48 patients pathologically confirmed as having clinically significant PCa (size > 0.5 cm) underwent preoperative DW-MRI (b of 0-900 s/mm(2)). Data was post-processed by monoexponential and IVIM model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analysis was performed by outlining entire-tumour regions of interest (ROIs) from histological-radiological correlation. The ability of imaging indices to differentiate low-grade (LG, Gleason score (GS) ≤6) from intermediate/high-grade (HG, GS > 6) PCa was analysed by ROC regression. RESULTS: Eleven patients had LG tumours (18 foci) and 37 patients had HG tumours (42 foci) on pathology examination. HG tumours had significantly lower ADCs and D in terms of mean, median, 10th and 75th percentiles, combined with higher histogram kurtosis and skewness for ADCs, D and f, than LG PCa (p < 0.05). Histogram D showed relatively higher correlations (ñ = 0.641-0.668 vs. ADCs: 0.544-0.574) with ordinal GS of PCa; and its mean, median and 10th percentile performed better than ADCs did in distinguishing LG from HG PCa. CONCLUSION: It is feasible to stratify the pathological grade of PCa by IVIM with histogram metrics. D performed better in distinguishing LG from HG tumour than conventional ADCs. KEY POINTS: • GS had relatively higher correlation with tumour D than ADCs. • Difference of histogram D among two-grade tumours was statistically significant. • D yielded better individual features in demonstrating tumour grade than ADC. • D* and f failed to determine tumour grade of PCa.
Authors: Whitney B Pope; Hyun J Kim; Jing Huo; Jeffry Alger; Matthew S Brown; David Gjertson; Victor Sai; Jonathan R Young; Leena Tekchandani; Timothy Cloughesy; Paul S Mischel; Albert Lai; Phioanh Nghiemphu; Syed Rahmanuddin; Jonathan Goldin Journal: Radiology Date: 2009-07 Impact factor: 11.105
Authors: N M deSouza; S F Riches; N J Vanas; V A Morgan; S A Ashley; C Fisher; G S Payne; C Parker Journal: Clin Radiol Date: 2008-04-18 Impact factor: 2.350
Authors: Pelin Aksit Ciris; Jr-Yuan George Chiou; Daniel I Glazer; Tzu-Cheng Chao; Clare M Tempany-Afdhal; Bruno Madore; Stephan E Maier Journal: Invest Radiol Date: 2019-04 Impact factor: 6.016