Search

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC, and accurate grading is crucial for prognosis and treatment selection. Biopsy is the reference standard for grading, but MRI methods can improve and complement the grading procedure. Purpose: Assess the performance of diffusion relaxation correlation spectroscopic imaging (DR‐CSI) in grading ccRCC. Study Type: Prospective. Subjects: 79 patients (age: 58.1 +/− 11.5 years; 55 male) with ccRCC confirmed by histopathology (grade 1, 7; grade 2, 45; grade 3, 18; grade 4, 9) following surgery. Field Strength/Sequence: 3.0 T MRI scanner. DR‐CSI with a diffusion‐weighted echo‐planar imaging sequence and T2‐mapping with a multi‐echo spin echo sequence. Assessment: DR‐CSI results were analyzed for the solid tumor regions of interest using spectrum segmentation with five sub‐region volume fraction metrics (VA, VB, VC, VD, and VE). The regulations for spectrum segmentation were determined based on the D‐T2 spectra of distinct macro‐components. Tumor size, voxel‐wise T2, and apparent diffusion coefficient (ADC) values were obtained. Histopathology assessed tumor grade (G1–G4) for each case. Statistical Tests: One‐way ANOVA or Kruskal–Wallis test, Spearman's correlation (coefficient, rho), multivariable logistic regression analysis, receiver operating characteristic curve analysis, and DeLong's test. Significance criteria: P < 0.05. Results: Significant differences were found in ADC, T2, DR‐CSI VB, and VD among the ccRCC grades. Correlations were found for ccRCC grade to tumor size (rho = 0.419), age (rho = 0.253), VB (rho = 0.553) and VD (rho = −0.378). AUC of VB was slightly larger than ADC in distinguishing low‐grade (G1‐G2) from high‐grade (G3‐G4) ccRCC (0.801 vs. 0.762, P = 0.406) and G1 from G2 to G4 (0.796 vs. 0.647, P = 0.175), although not significant. Combining VB, VD, and VE had better diagnostic performance than combining ADC and T2 for differentiating G1 from G2‐G4 (AUC: 0.814 vs 0.643). Data Conclusion: DR‐CSI parameters are correlated with ccRCC grades, and may help to differentiate ccRCC grades. Evidence Level: 2 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Purpose: To compare various models of diffusion-weighted imaging including mono-exponential, bi-exponential, diffusion kurtosis (DK) and fractional-order calculus (FROC) models in diagnosing prostate cancer (PCa) in transition zone (TZ) and distinguish the high-grade PCa [Gleason score (GS) ≥ 7] lesions from the total of low-grade PCa (GS ≤ 6) lesions and benign prostatic hyperplasia (BPH) in TZ. Methods: 80 Patients with 103 lesions were included in this study. Nine metrics [including apparent diffusion coefficient (ADC) derived from mono-exponential model, slow diffusion coefficient (Ds), fast diffusion coefficient (Df),, and f (the fraction of fast diffusion) from bi-exponential model; mean diffusivity (MD) and mean kurtosis (MK) from DK model; diffusion coefficient (D), fractional-order derivative in space (β), and spatial metric (μ) from FROC model] were calculated. Comparisons between BPH and PCa lesions as well as between clinically significant PCa (CsPCa) (GS ≥ 7, n = 31) and clinically insignificant lesions (Cins) (GS ≤ 6 and BPH, n = 72) of these metrics were conducted. Mann–Whitney U-test and receiver operating characteristic (ROC) analysis were used for statistical evaluations. Results: The areas under the ROC curve (AUC) values of β derived from FROC model were 0.778 and 0.853 in differentiating PCa from BPH and in differentiating CS (GS ≥ 7) from Cins (GS ≤ 6 and BPH), both were the highest compared to other metrics. The AUC value of β was significantly higher than that of ADC (P = 0.009) in differentiating CS from Cins, while the differentiation between BPH and PCa did not reach the statistical significance when comparing with ADC (P = 0.089). Conclusion: Although no significant difference was found in distinguishing PCa from BPH, the metric β derived from FROC model was superior to other diffusion metrics in differentiation between CS and Cins in TZ. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the value of radiomics features from diffusion-weighted imaging (DWI) in differentiating muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC).Methods: This retrospective study included 218 pathologically confirmed bladder cancer patients (training set: 131 patients, 86 MIBC; validation set: 87 patients, 55 MIBC) who underwent DWI before biopsy through transurethral resection (TUR) between July 2014 and December 2018. Radiomics models based on DWI for discriminating state of muscle-invasive were built using random forest (RF) and all-relevant (AR) methods on the training set and were tested on validation set. Combination models based on TUR data were also built. Discrimination performances were evaluated with the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, and F1 and F2 scores. Qualitative MRI evaluation based on morphology was performed for comparison.Results: No significant difference