scholarly journals Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xu Wang ◽  
Ge Song ◽  
Haitao Jiang
Keyword(s):  
Quantitative Data ◽  
Prediction Models ◽  
Clear Cell ◽  
Demographic Data ◽  
Region Of Interest ◽  
Renal Angiomyolipoma ◽  
Cell Renal Cell Carcinoma ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Abstract Background To investigate the value of using specific region of interest (ROI) on contrast-enhanced CT for differentiating renal angiomyolipoma without visible fat (AML.wovf) from small clear cell renal cell carcinoma (ccRCC). Methods Four-phase (pre-contrast phase [PCP], corticomedullary phase [CMP], nephrographic phase [NP], and excretory phase [EP]) contrast-enhanced CT images of AML.wovf (n = 31) and ccRCC (n = 74) confirmed by histopathology were retrospectively analyzed. The CT attenuation value of tumor (AVT), net enhancement value (NEV), relative enhancement ratio (RER), heterogeneous degree of tumor (HDT) and standardized heterogeneous ratio (SHR) were obtained by using different ROIs [small: ROI (1), smaller: ROI (2), large: ROI (3)], and the differences of these quantitative data between AML.wovf and ccRCC were statistically analyzed. Multivariate regression was used to screen the main factors for differentiation in each scanning phase, and the prediction models were established and evaluated. Results Among the quantitative parameters determined by different ROIs, the degree of enhancement measured by ROI (2) and the enhanced heterogeneity measured by ROI (3) performed better than ROI (1) in distinguishing AML.wovf from ccRCC. The receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of RER_CMP (2), RER_NP (2) measured by ROI (2) and HDT_CMP and SHR_CMP measured by ROI (3) were higher (AUC = 0.876, 0.849, 0.837 and 0.800). Prediction models that incorporated demographic data, morphological features and quantitative data derived from the enhanced phase were superior to quantitative data derived from the pre-contrast phase in differentiating between AML.wovf and ccRCC. Among them, the model in CMP was the best prediction model with the highest AUC (AUC = 0.986). Conclusion The combination of quantitative data obtained by specific ROI in CMP can be used as a simple quantitative tool to distinguish AML.wovf from ccRCC, which has a high diagnostic value after combining demographic data and morphological features.

scholarly journals Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT

2020 ◽  
Vol 93 (1114) ◽  
pp. 20200131
Author(s):  
Dong Han ◽  
Yong Yu ◽  
Nan Yu ◽  
Shan Dang ◽  
Hongpei Wu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Statistical Significance ◽  
Net Benefit ◽  
Cell Renal Cell Carcinoma ◽  
Who Grade ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Validation Set

Objective: Comparing the prediction models for the ISUP/WHO grade of clear cell renal cell carcinoma (ccRCC) based on CT radiomics and conventional contrast-enhanced CT (CECT). Methods: The corticomedullary phase images of 119 cases of low-grade (I and II) and high-grade (III and IV) ccRCC based on 2016 ISUP/WHO pathological grading criteria were analyzed retrospectively. The patients were randomly divided into training and validation set by stratified sampling according to 7:3 ratio. Prediction models of ccRCC differentiation were constructed using CT radiomics and conventional CECT findings in the training setandwere validated using validation set. The discrimination, calibration, net reclassification index (NRI) and integrated discrimination improvement index (IDI) of the two prediction models were further compared. The decision curve was used to analyze the net benefit of patients under different probability thresholds of the two models. Results: In the training set, the C-statistics of radiomics prediction model was statistically higher than that of CECT (p < 0.05), with NRI of 9.52% and IDI of 21.6%, both with statistical significance (p < 0.01).In the validation set, the C-statistics of radiomics prediction model was also higher but did not show statistical significance (p = 0.07). The NRI and IDI was 14.29 and 33.7%, respectively, both statistically significant (p < 0.01). Validation set decision curve analysis showed the net benefit improvement of CT radiomics prediction model in the range of 3–81% over CECT. Conclusion: The prediction model using CT radiomics in corticomedullary phase is more effective for ccRCC ISUP/WHO grade than conventional CECT. Advances in knowledge: As a non-invasive analysis method, radiomics can predict the ISUP/WHO grade of ccRCC more effectively than traditional enhanced CT.

