scholarly journals Comparison of Different Machine Models Based on Contrast-Enhanced Computed Tomography Radiomic Features to Differentiate High From Low Grade Clear Cell Renal Cell Carcinomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Xu Pei ◽  
Ping Wang ◽  
Jia-Liang Ren ◽  
Xiao-Ping Yin ◽  
Lu-Yao Ma ◽  
...  
Keyword(s):  
Renal Cell ◽  
Clear Cell ◽  
Multivariate Logistic Regression Model ◽  
Low Grade ◽  
Training Set ◽  
Tree Model ◽  
Renal Cell Carcinomas ◽  
Svm Model ◽  
Enhanced Computed Tomography ◽  
Testing Set

PurposeThis study was to investigate the role of different radiomics models with enhanced computed tomography (CT) scan in differentiating low from high grade renal clear cell carcinomas.Materials and MethodsCT data of 190 cases with pathologically confirmed renal cell carcinomas were collected and divided into the training set and testing set according to different time periods, with 122 cases in the training set and 68 cases in the testing set. The region of interest (ROI) was delineated layer by layer.ResultsA total of 402 radiomics features were extracted for analysis. Six of the radiomic parameters were deemed very valuable by univariate analysis, rank sum test, LASSO cross validation and correlation analysis. From these six features, multivariate logistic regression model, support vector machine (SVM), and decision tree model were established for analysis. The performance of each model was evaluated by AUC value on the ROC curve and decision curve analysis (DCA). Among the three prediction models, the SVM model showed a high predictive efficiency. The AUC values of the training set and the testing set were 0.84 and 0.83, respectively, which were significantly higher than those of the decision tree model and the multivariate logistic regression model. The DCA revealed a better predictive performance in the SVM model that possessed the highest degree of coincidence.ConclusionRadiomics analysis using the SVM radiomics model has highly efficiency in discriminating high- and low-grade clear cell renal cell carcinomas.

The Diagnostic Accuracy of Percutaneous Renal Needle Core Biopsy and Its Potential Impact on the Clinical Management of Renal Cortical Neoplasms

2014 ◽  
Vol 138 (12) ◽  
pp. 1673-1679 ◽  
Author(s):  
Lan L. Gellert ◽  
Rohit Mehra ◽  
Ying-Bei Chen ◽  
Anuradha Gopalan ◽  
Samson W. Fine ◽  
...  
Keyword(s):  
Clinical Management ◽  
Renal Cell ◽  
Core Biopsy ◽  
Clear Cell ◽  
Renal Tumors ◽  
Low Grade ◽  
Renal Cell Carcinomas ◽  
Biopsy Diagnosis ◽  
Potential Impact ◽  
Renal Cortical Neoplasms

Context While biopsies are now increasingly being performed for the diagnosis of renal cortical neoplasms, the influence of the rendered pathological diagnoses on the clinical management is only rarely documented. Objectives To report our experience with consecutively performed renal biopsies and the potential impact of the diagnosis on subsequent clinical management. Design Material from needle biopsies performed consecutively at our institution between 2006 and 2011 was reviewed. The influence of the reported pathology results on the clinical management was determined from patient follow-up medical record review. Results In total, 218 percutaneous biopsies for renal masses were performed during this period. Among them, 181 (83%) yielded neoplastic tissue, including 81 clear cell renal cell carcinomas, 29 low-grade oncocytic neoplasms, 7 papillary renal cell carcinomas, 5 clear cell papillary renal cell carcinomas, 5 angiomyolipomas, and 14 urothelial carcinomas. Fourteen additional cases (6%) contained lesional material from clinically known nonneoplastic processes, for a total diagnostic yield of 89%. Twenty-three (11%) were nonrepresentative of lesional tissue. In 10 of these, repeat biopsies or resections established the diagnosis of renal tumors. Biopsy diagnosis was confirmed in 29 of 30 cases (97%) on subsequent nephrectomy. Following the biopsy diagnosis, there were significant differences in the clinical management; overall, 79% of clear cell renal cell carcinomas received therapeutic interventions, and 17% were put on active surveillance. In contrast, 77% of the benign or low-grade lesions were put on active surveillance. Conclusions Accurate and specific diagnosis can be rendered on renal core biopsy in most renal tumors, and the biopsy diagnosis can have a definitive role in their clinical management.

Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma

2021 ◽  
pp. 1-12
Author(s):  
Ruo-Han Yin ◽  
You-Chang Yang ◽  
Xiao-Qiang Tang ◽  
Hai-Feng Shi ◽  
Shao-Feng Duan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Arterial Phase ◽  
Support Vector ◽  
Low Grade ◽  
Relative Standard ◽  
Enhanced Computed Tomography ◽  
Testing Set

OBJECTIVE: To develop and test an optimal machine learning model based on the enhanced computed tomography (CT) to preoperatively predict pathological grade of clear cell renal cell carcinoma (ccRCC). METHODS: A retrospective analysis of 53 pathologically confirmed cases of ccRCC was performed and 25 consecutive ccRCC cases were selected as a prospective testing set. All patients underwent routine preoperative abdominal CT plain and enhanced scans. Renal tumor lesions were segmented on arterial phase images and 396 radiomics features were extracted. In the training set, seven discrimination classifiers for high- and low-grade ccRCCs were constructed based on seven different machine learning models, respectively, and their performance and stability for predicting ccRCC grades were evaluated through receiver operating characteristic (ROC) analysis and cross-validation. Prediction accuracy and area under ROC curve were used as evaluation indices. Finally, the diagnostic efficacy of the optimal model was verified in the testing set. RESULTS: The accuracies and AUC values achieved by support vector machine with radial basis function kernel (svmRadial), random forest and naïve Bayesian models were 0.860±0.158 and 0.919±0.118, 0.840±0.160 and 0.915±0.138, 0.839±0.147 and 0.921±0.133, respectively, which showed high predictive performance, whereas K-nearest neighborhood model yielded lower accuracy of 0.720±0.188 and lower AUC value of 0.810±0.150. Additionally, svmRadial had smallest relative standard deviation (RSD, 0.13 for AUC, 0.17 for accuracy), which indicates higher stability. CONCLUSION: svmRadial performs best in predicting pathological grades of ccRCC using radiomics features computed from the preoperative CT images, and thus may have high clinical potential in guiding preoperative decision.

scholarly journals MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier

2021 ◽  
Vol 11 ◽  
Author(s):  
Xin-Yuan Chen ◽  
Yu Zhang ◽  
Yu-Xing Chen ◽  
Zi-Qiang Huang ◽  
Xiao-Yue Xia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Cell ◽  
Clear Cell ◽  
Texture Features ◽  
Low Grade ◽  
High Grade ◽  
Multi Layer Perceptron ◽  
Training Set ◽  
Cell Renal Cell Carcinoma ◽  
Validation Set

ObjectiveTo develop a machine learning (ML)-based classifier for discriminating between low-grade (ISUP I-II) and high-grade (ISUP III-IV) clear cell renal cell carcinomas (ccRCCs) using MRI textures.Materials and MethodsWe retrospectively evaluated a total of 99 patients (with 61 low-grade and 38 high-grade ccRCCs), who were randomly divided into a training set (n = 70) and a validation set (n = 29). Regions of interest (ROIs) of all tumors were manually drawn three times by a radiologist at the maximum lesion level of the cross-sectional CMP sequence images. The quantitative texture analysis software, MaZda, was used to extract texture features, including histograms, co-occurrence matrixes, run-length matrixes, gradient models, and autoregressive models. Reproducibility of the texture features was assessed with the intra-class correlation coefficient (ICC). Features were chosen based on their importance coefficients in a random forest model, while the multi-layer perceptron algorithm was used to build a classifier on the training set, which was later evaluated with the validation set.ResultsThe ICCs of 257 texture features were equal to or higher than 0.80 (0.828–0.998. Six features, namely Kurtosis, 135dr_RLNonUni, Horzl_GLevNonU, 135dr_GLevNonU, S(4,4)Entropy, and S(0,5)SumEntrp, were chosen to develop the multi-layer perceptron classifier. A three-layer perceptron model, which has 229 nodes in the hidden layer, was trained on the training set. The accuracy of the model was 95.7% with the training set and 86.2% with the validation set. The areas under the receiver operating curves were 0.997 and 0.758 for the training and validation sets, respectively.ConclusionsA machine learning-based grading model was developed that can aid in the clinical diagnosis of clear cell renal cell carcinoma using MRI images.