was found between RF and AR models. RF model was more sensitive than TUR (0.873 vs 0.655, p = 0.019) for discriminating muscle-invasive bladder cancer. When combining RF with TUR, the sensitivity increased to 0.964, significantly higher than TUR (0.655, p < 0.001), MRI evaluation (0.764, p = 0.006), and the combination of TUR and MRI (0.836, p = 0.046). Combining RF and TUR achieved the highest accuracy of 0.897 and F2 score of 0.946.Conclusion: Combining DWI radiomics features with TUR could improve the sensitivity and accuracy in discriminating the presence of muscle invasion in bladder cancer for clinical practice. Multicenter, prospective studies are needed to confirm our results.Key Points: • Twenty-seven to 51% of superficial bladder cancers diagnosed by transurethral resection are upstaged to muscle-invasive at radical cystectomy, suggesting its poor sensitivity for discriminating muscle-invasive bladder cancer. • A small subset of selected all-relevant radiomics features exhibited an equivalent performance compared to that of all the extracted features, confirming that radiomics data contained redundant or irrelevant features and that feature selection should be performed in building radiomics models. • Combining DWI radiomics features with transurethral resection could improve in clinical practice the sensitivity and accuracy for the detection of muscle invasion in bladder cancer. [ABSTRACT FROM AUTHOR]

Background: Microstructural changes of lupus nephritis (LN) kidney such as inflammatory cell infiltration or fibrosis could influence water molecular movement or diffusion, which indicates that diffusion-weighted imaging (DWI) may become a valuable tool in evaluation of this disease.Purpose: To explore whether multiparameter diffusion-weighted imaging (mDWI) could contribute to characterize pathological patterns in LN patients.Study Type: Retrospective.Population: Twenty-two patients with LN.Field Strength/sequence: Multi-b value DWI was performed with a 3.0 T scanner.Assessment: Apparent diffusion coefficient (ADC)m , perfusion-related diffusion coefficient (Df ), molecular diffusion coefficient (Ds ), perfusion fraction (f), ADCs , α, ADCk , and mean kurtosis (MK) were calculated by monoexponential, biexponential, stretched-exponential, and kurtosis models fits, respectively.Statistical Tests: Independent sample t-test, Pearson analysis and receiver operating characteristic (ROC).Results: In the whole group, the activity index (AI) correlated significantly with alpha values in the medulla (rho = -0.54, P = 0.03). The chronicity index (CI) correlated significantly with Ds values in the medulla (rho = -0.61, P = 0.02). No significant association was found between any other diffusion parameter and histologic grade with all P > 0.05. For differentiating proliferative LN (Class III or IV) from Class V, the area under the ROC curve (AUC) of alpha in the medulla was 0.833 (P = 0.023). DATA CONCLUSION: mDWI might be used for the characterization of pathological patterns in LN patients.Level Of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1075-1084. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the utility of MR R2*-mapping and the optimal time-point for assessing the response of pulmonary metastatic renal cell carcinoma (mRCC) to anti-angiogenic targeted therapy (aATT).Materials and Methods: The exploration-sample group and the validation-sample group consisted of 22 and 16 patients. The parameters of MR R2*-mapping, including the R2* value at each time-point (R2*base, R2*1cyc and R2*2cyc) and change between different time-points (R2*(1cyc-base)/base, R2*(2cyc-base)/base and R2*(2cyc-1cyc)/1cyc), were evaluated with a receiver-operating-characteristic analysis, and a cut-off value derived from the clinical outcome was applied to the Kaplan-Meier method to assess the value of R2* mapping and Response-Evaluation-Criteria in Solid Tumours (RECIST) during treatment evaluation.Results: The inter-, intra-observer agreements and inter-scan consistency were excellent (p > 0.80). For the exploration-sample group, the areas under the curve for the parameters of MR R2* mapping were 0.55, 0.60, 0.83, 0.64, 0.88 and 0.83 for R2*base, R2*1cyc, R2*2cyc, R2*(1cyc-base)/base, R2*(2cyc-base)/base and R2*(2cyc-1cyc)/1cyc. For the validation-sample, R2*(2cyc-base)/base better predicted progression-free survival (p = 0.03) than RECIST and other R2* mapping parameters with a lower p value.Conclusion: Assessing aATT outcome based on changes in the R2* value between baseline and second treatment is more accurate than assessment at other time-points and assessment based on the RECIST.Key Points: • The inter-scan consistency of R2*-mapping in pulmonary mRCC are excellent. • The intra-/inter-observer agreement of R2* mapping in pulmonary mRCC are excellent. • Using changes in R2* value between baseline/after second-treatment is better than RECIST. • The choice of baseline/after second treatment is better than other time-points. [ABSTRACT FROM AUTHOR]