Small (<4 cm) clear cell renal cell carcinoma: correlation between CT findings and histologic grade

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma

BACKGROUND

Small (< 4 cm) clear cell renal cell carcinoma (ccRCC) is the most common type of small renal cancer and its prognosis is poor. However, conventional radiological characteristics obtained by computed tomography (CT) are not sufficient to predict the nuclear grade of small ccRCC before surgery.

METHODS

A total of 113 patients with histologically confirmed ccRCC were randomly assigned to the training set (n = 67) and the testing set (n = 46). The baseline and CT imaging data of the patients were evaluated statistically to develop a clinical model. A radiomics model was created, and the radiomics score (Rad-score) was calculated by extracting radiomics features from the CT images. Then, a clinical radiomics nomogram was developed using multivariate logistic regression analysis by combining the Rad-score and critical clinical characteristics. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of small ccRCC in both the training and testing sets.

RESULTS

The radiomics model was constructed using six features obtained from the CT images. The shape and relative enhancement value of the nephrographic phase (REV of the NP) were found to be independent risk factors in the clinical model. The area under the curve (AUC) values for the training and testing sets for the clinical radiomics nomogram were 0.940 and 0.902, respectively. Decision curve analysis (DCA) revealed that the radiomics nomogram model was a better predictor, with the highest degree of coincidence.

CONCLUSION

The CT-based radiomics nomogram has the potential to be a noninvasive and preoperative method for predicting the WHO/ISUP grade of small ccRCC.

CT-based peritumoral radiomics signatures for malignancy grading of clear cell renal cell carcinoma

OBJECTIVE

To evaluate the efficiency of CT-based peritumoral radiomics signatures of clear cell renal cell carcinoma (ccRCC) for malignancy grading in preoperative prediction.

MATERIALS AND METHODS

203 patients with pathologically confirmed as ccRCC were retrospectively enrolled in this study. All patients were categorized into training set (n = 122) and validation set (n = 81). For each patient, two types of volumes of interest (VOI) were masked on CT images. One type of VOIs was defined as the tumor mass volume (TMV), which was masked by radiologists delineating the outline of all contiguous slices of the entire tumor, while the other type defined as the peritumoral tumor volume (PTV), which was automatically created by an image morphological method. 1760 radiomics features were calculated from each VOI, and then the discriminative radiomics features were selected by Pearson correlation analysis for reproducibility and redundancy. These selected features were investigated their validity for building radiomics signatures by mRMR feature ranking method. Finally, the top ranked features, which were used as radiomics signatures, were input into a classifier for malignancy grading. The prediction performance was evaluated by receiver operating characteristic (ROC) curve in an independent validation cohort.

RESULTS

The radiomics signatures of PTV showed a better performance on malignancy grade prediction of ccRCC with AUC of 0.807 (95% CI 0.800-0.834) in train data and 0.848 (95% CI 0.760-0.936) in validation data, while the radiomics signatures of TMV with AUC of 0.773 (95% CI 0.744-0.802) in train data and 0.810 (95% CI 0.706-0.914) in validation data.

CONCLUSION

The CT-based peritumoral radiomics signature is a potential way to be used as a noninvasive tool to preoperatively predict the malignancy grades of ccRCC.

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

OBJECTIVE

To investigate the predictive performance of different machine learning models for the discrimination of low and high nuclear grade clear cell renal cell carcinoma (ccRCC) by using multiphase computed tomography (CT)-based radiomic features.

MATERIALS AND METHODS

A total of 137 consecutive patients with pathologically proven ccRCC (including 96 low-grade [grade 1 or 2] and 41 high-grade [grade 3 or 4] ccRCC) from January 2011 to January 2019 were enrolled in this retrospective study. Target region of interest (ROI) delineation followed by texture extraction was performed on a representative slice with the largest section of the tumor on the four-phase (unenhanced phase [UP], corticomedullary phase [CMP], nephrographic phase [NP] and excretory phase [EP]) CT images. Fifteen concatenations of the four-phase features were fed into 176 classification models (built with 8 classifiers and 22 feature selection methods), the classification performances of the 2640 resultant discriminative models were compared, and the top-ranked features were analyzed.

RESULTS

Image features extracted from the unenhanced phase (UP) CT images demonstrated a dominant classification performance over features from the other three phases. The discriminative model "Bagging + CMIM" achieved the highest classification AUC of 0.75. The top-ranked features from the UP included one shape-based feature and five first-order statistical features.

CONCLUSION

Image features extracted from the UP are more effective than other CT phases in differentiating low and high nuclear grade ccRCC based on machine learning-based classification modeling.

