CT-based radiomic model predicts high grade of clear cell renal cell carcinoma

Ding, Jiang; Zhou, Xing; Jiang, Zhenxing; Chen, Jie; Liang, Pan; Qiu, Jiawei; Xing, Wei

doi:10.1016/j.ejrad.2018.04.013

Cited by 126 publications

(86 citation statements)

References 33 publications

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“…However, the abundance of predictive modeling techniques means that it is important to choose the correct one for predicting tumor status. As some previous radiomics studies (16,28) (table 1). The SVM was the bestperforming classifier in this study.…”

Section: Discussionsupporting

confidence: 73%

“…For this purpose, two approaches are attractive for researchers at present. One is the Apparent diffusion coefficient (ADC) value in MRI imaging (14), and the other is CT-based semiquantitative and quantitative techniques (15,16).…”

Section: Introductionmentioning

confidence: 99%

“…These results suggest that the AUC logistic regression model is approximately similar to results obtained in previous studies. Juice Dinga et al(16) used a texture-score based logistic regression model on a training cohort and the AUC was 0.878. When predictive models were applied on the validation cohort, good results were still obtained (AUC > 0.670).…”

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confidence: 99%

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Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

et al. 2020

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To identify optimal classification methods for computed tomography (CT) radiomicsbased preoperative prediction of clear cells renal cell carcinoma (ccRCC) grade.Methods and material: Seventy-one ccRCC patients (31 low-grade and 40 high-grade) were included in the study. All tumors were segmented manually on CT images, and three image preprocessing techniques (Laplacian of Gaussian, wavelet filter, and discretization of the intensity values) were applied on tumor volumes. In total, 2530 radiomics features (tumor shape and size, intensity statistics, and texture) were extracted from each segmented tumor volume. Univariate analysis was performed to assess the association of each feature with the histological condition. In the case of multivariate analysis, the following was implemented: three feature selection including the least absolute shrinkage and selection operator (LASSO), student's t-test and minimum Redundancy Maximum Relevance (mRMR) algorithms. These selected features were then used to construct three classification models (SVM, random forest, and logistic regression) to discriminate the high from low-grade ccRCC at nephrectomy. Lastly, multivariate model performance was evaluated on the bootstrapped validation cohort using the area under receiver operating characteristic curve (AUC).Results: Univariate analysis demonstrated that among different image sets, 128 bin discretized images have statistically significant different (q-value < 0.05) texture parameters with a mean of AUC 0.74±3 (q-value < 0.05). The three ML-based classifier shows proficient discrimination of the high from low-grade ccRCC. The AUC was 0.78 in logistic regression, 0.62 in random forest, and 0.83 in SVM model, respectively. Conclusion:Radiomics features can be a useful and promising non-invasive method for preoperative evaluation of ccRCC Fuhrman grades.

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Section: Discussionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

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Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

et al. 2020

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“…The studies based on CT were relatively simple; thus, the application of the recently popular radiomic analysis in FG prediction achieved good results. 9 However, Orlhac et al pointed out that there is difference in texture parameters between different CT scanners and that the scanning parameters of different hospitals are different, which will affect the generalization ability of the model. 30 The CT radiomic model based on multi-phase enhancement cannot be generalized to CTA data and more impossible to be applied to the MR image.…”

Section: Discussionmentioning

confidence: 99%

<p>New Preoperative Nomogram Using the Centrality Index to Predict High Nuclear Grade Clear Cell Renal Carcinoma</p>

Zhan¹,

Lou²,

Zhang³

et al. 2020

CMAR

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Objective: Nuclear grading is an independent prognosis factor of clear-cell renal cell carcinoma (ccRCC). A non-invasive preoperative predictive WHO/International Society of Urologic Pathology (WHO/ISUP) grading of ccRCC model is needed for clinical use. The anatomical complexity scoring system can span a variety of image modalities. The Centrality index (CI) is a quantitatively anatomical score commonly used for renal tumors. The purpose of this study was to develop a simple model to predict WHO/ISUP grading based on CI. Materials and methods: The data in this study were from 248 ccRCC patients from five hospitals. We developed three predictive models using training data from 167 patients: a CIonly model, a valuable clinical parameter model and a fusion model of CI with valuable clinical parameters. We compared and evaluated the three models by discrimination, clinical usefulness and calibration, then tested them in a set of validation data from 81 patients.Results: The fusion model consisting of CI and tumor size (valuable clinical parameter) had an area under the curve (AUC) of 0.82. In the validation set, the AUC was 0.85. The decision curve showed that the model had a good net benefit between the threshold probabilities of 5-80%. And the calibration curve showed good calibration in the training set and validation set.Conclusion: This study confirms that CI is associated with the WHO/ISUP grade of ccRCC, and the possibility that a bivariate model incorporating tumor size may help urologist's evaluation patients' prognostic.

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“…Previous studies have shown that traditional image features, such as enhancement characteristics, could provide valuable predictive information for identifying benign and malignant tumors, tumor subtypes, and tumor grades in RCC (16,17). Nowadays, with the development of radiomics technology, radiomics methods that translate medical imaging data into high-dimension data can also be used as non-invasive biomarkers for prognosis or prediction (18)(19)(20)(21). However, it remains unclear whether it is possible to predict the presence or absence of CN in ccRCC tumors by using radiomics features and traditional features based on CT images.…”

Section: Sample Size Considerationmentioning

confidence: 99%

CT-Based Radiomics Signature for Preoperative Prediction of Coagulative Necrosis in Clear Cell Renal Cell Carcinoma

Liu

et al. 2020

Korean J Radiol

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The presence of coagulative necrosis (CN) in clear cell renal cell carcinoma (ccRCC) indicates a poor prognosis, while the absence of CN indicates a good prognosis. The purpose of this study was to build and validate a radiomics signature based on preoperative CT imaging data to estimate CN status in ccRCC. Materials and Methods: Altogether, 105 patients with pathologically confirmed ccRCC were retrospectively enrolled in this study and then divided into training (n = 72) and validation (n = 33) sets. Thereafter, 385 radiomics features were extracted from the three-dimensional volumes of interest of each tumor, and 10 traditional features were assessed by two experienced radiologists using triple-phase CT-enhanced images. A multivariate logistic regression algorithm was used to build the radiomics score and traditional predictors in the training set, and their performance was assessed and then tested in the validation set. The radiomics signature to distinguish CN status was then developed by incorporating the radiomics score and the selected traditional predictors. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance. Results: The area under the ROC curve (AUC) of the radiomics score, which consisted of 7 radiomics features, was 0.855 in the training set and 0.885 in the validation set. The AUC of the traditional predictor, which consisted of 2 traditional features, was 0.843 in the training set and 0.858 in the validation set. The radiomics signature showed the best performance with an AUC of 0.942 in the training set, which was then confirmed with an AUC of 0.969 in the validation set. Conclusion: The CT-based radiomics signature that incorporated radiomics and traditional features has the potential to be used as a non-invasive tool for preoperative prediction of CN in ccRCC.

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CT-based radiomic model predicts high grade of clear cell renal cell carcinoma

Cited by 126 publications

References 33 publications

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

<p>New Preoperative Nomogram Using the Centrality Index to Predict High Nuclear Grade Clear Cell Renal Carcinoma</p>

CT-Based Radiomics Signature for Preoperative Prediction of Coagulative Necrosis in Clear Cell Renal Cell Carcinoma

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