Dataset Information

Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma.

ABSTRACT:

Objectives

This study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).

Methods

The study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan-Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS.

Results

The PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05).

Conclusion

The CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC.

Critical relevance statement

The intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies.

Key points

The multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis. The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading. The nomogram and radiomics models have a broad range of clinical applications.

SUBMITTER: Li X

PROVIDER: S-EPMC11239644 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Publications

Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma.

Li Xiaoxia X Lin Jinglai J Qi Hongliang H Dai Chenchen C Guo Yi Y Lin Dengqiang D Zhou Jianjun J

Insights into imaging 20240712 1

<h4>Objectives</h4>This study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).<h4>Methods</h4>The study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation c ...[more]

PMID: 38992169

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Project description:Purpose: Exploring the value of predicting the WHO/ISUP grade of clear cell renal cell carcinoma (ccRCC) using computed tomography urography (CTU) images, providing valuable recommendations for the treatment of ccRCC. Method: CTU images from the Renmin Hospital of Wuhan University (RHWU) cohort, including 328 patients with ccRCC, were retrospectively collected. The corticomedullary (CMP) phase features of ccRCC were extracted from the CTU images using the Pyradiomics package, and key features were selected through the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The 328 patients were split into training and testing sets in a 7:3 ratio. 175 patients from the The Cancer Genome Atlas (TCGA) cohort were used for the external validation set. Various models, including Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were employed to predict the ISUP grade. SHAP analysis was then used to visualize the performance of the best model. Results: A total of 1,218 features were extracted using the Pyradiomics package, with 20 features selected for model training through LASSO analysis. In the training set, the AUC for the LR model was 0.88 (95% confidence interval [CI] 0.84-0.91), for MLP it was 0.89 (95% CI 0.86-0.93), for SVM it was 0.86 (95% CI 0.83-0.90), and for XGBoost it was 0.96 (95% CI 0.92-0.99). In the testing set, the AUC for LR was 0.79 (95% CI 0.73-0.85), for MLP it was 0.78 (95% CI 0.72-0.83), for SVM it was 0.78 (95% CI 0.73-0.82), and for XGBoost it was 0.80 (95% CI 0.75-0.85). In the validation set, the AUC for LR was 0.74 (95% CI 0.68-0.79), for MLP it was 0.68 (95% CI 0.63-0.73), for SVM it was 0.67 (95% CI 0.64-0.71), and for XGBoost it was 0.78 (95% CI 0.74-0.83). XGBoost demonstrated superior performance, with a sensitivity of 0.99 (95% CI 0.96-1.00) in the training set, 0.92 (95% CI 0.88-0.97) in the testing set and 0.91 (95% CI 0.86,0.95) in validation set. SHAP analysis revealed that the wavelet-LHL_glcm_Idn and wavelet-LHL_glrlm_LongRunEmphasis features played pivotal roles in the classification task. Conclusion: In this study, we employ an artificial intelligence model to conduct non-invasive ISUP grade prediction on preoperative CTU images of ccRCC, thereby aiding clinical decision-making. Additionally, we uncover that the radiomics features extracted from the CMP phase of CTU images hold promise as potential biomarkers for grading ccRCC.

Project description:ObjectivesThis study aims to establish predictive logistic models for the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grades of clear cell renal cell carcinoma (ccRCC) based on tumoral and peritumoral radiomics.MethodsA cohort of 370 patients with pathologically confirmed ccRCCs were included in this retrospective study between January 2014 and December 2020 according to the WHO/ISUP grading system. The volume of interests of triphasic computed tomography images were depicted manually using the "itk-SNAP" software, and the radiomics features were calculated. The cohort was segmented into the training cohort and validation cohort with a random proportion of 7:3. After extraction of radiomics features by analysis of variance (ANOVA) or Mann-Whitney U test, correlation analysis, and the least absolute shrinkage and selection operator (LASSO) method, the logistic models of tumoral radiomics (LR-tumor) and peritumoral radiomics (LR-peritumor) were developed. The LR-peritumor was subdivided into LR-peritumor-2mm, LR-peritumor-5mm, and LR-peritumor-10mm, and the LR-peritumor-2mm was subdivided into LR-peritumor-kid and LR-peritumor-fat based on the neighboring tissues of ccRCCs. Finally, an integrative model of tumoral and peritumoral radiomics (LR-tumor/peritumor) was built. The value of areas under the receiver operator characteristics curve (AUCs) was calculated to assess the efficacy of the models.ResultsThere were 209 low-grade and 161 high-grade ccRCCs enrolled. The AUCs of LR-tumor in CT images of venous phase were 0.802 in the training cohort and 0.796 in the validation cohort. The AUCs were higher in the LR-peritumor-2mm than those in LR-peritumor-5mm and LR-peritumor-10mm (training cohort: 0.788 vs. 0.788 and 0.759; validation cohort: 0.787 vs. 0.785 and 0.758). Moreover, the AUCs of LR-peritumor-fat were higher compared with those of LR-peritumor-kid. The LR-tumor/peritumor displayed the highest AUCs of 0.812 in the training cohort and 0.804 in the validation cohort.ConclusionsThe tumoral and peritumoral radiomics helped to predict the WHO/ISUP grades of ccRCCs. On the diagnostic performance of peritumoral radiomics, better results were seen for the LR-peritumor-2mm and LR-peritumor-fat.

