Intravoxel incoherent motion and diffusion kurtosis imaging and their machine-learning-based texture analysis for detection and assessment of prostate cancer severity at 3 T.

OBJECTIVES: To evaluate the role of combined intravoxel incoherent motion and diffusion kurtosis imaging (IVIM-DKI) and their machine-learning-based texture analysis for the detection and assessment of severity in prostate cancer (PCa). MATERIALS AND METHODS: Eighty-eight patients underwent MRI on a 3 T scanner after giving informed consent. IVIM-DKI data were acquired using 13 b values (0-2000 s/mm2) and analyzed using the IVIM-DKI model with the total variation (TV) method. PCa patients were categorized into two groups: clinically insignificant prostate cancer (CISPCa) (Gleason grade ≤ 6) and clinically significant prostate cancer (CSPCa) (Gleason grade ≥ 7). One-way analysis-of-variance, t test, and receiver operating characteristic analysis was performed to measure the discriminative ability to detect PCa using IVIM-DKI parameters. A chi-square test was used to select important texture features of apparent diffusion coefficient (ADC) and IVIM-DKI parameters. These selected texture features were used in an artificial neural network for PCa detection. RESULTS: ADC and diffusion coefficient (D) were significantly lower (p < 0.001), and kurtosis (k) was significantly higher (p < 0.001), in PCa as compared with benign prostatic hyperplasia (BPH) and normal peripheral zone (PZ). ADC, D, and k showed high areas under the curves (AUCs) of 0.92, 0.89, and 0.88, respectively, in PCa detection. ADC and D were significantly lower (p < 0.05) as compared with CISPCa versus CSPCa. D for detecting CSPCa was high, with an AUC of 0.63. A negative correlation of ADC and D with GS (ADC, ρ = -0.33; D, ρ = -0.35, p < 0.05) and a positive correlation of k with GS (ρ = 0.22, p < 0.05) were observed. Combined IVIM-DKI texture showed high AUC of 0.83 for classification of PCa, BPH, and normal PZ. CONCLUSION: D, f, and k computed using the IVIM-DKI model with the TV method were able to differentiate PCa from BPH and normal PZ. Texture features of combined IVIM-DKI parameters showed high accuracy and AUC in PCa detection.

Body mass index-based controlled attenuation parameter cut-offs for assessment of hepatic steatosis in non-alcoholic fatty liver disease.

BACKGROUND AND AIM: In patients with liver disease, etiology and body mass index (BMI) affects controlled attenuation parameter (CAP) assessment using FibroScan. We aimed to assess the performance characteristics of CAP for hepatic steatosis in patients with non-alcoholic fatty liver disease (NAFLD) stratified into obese (BMI ≥ 30 kg/m2) and non-obese (BMI < 30 kg/m2) subgroups. METHODS: In this prospective study, 219 consecutive adult NAFLD patients, with an available FibroScan value (liver stiffness measurement-[LSM] and CAP) and liver biopsy, were included. Receiver operating characteristic curves were used for assessment of the CAP cut-off values predicting different stages of hepatic steatosis. RESULTS: The mean ± standard deviation age of patients was 39.7 ± 10.5 years, 116 (53%) were males, and median (interquartile range) BMI was 31.8 (25.7-43.8) kg/m2. One hundred (45.7%) and 119 (54.3%) patients were non-obese and obese, respectively. The median values of CAP and LSM were significantly higher among obese patients as compared with the non-obese ones: 333 (304-368) vs. 320 (296-345) dB/m, p = 0.002 and 8.3 (6.1-11.4) vs. 6.6 (5.7-10.3) kPa, p = 0.012, respectively. Among non-obese NAFLD, optimal CAP cut-off values for steatosis (S) ≥ S1, ≥ S2, and ≥ S3 were 275 dB/m, 319 dB/m, and 337 dB/m, respectively. The corresponding CAP values among obese patients were higher as 285 dB/m, 340 dB/m, and 355 dB/m, respectively. BMI independently predicted CAP on multivariate analysis. The discordance of 2-grades between CAP and biopsy measured steatosis was seen in 13% in non-obese and 19.3% in obese NAFLD. CAP overestimated steatosis more often than underestimating it, with a higher proportion in obese NAFLD. CONCLUSION: In patients with NAFLD, interpretation of CAP requires consideration of BMI.

Controlled Attenuation Parameter for Assessment of Hepatic Steatosis in Indian Patients.

BACKGROUND/AIMS: The gold standard method for measurement of hepatic steatosis is liver histology. Controlled Attenuation Parameter (CAP) can measure hepatic steatosis non-invasively. We aimed to assess the accuracy of CAP for detection of hepatic steatosis. METHODS: A total of 462 patients (May 2012-January 2017)-89 non-alcoholic fatty liver disease, 182 chronic hepatitis B, 88 chronic hepatitis C and 103 patients with other etiologies who underwent simultaneous liver biopsy and CAP estimation using Transient Elastography (TE) were included. Steatosis was graded as S0: steatosis in 0-5% of hepatocytes, S1: 6-33%, S2: 34-66% and S3: 67-100%. Receiver Operating Characteristic (ROC) curves were plotted to evaluate the accuracy of CAP in detecting hepatic steatosis. Predictors of CAP were assessed by multivariate linear regression model. RESULTS: The mean age ± SD was 33.8 ± 11.6 years; 296 (64.1%) were males. On liver histology, steatosis grades S0, S1, S2 and S3 were seen in 331 (71.6%), 74 (16.0%), 39 (8.4%) and 18 (3.9%), respectively. The median CAP (IQR) values for S0, S1, S2, and S3 steatosis were 206 (176-252) dB/m, 295 (257-331) dB/m, 320 (296-356) dB/m, and 349 (306-363) dB/m, respectively. For estimation of ≥S1, ≥S2, and ≥S3 using CAP, AUROC were 0.879, 0.893, and 0.883, respectively. In multivariate analysis, only BMI (OR 1.18; CI, 1.11-1.26, P < 0.001) and grade of hepatic steatosis (grade 1, OR, 3.94; 95% CI, 1.58-9.84, P = 0.003; grade 2, OR 42.04; 95% CI, 4.97-355.31, P = 0.001 and grade 3, OR 35.83; 95% CI 4.31-297.61, P = 0.001) independently predicted CAP. CONCLUSIONS: CAP detects hepatic steatosis with good accuracy in Indian patients with various etiologies.