Imaging of Solid Kidney Tumours in Children

1995 ◽  
Vol 36 (3) ◽  
pp. 254-260 ◽  
Author(s):  
C. Hugosson ◽  
R. Nyman ◽  
B. Jacobsson ◽  
H. Jorulf ◽  
K. Sackey ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Clear Cell ◽  
Wilms Tumour ◽  
Rhabdoid Tumour ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Renal Tumours ◽  
Enhanced Ct ◽  
Mr Characteristics

Eighteen children aged 6 months to 12 years with 20 solid renal tumours; 13 Wilms' tumours (WT), 2 clear cell sarcomas of the kidney, 1 malignant rhabdoid tumour of the kidney and 2 cases of bilateral nephroblastomatosis with Wilms' tumour underwent evaluation with US, CT and MR imaging. Contrast-enhanced CT and non-enhanced MR were equally accurate in determining the size and origin of the tumour but were unreliable in separation of stages I, II and III. US could only accurately assess the size of the tumours. MR characteristics varied somewhat between WTs and non-WTs but contrast-enhanced MR imaging might be useful for separation of WTs from nephroblastomatosis.

scholarly journals Giant Renal Angiomyolipoma by the Example of Clinical Cases

2021 ◽  
Vol 102 (5) ◽  
pp. 304-310
Author(s):  
S. V. Yadrentseva ◽  
N. V. Nudnov ◽  
Emil’ G. Gasymov
Keyword(s):  
Differential Diagnosis ◽  
Morphological Characteristics ◽  
High Sensitivity ◽  
Renal Angiomyolipoma ◽  
Distinctive Features ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Diagnostic Difficulties ◽  
Clinical Cases

The paper presents two clinical cases of patients with giant renal angiomyolipomas (AML), in one of whom its course was complicated by intratumoral hemorrhage. It describes key diagnostic criteria for computed tomography (CT), as well as the distinctive features of other neoplasms that should undergo a differential diagnosis. The similar clinical presentations and morphological characteristics of different renal neoplasms can cause certain diagnostic difficulties; however, the carefully collected historical data and distinctive criteria allow AML to be identified. Due to its high sensitivity and specificity, abdominal contrast-enhanced CT is an effective imaging technique in the detection and differential diagnosis of giant renal AML.

Contrast-enhanced CT in 100 clear cell renal cell cancers — an analysis of enhancement, tumour size, and survival

2015 ◽  
Vol 70 (12) ◽  
pp. 1357-1361 ◽  
Author(s):  
R. Veeratterapillay ◽  
R. Ijabla ◽  
D. Conaway ◽  
P. Haslam ◽  
N. Soomro ◽  
...  
Keyword(s):  
Renal Cell ◽  
Clear Cell ◽  
Tumour Size ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

scholarly journals Radiomics Analysis of Contrast-Enhanced CT Predicts Survival in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Lei Yan ◽  
Guangjie Yang ◽  
Jingjing Cui ◽  
Wenjie Miao ◽  
Yangyang Wang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Clinical Factors ◽  
Cell Renal Cell Carcinoma ◽  
Incremental Value ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced

PurposeTo develop and validate the radiomics nomogram that combines clinical factors and radiomics features to estimate overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC), and assess the incremental value of radiomics for OS estimation.Materials and MethodsOne hundred ninety-four ccRCC cases were included in the training cohort and 188 ccRCC patients from another hospital as the test cohort. Three-dimensional region-of-interest segmentation was manually segmented on multiphasic contrast-enhanced abdominal CT images. Radiomics score (Rad-score) was calculated from a formula generated via least absolute shrinkage and selection operator (LASSO) Cox regression, after which the association between the Rad-score and OS was explored. The radiomics nomogram (clinical factors + Rad-score) was developed to demonstrate the incremental value of the Rad-score to the clinical nomogram for individualized OS estimation, which was then evaluated in relation to calibration and discrimination.ResultsRad-score, calculated using a linear combination of the 11 screened features multiplied by their respective LASSO Cox coefficients, was significantly associated with OS. Calibration curves showed good agreement between the OS predicted by the nomograms and observed outcomes. The radiomics nomogram presented higher discrimination capability compared to clinical nomogram in the training (C-index: 0.884; 95% CI: 0.808–0.940 vs. 0.803; 95% CI: 0.705–0.899, P &lt; 0.05) and test cohorts (C-index: 0.859; 95% CI: 0.800–0.921 vs. 0.846; 95% CI: 0.777–0.915, P &lt; 0.05).ConclusionsThe radiomics nomogram may be used for predicting OS in patients with ccRCC, and radiomics is useful to assist quantitative and personalized treatment.

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