Objective: To quantitatively compare the diagnostic values of conventional diffusion-weighted imaging (DWI) and introvoxel incoherent motion (IVIM) analysis of microstructural differences for clear cell renal cell carcinoma (ccRCC). Methods: Multiple b value DWIs and IVIMs were performed in patients with 146 ccRCCs, 42 with Grade Ⅰ, 46 with Grade Ⅱ, 28 with Grade Ⅲ and 30 with Grade Ⅳ. These tumours were divided into low (Ⅰ+Ⅱ, n = 88) and high grades (Ⅲ+Ⅳ, n = 58). The diagnostic efficacy of various diffusion parameters for predicting ccRCC grades was compared. Results: The mean signal-to-noise ratios (SNRs) of IVIM images at b = 0, 800 and 1500 s/mm2 were 31.9, 12.3 and 8.4, respectively. The apparent diffusion coefficient (ADC), D and D* values correlated negatively with ccRCC grading (r = -0.786,–0.913, −0879, p < 0.05). f values correlated positively with ccRCC grading (r = 0.811, p < 0.05). The ADC, D and D* values were higher for Grade Ⅱ ccRCC than that of Grade Ⅲ ccRCC (p < 005), however, f values were higher for Grade Ⅲ ccRCC than that of Grade Ⅱ ccRCC (p < 005). Receiver operating characteristic curve analyses showed that D values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ ccRCC grading. The area under the curve, sensitivity, specificity and accuracy of the D values were 0.963, 0.960; 90.9%, 89.1%; 81.0%,78.6 and 89.0%, 87.8%, respectively. For pairwise comparisons of receiver operating characteristic curves and diagnostic efficacy, ADC was worse than IVIM (all p < 0.05). Conclusion: IVIM parameters have better performance than ADC in differentiating ccRCC grading, given an adequate SNR of IVIM images. Advances in knowledge: 1. D values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ ccRCC grading. 2. IVIM parameters have better performance than ADC in differentiating ccRCC grading, given an adequate SNR of IVIM images. 3. The ADC, D and D* values correlated negatively with ccRCC grading, however, f values correlated positively with ccRCC grading.

The present study aimed to investigate the ability of CT-based texture analysis to differentiate invasive adenocarcinoma (IA) from pre-invasive lesions (PIL) or minimally IA (MIA) appearing as ground-glass opacity (GGO) nodules, and to further compare the performance of non-enhanced CT (NECT) images with that of contrast-enhanced CT (CECT) images. A total of 77 patients with GGO nodules and surgically confirmed pulmonary adenocarcinoma were included in the present retrospective study. Each GGO nodule was manually segmented and its texture features were extracted from NECT and CECT images using in-house developed software coded in MATLAB (MathWorks). The independent-samples t-test was used to select the texture features with statistically significant differences between IA and MIA/PIL. Multivariate logistic regression and receiver operating characteristics (ROC) curve analyses were performed to identify predictive features. Of the 77 GGO nodules, 12 were atypical adenomatous hyperplasia or adenocarcinoma in situ (15.6%), 36 were MIA (46.8%) and 29 were IA (37.7%). IA and MIA/PIL exhibited significant differences in most histogram features and gray-level co-occurrence matrix features (P<0.05). Multivariate logistic regression and ROC curve analyses revealed that smaller energy and higher entropy were significant differentiators of IA from MIA and PIL, irrespective of whether NECT images [area under the curve (AUC): 0.839, 0.859] or CECT images (AUC: 0.818, 0.820) are used. Texture analysis of CT images, regardless of whether NECT or CECT is used, has the potential to distinguish IA from PIL or MIA, particularly the parameters of energy and entropy. Furthermore, NECT images were simpler to obtain and no contrast agent was required; thus, analysis with NECT may be a preferred choice.

