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

Moldovanu, Claudia-Gabriela; Boca, Bianca; Lebovici, Andrei; Tamas-Szora, Attila; Feier, Diana; Crişan, Nicolae; Andraș, Iulia; Buruian, Mircea

doi:10.3390/jpm11010008

Cited by 19 publications

(20 citation statements)

References 65 publications

(27 reference statements)

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“…Most previous studies constructed ML models only based on CT radiomics features, which ignored the importance of traditional clinical and radiological information [ 26 , 41 , 44 ]. In our study, some parameters with clinical and radiological information that have the potential to be risk factors in the WHO/ISUP nuclear grade of CCRCC determined by multivariate regression model were fed into ML model, and the radiomics features combined with the clinicoradiological characteristics showed a better performance for the discrimination of CCRCC grades.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Guo

et al. 2021

Insights Imaging

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Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. Results SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. Conclusion A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Guo

et al. 2021

Insights Imaging

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“…Several studies have demonstrated that machine learning (ML)-based CT radiomics models can distinguish Fuhrman grade or WHO/ISUP grade of CCRCC (17,19,(28)(29)(30). However, most of these studies built ML models based on radiomics features only, neglecting the importance of clinical and radiological characteristics (17,19). The radiomics-derived data are not a panacea for computerized clinical decision- (35).…”

Section: Discussionmentioning

confidence: 99%

“…Contrast-enhanced CT examination revealed different information as time progressed. To the best of our knowledge, features extracted from full-phase or CMP combined with NP images are the most common objects of the ML-based radiomics model to predict the Fuhrman or WHO/ISUP grade of CCRCC (19,28,36,37). Huhdanpaa et al (38) found that absolute enhancement and residual enhancement in the NP phase are both more heterogeneous for low-grade tumors.…”

Section: Discussionmentioning

confidence: 99%

“…Pre-contrast CT of the abdomen was first acquired, followed by two post-contrast CT scans obtained in corticomedullary phase (CMP, 25-28 s after contrast agent was administrated) and nephrographic phase (NP, 65-70 s after contrast agent was administrated). Finally, excretory phase was acquired (EP, 6-8 min after contrast agent was administrated) (17)(18)(19).…”

Section: Ct Imaging Parametersmentioning

confidence: 99%

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A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma

Guo

et al. 2021

Front. Oncol.

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ObjectiveThis study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs).Methods370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).ResultsUsing multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness.ConclusionThe CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.

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“…However, most of the previous studies on the prediction of nuclear grading of ccRCC by texture analysis have been based on the Fuhrman classification system, which has some inevitable inadequacies, such as interpretation difficulties and poor reproducibility in clinical applications (10,14). Besides the high application value for ccRCC, the WHO/ISUP nuclear grading is also a reliable prognosis indicator of patients with ccRCC (15).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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

Cited by 19 publications

References 65 publications

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma

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

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