Project description:ObjectivesTo develop radiomics models based on multi-sequence MRI from two centers for the preoperative prediction of the WHO/ISUP grade of Clear Cell Renal Cell Carcinoma (ccRCC).MethodsThis retrospective study included 334 ccRCC patients from two centers. Significant clinical factors were identified through univariate and multivariate analyses. MRI sequences included Dynamic contrast-enhanced MRI, axial fat-suppressed T2-weighted imaging, diffusion-weighted imaging, and in-phase/out-of-phase images. Feature selection methods and logistic regression (LR) were used to construct clinical and radiomics models, and a combined model was developed using the Rad-score and significant clinical factors. Additionally, seven classifiers were used to construct the combined model and different folds LR was used to construct the combined model to evaluate its performance. Models were evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), and decision curve analysis (DCA). The Delong test compared ROC performance, with p < 0.050 considered significant.ResultsMultivariate analysis identified intra-tumoral vessels as an independent predictor of high-grade ccRCC. In the external validation set, the radiomics model (AUC = 0.834) outperformed the clinical model (AUC = 0.762), with the combined model achieving the highest AUC (0.855) and significantly outperforming the clinical model (p = 0.003). DCA showed that the combined model had a higher net benefit within the 0.04-0.54 risk threshold range than clinical model. Additionally, the combined model constructed using logistic regression has a higher priority compared to other classifiers. Additionally, 10-fold cross-validation with LR for the combined model showed consistent AUC values (0.849-0.856) across different folds.ConclusionThe radiomics models based on multi-sequence MRI might be a noninvasive and effective tool, demonstrating good efficacy in preoperatively predicting the WHO/ISUP grade of ccRCC.

Project description:Objective: The International Society of Urological Pathology (ISUP) has proposed a grading system to classify renal cell carcinoma (RCC). However, classification using biopsy specimens remains problematic and, consequently, the accuracy of a biopsy-based diagnosis is relatively poor. This study aims to combine clinical and immunohistochemical (IHC) factors for the prediction of high ISUP grade clear cell RCC (ccRCC) in an attempt to complement and improve the accuracy of a biopsy-based diagnosis. Methods: A total of 362 ccRCC patients were enrolled in this study and used for the training set. We performed IHC analysis of 18 protein markers on standard tissue sections using an automated stainer. Multivariate logistic regression models were developed to evaluate independent predictors for high ISUP grade. We evaluated different prediction models using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) analysis. A nomogram for the derivation of an integrated score for predicting high ISUP grade ccRCC and a calibration curve were also plotted. Finally, an internal validation cohort was examined to evaluate the performance of our integrated scoring system and nomogram. Results: Multivariate logistic analyses revealed seven credible candidates for predicting high grade ISUP. These were age, tumor diameter, surgery, and CK7, Ki-67, PTEN, and MTOR protein expression. The ROC curves for the clinical, IHC and integrated models were compared in the training set, and the AUC for each was 0.731, 0.744, and 0.801, respectively. DeLong's test showed that the integrated model was significantly better at predicting high ISUP grade, when compared with the other models. Internal validation confirmed the good performance of the integrated score in predicting ISUP grade. Conclusion: We have developed a nomogram integrating clinical and immunohistochemical parameters to predict high ISUP grade for M0 ccRCC patients. This nomogram may offer potentially useful information during preoperative individualized patient risk assessment, and consequently may help urologists when planning personalized management regimens.

Project description: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.

Project description:BackgroundThe aim is to develop and validate radiomics based on 2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) parameters for predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade of clear cell renal cell carcinoma (ccRCC).MethodsA total of 209 patients with 214 lesions, who underwent 2-[18F]FDG PET/CT scans between December 2016 to December 2023, were included in our study. All ccRCC lesions were categorized into low grade (WHO/ISUP grade I-II) and high grade (WHO/ISUP grade III-IV). The lesions were allocated into a training group and a testing group in a ratio of 7:3. The radiomics features were extracted by a serious of maximum standardized uptake value (SUVmax) thresholds (0,2.5%,25%,40%) with the utilization of the minimum redundancy and maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression algorithm. The clinical, radiomics and combined models were constructed. The receiver operating characteristic (ROC) curve, decision curve and calibration curves were plotted to assess the predicting performance.ResultsThe area under curve (AUC) of PET-0, PET-2.5%, PET-25%, PET-40% model in the training group were 0.881(95% CI: 0.822-0.940),0.883(95% CI: 0.825-0.942),0.889(95% CI: 0.831-0.946),0.887(95% CI: 0.826-0.948); and 0.878(95% CI: 0.777-0.978),0.876(95% CI: 0.776-0.977),0.871(95% CI: 0.769-0.972),0.882(95% CI: 0.786-0.979) in the testing group. Due to perfect prediction and verification performance, the volume of interest (VOI) from PET images with SUVmax threshold of 40% were selected to construct the radiomics model and combined model. The AUC of the clinical model and radiomics model was 0.859 (sensitivity = 0.846, specificity = 0.747) and 0.909 (sensitivity = 0.808, specificity = 0.751) in the training group, respectively; 0.882 (sensitivity = 0.857, specificity = 0.857) and 0.901 (sensitivity = 0.905, specificity = 0.833) in the testing group, respectively. In combined models, the AUC was 0.916, the sensitivity was 0.923 and the specificity was 0.808 in the training group; the AUC was 0.916, the sensitivity was 0.881 and the specificity was 0.792 in the training group.ConclusionRadiomics based on 2-[18F]FDG PET/CT can be helpful to predict WHO/ISUP grade of ccRCC.

Dataset Information

Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma.

Objectives

Methods

Results

Conclusion

Critical relevance statement

Key points

Publications

Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma.

Similar Datasets

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