Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma

Lai, Shih‐Wei; Sun, Lei; Wu, Jialiang; Wei, Rui-Li; Luo, Sanzhong; Ding, Wenshuang; Liu, Xiaoyang; Yang, Ruimeng; Zhen, Xin

doi:10.2147/cmar.s290327

Cited by 13 publications

(14 citation statements)

References 32 publications

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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%

“…All patients were injected with nonionic intravenous contrast agent, via the antecubital vein with mechanical power injector, according to their weight (1 mL/kg body weight, with a maximum of 150 mL). Phase and delay time were as follows: Phase 1, unenhanced; Phase 2, postcontrast corticomedullary phase (CMP): 25–28 s after contrast agent was administrated; Phase 3, postcontrast nephrographic phase (NP): 65–70 s after contrast agent was administrated; and Phase 4, postcontrast excretory phase [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

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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%

Section: Methodsmentioning

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%

“…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%

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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“…Preoperative noninvasive prediction of ccRCC is conducive to delivering an individualized treatment. Previous studies ( 12 , 13 ), using radiation characteristics based on multiphase CT, investigated the predictive performance of different machine learning models for discriminating ccRCC. Beyond that ( 14 – 17 ), have shown that convolutional neural networks based on single or multiphase CT images are beneficial for evaluating ccRCC grading.…”

Section: Introductionmentioning

confidence: 99%

CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma

Yang

et al. 2022

Front. Oncol.

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BackgroundClear cell Renal Cell Carcinoma (ccRCC) is the most common malignant tumor in the urinary system and the predominant subtype of malignant renal tumors with high mortality. Biopsy is the main examination to determine ccRCC grade, but it can lead to unavoidable complications and sampling bias. Therefore, non-invasive technology (e.g., CT examination) for ccRCC grading is attracting more and more attention. However, noise labels on CT images containing multiple grades but only one label make prediction difficult. However, noise labels exist in CT images, which contain multiple grades but only one label, making prediction difficult.AimWe proposed a Transformer-based deep learning algorithm with CT images to improve the diagnostic accuracy of grading prediction and to improve the diagnostic accuracy of ccRCC grading.MethodsWe integrate different training models to improve robustness and predict Fuhrman nuclear grade. Then, we conducted experiments on a collected ccRCC dataset containing 759 patients and used average classification accuracy, sensitivity, specificity, and AreaUnderCurve as indicators to evaluate the quality of research. In the comparative experiments, we further performed various current deep learning algorithms to show the advantages of the proposed method. We collected patients with pathologically proven ccRCC diagnosed from April 2010 to December 2018 as the training and internal test dataset, containing 759 patients. We propose a transformer-based network architecture that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to extract a persuasive feature automatically. And then, a nonlinear classifier is applied to classify. We integrate different training models to improve the accuracy and robustness of the model. The average classification accuracy, sensitivity, specificity, and area under curve are used as indicators to evaluate the quality of a model.ResultsThe mean accuracy, sensitivity, specificity, and Area Under Curve achieved by CNN were 82.3%, 89.4%, 83.2%, and 85.7%, respectively. In contrast, the proposed Transformer-based model obtains a mean accuracy of 87.1% with a sensitivity of 91.3%, a specificity of 85.3%, and an Area Under Curve (AUC) of 90.3%. The integrated model acquires a better performance (86.5% ACC and an AUC of 91.2%).ConclusionA transformer-based network performs better than traditional deep learning algorithms in terms of the accuracy of ccRCC prediction. Meanwhile, the transformer has a certain advantage in dealing with noise labels existing in CT images of ccRCC. This method is promising to be applied to other medical tasks (e.g., the grade of neurogliomas and meningiomas).

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Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma

Cited by 13 publications

References 32 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

CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma

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