Can quantitative CT texture analysis be used to differentiate subtypes of renal cell carcinoma?

AIM: To investigate whether computed tomography (CT) texture analysis (TA) can be used to differentiate non-clear-cell renal cell carcinoma (non-ccRCC) from clear-cell RCC (ccRCC) and classify non-ccRCC subtypes. MATERIALS AND METHODS: One hundred ccRCC and 27 non-ccRCC (12 papillary and 15 chromophobe) were analysed. Texture parameters quantified from multiphasic CT images were compared for the objectives. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was calculated. The optimal discriminative texture parameters were used to produce support vector machine (SVM) classifiers. Diagnostic accuracy and 10-fold cross-validation was performed. RESULTS: Compared to ccRCC, non-ccRCC had significantly lower mean grey-level intensity (mean), standard deviation (SD), entropy, mean of positive pixels (MPP), and higher kurtosis (p<0.001). A model incorporating SD, entropy, MPP, and kurtosis produced an AUC of 0.94±0.03 with an accuracy of 87% (sensitivity=89%, specificity=92%) to identify non-ccRCC from ccRCC. Compared to chromophobe RCC, papillary RCC had significantly lower mean and MPP (p=0.002). A model incorporating SD, MPP, and skewness resulted in an AUC of 0.96±0.04 with an accuracy of 78% (sensitivity=87%, specificity=92%) to differentiate between papillary and chromophobe RCC. CONCLUSION: CT TA could potentially be used as a less invasive tool to classify histological subtypes of RCC.

Diffusional kurtosis imaging in assessing renal function and pathology of IgA nephropathy: a preliminary clinical study.

AIM: To evaluate renal fibrosis in immunoglobulin A nephropathy (IgAN) using diffusion kurtosis imaging (DKI). MATERIALS AND METHODS: Twenty patients with biopsy-proven IgAN were enrolled. DKI was performed on a clinical 3 T magnetic resonance imaging (MRI) system, and region-of-interest measurements were conducted to determine mean kurtosis (K), mean diffusivity (D), and apparent diffusion coefficient (ADC) of the kidney cortex. Renal biopsy specimens were scored based on the severity of renal fibrosis. The associations between the DKI data and clinicopathological parameters were investigated. RESULTS: Both the K and ADC were not only well correlated with the estimated glomerular filtration rate, but also significantly associated with the pathological scores of fibrosis, including the glomerular sclerosis index (K: r=0.759, p<0.001; ADC: r=-0.636, p=0.003) and the percentage of tubular atrophy and interstitial fibrosis (K: r=0.767, p<0.001; ADC: r=-0.702, p=0.001). Further receiver operating characteristic analysis showed that K demonstrated better diagnostic performance in discriminating severe glomerulosclerosis (area under curve [AUC] 0.970, sensitivity 81.8%, specificity 100%), and ADC displayed better capabilities in identifying severe tubular atrophy/interstitial fibrosis (AUC 0.976, sensitivity 100%, specificity 92.9%). CONCLUSION: This DKI method can be used to detect renal fibrosis in IgAN in a non-invasive manner and may provide additional information for characterisation and surveillance.

Uric acid versus non-uric acid urinary stones: differentiation with single energy CT texture analysis.

AIM: To evaluate the accuracy of computed tomography (CT) texture analysis (TA) to differentiate uric acid (UA) stones from non-UA stones on unenhanced CT in patients with urinary calculi with ex vivo Fourier transform infrared spectroscopy (FTIR) as the reference standard. MATERIALS AND METHODS: Fourteen patients with 18 UA stones and 31 patients with 32 non-UA stones were included. All the patients had preoperative CT evaluation and subsequent surgical removal of the stones. CTTA was performed on CT images using commercially available research software. Each texture feature was evaluated using the non-parametric Mann-Whitney test. Receiver operating characteristic (ROC) curves were created and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different. The features were used to train support vector machine (SVM) classifiers. Diagnostic accuracy was evaluated. RESULTS: Compared to non-UA stones, UA stones had significantly lower mean, standard deviation and mean of positive pixels but higher kurtosis (p<0.001) on both unfiltered and filtered texture scales. There were no significant differences in entropy or skewness between UA and non-UA stones. The average SVM accuracy of texture features for differentiating UA from non-UA stones ranged from 88% to 92% (after 10-fold cross validation). A model incorporating standard deviation, skewness, and kurtosis from unfiltered texture scale images resulted in an AUC of 0.965±00.029 with a sensitivity of 94.4% and specificity of 93.7%. CONCLUSION: CTTA can be used to accurately differentiate UA stones from non-UA stones in vivo using unenhanced CT images.

Prospective prediction of the major component of urinary stone composition with dual-source dual-energy CT in vivo.

AIM: To prospectively evaluate the diagnostic accuracy of dual-source dual-energy computed tomography (DSDECT) for predicting the major component and determining the composition of urinary calculi in patients with urolithiasis, using postoperative in vitro Fourier transform infrared spectroscopy (FT-IR) analysis as the reference standard. MATERIALS AND METHODS: Patients with known urolithiasis underwent preoperative DSDECT evaluation, and subsequently, underwent surgical removal of the stones. All patients were examined using the dual-energy renal stone protocol. Material-specific chromatic images were made using dedicated post-processing software. The final determination of stone composition was made using FT-IR postoperatively. Diagnostic parameters of DSDECT for predicting the major component and detecting the presence of four composition types were calculated. RESULTS: A total of 81 urinary calculi were included in this study. Forty-three were pure stones and 38 were mixed stones according to FT-IR. DSDECT correctly identified the major component of all pure stones and 36 mixed stones. The major component of two mixed stones with uric acid as the major component was falsely interpreted as calcium oxalate. The overall accuracy of DSDECT for predicting the major component of stones was 97.5% (79/81). The accuracy of DSDECT for detecting the presence of four types of composition, uric acid, cysteine, hydroxyapatite, and calcium oxalate, was 97.5% (79/81), 93.8% (76/81), 80.2% (65/81), and 93.8% (76/81), respectively. CONCLUSION: DSDECT could accurately predict the major component of urinary calculi and detect uric acid, cysteine, and calcium oxalate with a satisfactory accuracy.