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Background Cystic renal cell carcinoma (CRCC) and cystic collecting duct carcinoma (CCDC) share similar oncogeni and some imaging findings. The aim of this study was to characterize the clinical and CT imagings features of CRCC and CCDC. Methods Thirty-three patients with CRCC and thirteen patients with CCDC with pathologically proven were retrospectively studied. Tumor characteristics were assessed. Results On CT imaging, 33 patients(100 %) with CRCC and 13 patients(100 %) with CCDC, tumors calcifications (8 vs. 9, P < 0.0001), had a clear boundary (capsule sign, 30 vs. 2, P < 0.0001), infiltrative appearance (1 vs. 13, P < 0.0001), exogenous appearance (29 vs. 3, P < 0.0001), invaded the renal pelvis or ureter (1 vs. 10, P < 0.0001), hemorrhage (1 vs. 10, P < 0.0001), had retroperitoneal lymph node or distant metastasis (2 vs. 10, P < 0.0001), thickened enhancing internal septations (31 vs. 2, P < 0.0001), and mural soft-tissue nodules (21 vs. 1, P < 0.0001). On MR imaging,13 patients(39 %) with CRCC and 4 patients(31 %) with CCDC, all CRCCs appeared hypointense on T1-weighted images and hyperintense on T2-weighted images, however, all CCDCs appeared hypointense on T1-weighted images and hypointense on T2-weighted images(P < 0.0001). 33 patients with CRCC, they were all alive from3 years to 10 years follow-up, however, 13 patients with CCDC, of which 11 patients were able to be followed up, and 9 patients expired within 5 years of the initial diagnosis and the others are currently still alive. Conclusions Distinguishing features of CRCC and CCDC included calcifications, capsule signs, infiltrative appearance, metastasis, internal septations, mural nodules and signal on CT or MR images. These imaging features may help in differentiating the two renal tumor types.

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Objective: To investigate the feasibility of magnetic resonance diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) for distinguishing Type 1 and 2 of papillary renal cell carcinoma (PRCC). Methods: A total of Type 1 (n = 20) and Type 2 (n = 16) of PRCC were examined by pathology. For DKI and IVIM, mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK), kurtosis anisotropy (KA), radial kurtosis (RK), diffusivity (D), pseudodiffusivity (D*) and perfusion fraction (f) were performed in assessment of type of PRCC. Results: The mean SNRs of IVIM and DKI images at b = 1500 and 2000 s/mm2 were 8.6 ± 0.8 and 7.8 ± 0.6. Statistically significant differences were observed in MD and D values (1.11 ± 0.23 vs 0.73 ± 0.13, 0.91 ± 0.24 vs 0.49 ± 0.13, p < 0.05) between Type 1 and Type 2 of PRCC, while comparable FA, RK, D* and f values were found between Type 1 and Type 2 of PRCC (p > 0.05). Statistically significant differences were observed in MK and KA values (1.23 ± 0.16 vs 1.91 ± 0.26, 1.49 ± 0.19 vs 2.36 ± 0.39, p < 0.05) between Type 1 and Type 2 of PRCC. Areas of MD, MK, KA and D values under ROC curves for differentiating Type 1 and Type 2 of PRCC were 0.836, 0.818, 0.881 and 0.766, respectively. Using MD, MK, KA and D values of 0.93, 1.64, 1.94, 0.68 as the threshold value for differentiating Type 1 from Type 2 of PRCC, the best result obtained had a sensitivity of 85.0%, 80.0%, 90.0%, 85.0%, a specificity 75.0%, 68.7%, 87.5%, 81.2%, and an accuracy of 83.3%, 80.5%, 88.9%, 86.1%, respectively. Conclusion: DKI and IVIM are feasible techniques for distinguishing type of PRCC, given an adequate SNR of IVIM and DKI images. Advances in knowledge: 1. MD and D values are higher for Type 1 of PRCC and lower for Type 2 of PRCC. 2. MK and KA values are higher for Type 2 of PRCC and lower for Type 1 of PRCC. 3. DKI and IVIM can be used as clinical biomarker for PRCC type’s differential diagnosis, given an adequate SNR.