Diffusion kurtosis imaging as an imaging biomarker for predicting prognosis in chronic kidney disease patients.

BACKGROUND: Renal fibrosis is the strongest prognostic predictor of end-stage renal disease (ESRD) in chronic kidney disease (CKD). Diffusion kurtosis imaging (DKI) is a promising method of magnetic resonance imaging successfully used to assess renal fibrosis in immunoglobulin A nephropathy. This study aimed to be the first to evaluate the long-term prognostic value of DKI in CKD patients. METHODS: Forty-two patients with CKD were prospectively enrolled, and underwent DKI on a clinical 3T MR scanner. We excluded patients with comorbidities that could affect the volume or the components of the kidney. DKI parameters, including mean Kurtosis (K), mean diffusivity and apparent diffusion coefficient (ADC) of kidney cortex were obtained by region-of-interest measurement. We followed up these patients for a median of 43 months and investigated the correlations between each DKI parameter and overall renal prognosis. RESULTS: Both K and ADC values were correlated well with the estimated glomerular filtration rate (eGFR) on recruitment and the eGFR of the last visit in follow-up (P ˂ 0.001). K and ADC values were also well associated with the eGFR slopes in CKD patients, both with the first-last time point slope (P = 0.011 and P ˂ 0.001, respectively) and with the regression slope (P = 0.010 and P ˂ 0.001, respectively). Cox proportional hazard regression indicated that lower eGFR and ADC values independently predicted eGFR loss of ˃30% and ESRD. The receiver operating characteristic analysis showed that K and ADC values were predictable for renal prognosis, and ADC displayed better capabilities for both ESRD [area under the curve (AUC) 0.936, sensitivity 92.31%, specificity 82.76%] and the composite endpoint (eGFR loss ˃30% or ESRD) (AUC 0.881, sensitivity 66.67%, specificity 96.3%). CONCLUSIONS: Renal ADC values obtained from DKI showed significant predictive value for the prognosis of CKD patients, which could be a promising noninvasive technique in follow-up.

Radiomics Based on MRI as a Biomarker to Guide Therapy by Predicting Upgrading of Prostate Cancer From Biopsy to Radical Prostatectomy.

BACKGROUND: Biopsy Gleason score (GS) is crucial for prostate cancer (PCa) treatment decision-making. Upgrading in GS from biopsy to radical prostatectomy (RP) puts a proportion of patients at risk of undertreatment. PURPOSE: To develop and validate a radiomics model based on multiparametric magnetic resonance imaging (mp-MRI) to predict PCa upgrading. STUDY TYPE: Retrospective, radiomics. POPULATION: A total of 166 RP-confirmed PCa patients (training cohort, n = 116; validation cohort, n = 50) were included. FIELD STRENGTH/SEQUENCE: 3.0T/T2 -weighted (T2 W), apparent diffusion coefficient (ADC), and dynamic contrast enhancement (DCE) sequences. ASSESSMENT: PI-RADSv2 score for each tumor was recorded. Radiomic features were extracted from T2 W, ADC, and DCE sequences and Mutual Information Maximization criterion was used to identify the optimal features on each sequence. Multivariate logistic regression analysis was used to develop predictive models and a radiomics nomogram and their performance was evaluated. STATISTICAL TESTS: Student's t or chi-square were used to assess the differences in clinicopathologic data between the training and validation cohorts. Receiver operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was calculated. RESULTS: In PI-RADSv2 assessment, 67 lesions scored 5, 70 lesions scored 4, and 29 lesions scored 3. For each sequence, 4404 features were extracted and the top 20 best features were selected. The radiomics model incorporating signatures from the three sequences achieved better performance than any single sequence (AUC: radiomics model 0.868, T2 W 0.700, ADC 0.759, DCE 0.726). The combined mode incorporating radiomics signature, clinical stage, and time from biopsy to RP outperformed the clinical model and radiomics model (AUC: combined model 0.910, clinical model 0.646, radiomics model 0.868). The nomogram showed good performance (AUC 0.910) and calibration (P-values: training cohort 0.624, validation cohort 0.294). DATA CONCLUSION: Radiomics based on mp-MRI has potential to predict upgrading of PCa from biopsy to RP. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 5 J. Magn. Reson. Imaging 2020;52:1239-1248.

Computed Tomography Signs for Active Severe Gastrointestinal Bleeding.

Objective To summarize the positive signs on multi-detector CT angiography (CTA) for active severe gastrointestinal bleeding (GIB).Methods We retrospectively evaluated the clinical records and CT images of 93 patients with active severe GIB confirmed by digital subtraction angiography (DSA),endoscopy or surgery. The positive CT signs indicating the locations and causes of the bleeding were recorded. Results Intraluminal extravasation of contrast material was identified in 44 cases (47.3%),vascular malformation was found in 22 cases (23.7%),gastrointestinal tumor was detected in 18 cases (19.4%),focal or segmental abnormal bowel mucosal enhancement was present in 7 cases (7.5%),and diverticulum with abnormal enhancement was found in 2 cases (2.2%). Conclusion Positive signs for active severe GIB on CT are diverse and thus should be carefully interpreted in clinical setting.

Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma.

PURPOSE: To investigate the feasibility of using CT texture analysis (CTTA) to differentiate between low- versus high-grade urothelial carcinoma. METHODS: A total of 105 patients with high-grade urothelial carcinoma (HGUC, n = 106) and low-grade urothelial carcinoma (LGUC, n = 18) were included in this retrospective study. Both unenhanced and enhanced CT images representing the largest cross-sectional area of the tumor were chosen for CTTA performed using TexRAD software. Comparison of texture parameters, mean gray-level intensity (Mean), standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were made for the objective. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve was calculated for texture parameters that were significantly different (P < 0.05) for the purpose. Sensitivity (Se), specificity (Sp), positive predictive value, negative predictive value, and accuracy were calculated using the cut-off value of texture parameter with the highest AUC. RESULTS: Compared to HGUC, LGUC had significantly lower Mean (P = 0.001), Entropy (P = 0.002), and MPP (P < 0.001) on unenhanced and enhanced images and lower SD (P = 0.048) on enhanced images. There was no significant difference in skewness or kurtosis at any texture scale on unenhanced and enhanced images. A MPP <24.13 at fine texture scale on unenhanced images identified LGUC from HGUC with the highest AUC of 0.779 ± 0.065 (Se = 72.2%, Sp = 84.9%, PPV = 44.8%, NPV = 94.7%, and accuracy = 83.1%). CONCLUSIONS: CTTA proved to be a feasible tool for differentiating LGUC from HGUC. MPP quantified from fine texture scale on unenhanced images was the optimal diagnostic parameter for estimating histologic grade of urothelial carcinoma.

Differentiating pheochromocytoma from lipid-poor adrenocortical adenoma by CT texture analysis: feasibility study.

OBJECTIVES: To investigate the feasibility of using CT texture analysis (CTTA) to differentiate pheochromocytoma from lipid-poor adrenocortical adenoma (lp-ACA). METHODS: Ninety-eight pheochromocytomas and 66 lp-ACAs were included in this retrospective study. CTTA was performed on unenhanced and enhanced images. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different for the objective. Diagnostic accuracies were evaluated using the cutoff values of texture parameters with the highest AUCs. RESULTS: Compared to lp-ACAs, pheochromocytomas had significantly higher mean gray-level intensity (Mean), entropy, and mean of positive pixels (MPP), but lower skewness and kurtosis on unenhanced images (P < 0.001). On enhanced images, these texture-quantifiers followed a similar trend where Mean, entropy, and MPP were higher, but skewness and kurtosis were lower in pheochromocytomas. Standard deviation (SD) was also significantly higher in pheochromocytomas on enhanced images. Mean and MPP quantified from no filtration on unenhanced CT images yielded the highest AUC of 0.86 ± 0.03 (95% CI 0.81-0.91) at a cutoff value of 34.0 for Mean and MPP, respectively (sensitivity = 79.6%, specificity = 83.3%, accuracy = 81.1%). CONCLUSIONS: It was feasible to use CTTA to differentiate pheochromocytoma from lp-ACA.

High-pitch low-dose abdominopelvic CT with tin-filtration technique for detecting urinary stones.

OBJECTIVES: To evaluate urinary stone detection, radiation exposure, image quality, breathing-motion artifacts, and scanning time with high-pitch tin filter-based abdominopelvic CT. METHODS: Sixty-three consecutive patients with urolithiasis underwent non-enhanced abdominopelvic CT with both regular (120 kV, pitch 0.6) and low-dose (Sn150kV, pitch 3.0) protocols on a third-generation dual-source CT. Stone characteristics, image noise (SD), signal-to-noise ratio (SNR), subjective image quality on a 5-point likert scale breathing-motion artifacts, and scanning time were evaluated. Volume CT dose index (CTDIvol), dose-length product (DLP), effective dose (ED) were compared. RESULTS: A total of 157 urinary stones were detected by regular protocol; 154 were correctly identified by low-dose protocol with an overall detection rate of 98.1%. No significant differences were observed in SD, SNR, or subjective image quality between two protocols (P > 0.05). Compared to regular protocol, CTDIvol and ED were 56.6% (7.19 vs. 3.12 mGy, P < 0.001) and 55.6% (5.25 vs. 2.33 mSv, P < 0.001) lower; scanning time was 89.5% (7.9 vs. 0.83, P < 0.001) shorter; and breathing-motion artifacts were fewer (8 vs. 0 patients) with low-dose protocol. CONCLUSIONS: High-pitch abdominopelvic CT with Sn150kV substantially reduced radiation exposure and scanning time, while maintained stone detection and image quality and prevented breathing-motion artifacts.