Low-grade metastases in high-grade clear cell renal cell carcinomas

2016 ◽  
Vol 20 ◽  
pp. 13-18 ◽  
Author(s):  
José I. López ◽  
Lorena Mosteiro ◽  
Rosa Guarch ◽  
Gorka Larrinaga ◽  
Rafael Pulido ◽  
...  
Keyword(s):  
Renal Cell ◽  
Clear Cell ◽  
Low Grade ◽  
High Grade ◽  
Renal Cell Carcinomas

scholarly journals Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features

2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Claudia-Gabriela Moldovanu ◽  
Bianca Boca ◽  
Andrei Lebovici ◽  
Attila Tamas-Szora ◽  
Diana Sorina Feier ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Predictive Ability ◽  
Nuclear Grade ◽  
Training Set ◽  
Cell Renal Cell Carcinoma ◽  
Validation Set ◽  
Sensitivity Specificity

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92–1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

scholarly journals PD14-07 DIFFERENTIATION BETWEEN CLEAR CELL RENAL CELL CARCINOMAS AND ONCOCYTOMAS USING TEXTURE ANALYSIS OF CT IMAGES

2016 ◽  
Vol 195 (4S) ◽  
Author(s):  
Vinay Duddalwar ◽  
Xuejun Zhang ◽  
Darryl Hwang ◽  
Steven Cen ◽  
Felix Yap ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Renal Cell ◽  
Clear Cell ◽  
Ct Images ◽  
Renal Cell Carcinomas

Value of intravoxel incoherent motion for differential diagnosis of renal tumors

2018 ◽  
Vol 60 (3) ◽  
pp. 382-387 ◽  
Author(s):  
Qingqiang Zhu ◽  
Wenrong Zhu ◽  
Jing Ye ◽  
Jingtao Wu ◽  
Wenxin Chen ◽  
...  
Keyword(s):  
Renal Cell ◽  
Clear Cell ◽  
Threshold Value ◽  
Intravoxel Incoherent Motion ◽  
Renal Tumors ◽  
Threshold Values ◽  
Renal Cell Carcinomas ◽  
Perfusion Fraction ◽  
D Values ◽  
Tumor Types

Background Few studies have reported on the use of intravoxel incoherent motion (IVIM) for renal tumors. Purpose To investigate the value of IVIM for distinguishing renal tumors. Material and Methods Thirty-one patients with clear cell renal cell carcinomas (CCRCCs), 13 patients with renal angiomyolipomas with minimal fat (RAMFs), eight patients with chromophobe renal cell carcinomas (ChRCCs), and ten patients with papillary renal cell carcinomas (PRCCs) were examined. The tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f) were calculated. Results The D and f values were highest for CCRCCs, lowest for PRCCs, and intermediate for ChRCCs and RAMFs ( P < 0.05). The D values of CCRCCs differed significantly from those of ChRCCs and PRCCs ( P < 0.05). The D* values were highest for RAMFs, lowest for ChRCCs, and intermediate for CCRCCs and PRCCs ( P < 0.05). Statistically significant differences were observed between the D* values of CCRCCs and RAMFs ( P < 0.05). The D* values of the CCRCCs differed significantly from the D* values of the ChRCCs ( P < 0.05). Using the D and f values of 1.10 and 0.41, respectively, as the threshold values for differentiating CCRCCs from RAMFs, ChRCCs, and PRCCs, the best results had sensitivities of 81.0% and 66.8% and specificities of 85.7% and 81.0%, respectively. Using the D* value of 0.038 as the threshold value for differentiating RAMFs from CCRCCs, ChRCCs, and PRCCs, the best result obtained had a sensitivity of 90.5% and specificity of 76.2%. Conclusion IVIM may provide information for differentiating renal tumor types.

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