Computed tomography features predicting aggressiveness of malignant parenchymal renal tumors suitable for partial nephrectomy

The aim of this study was to identify and standardize computed tomography (CT) features having a potential role in predicting aggressiveness of malignant parenchymal renal tumors suitable for partial nephrectomy (PN). We performed a non-systematic review of the recent literature to evaluate the potential impact of CT variables proposed by the Society of Abdominal Radiology Disease-Focused Panel on Renal Cell Carcinoma in predicting aggressiveness of newly diagnosed malignant parenchymal renal tumors. The analyzed variables were clinical tumor size, tumor growth rate, enhancement characteristics, amount of cystic component, polar and capsular location, tumor margins and distance between tumor and renal sinus. Unfavorable behavior was defined as: 1) renal cell carcinoma (RCC) with stage ≥pT3; 2) nuclear grade 3 or 4; 3) presence of sarcomatoid de-differentiation; or 4) non-clear cell subtypes with unfavorable prognosis (type 2 papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC). Beyond clinical tumor size, tumor growth rate, enhancement characteristics, amount of cystic component, tumor margins and distance between tumor and renal sinus are highly relevant features predicting an unfavorable behavior. Moreover, several studies supported the role of necrosis as preoperative predictor of tumor aggressiveness. Peritumoral and intratumoral vasculature as well as capsule status are emerging variables that need to be further evaluated. Tumor size, enhancement characteristics, tumor margins and distance to the renal sinus are highly relevant CT features predicting biological aggressiveness of malignant parenchymal renal tumors. Combination of these parameters might be useful to generate tools to predict the unfavorable behavior of renal tumors suitable for PN.

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

OBJECTIVE

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.

Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT

OBJECTIVE

Comparing the prediction models for the ISUP/WHO grade of clear cell renal cell carcinoma (ccRCC) based on CT radiomics and conventional contrast-enhanced CT (CECT).

METHODS

The corticomedullary phase images of 119 cases of low-grade (I and II) and high-grade (III and IV) ccRCC based on 2016 ISUP/WHO pathological grading criteria were analyzed retrospectively. The patients were randomly divided into training and validation set by stratified sampling according to 7:3 ratio. Prediction models of ccRCC differentiation were constructed using CT radiomics and conventional CECT findings in the training setandwere validated using validation set. The discrimination, calibration, net reclassification index (NRI) and integrated discrimination improvement index (IDI) of the two prediction models were further compared. The decision curve was used to analyze the net benefit of patients under different probability thresholds of the two models.

RESULTS

In the training set, the C-statistics of radiomics prediction model was statistically higher than that of CECT (p < 0.05), with NRI of 9.52% and IDI of 21.6%, both with statistical significance (p < 0.01).In the validation set, the C-statistics of radiomics prediction model was also higher but did not show statistical significance (p = 0.07). The NRI and IDI was 14.29 and 33.7%, respectively, both statistically significant (p < 0.01). Validation set decision curve analysis showed the net benefit improvement of CT radiomics prediction model in the range of 3-81% over CECT.

CONCLUSION

The prediction model using CT radiomics in corticomedullary phase is more effective for ccRCC ISUP/WHO grade than conventional CECT.

ADVANCES IN KNOWLEDGE

As a non-invasive analysis method, radiomics can predict the ISUP/WHO grade of ccRCC more effectively than traditional enhanced CT.

Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study

PURPOSE

To predict the histologic grade of small clear cell renal cell carcinomas (ccRCCs) using texture analysis and machine learning algorithms.

METHODS

Fifty-two noncontrast (NC), 26 corticomedullary (CM) phase, and 35 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected ccRCCs were retrospectively identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (gray-level co-occurrences (GLC) and gray-level run-lengths (GLRL)) features were calculated for each tumor in each phase. T testing compared feature values in low- and high-grade ccRCCs, with a (Benjamini-Hochberg) false discovery rate of 10%. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of low- and high-grade ccRCCs in each phase. Histogram, texture, and combined histogram and texture data sets were used to train and test four algorithms (k-nearest neighbor (KNN), support vector machine (SVM), random forests, and decision tree) with tenfold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of low- and high-grade ccRCCs.

RESULTS

Zero, 23, and 0 features in the NC, CM, and NG phases had statistically significant differences between low and high-grade ccRCCs. CM histogram skewness and GLRL short run emphasis had the highest AUCs (0.82) in predicting histologic grade. All four algorithms had the highest AUCs (0.97) predicting histologic grade using CM histogram features. The algorithms' AUCs decreased using histogram or texture features from NC or NG phases.

CONCLUSION

The histologic grade of small ccRCCs can be accurately predicted with machine learning algorithms using CM histogram features, which outperform NC and NG phase image data.

Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade

PURPOSE

To evaluate the performance of machine learning (ML)-based computed tomography (CT) radiomics analysis for discriminating between low grade (WHO/ISUP I-II) and high grade (WHO/ISUP III-IV) clear cell renal cell carcinomas (ccRCCs).

METHODS

A total of 164 low grade and 107 high grade ccRCCs were retrospectively analyzed in this study. Radiomic features were extracted from corticomedullary phase (CMP) and nephrographic phase (NP) CT images. Intraclass correlation coefficient (ICC) was calculated to quantify the feature's reproducibility. The training and validation cohort consisted of 163 and 108 cases. Least absolute shrinkage and selection operator (LASSO) regression method was used for feature selection. The machine learning (ML) classifiers were k-NearestNeighbor (KNN), Logistic Regression (LR), multilayer perceptron (MLP), Random Forest (RF), and support vector machine (SVM). The performance of classifiers was mainly evaluated and compared by certain metrics.

RESULTS

Seven CMP features (ICC range, 0.990-0.999) and seven NP features (ICC range, 0.931-0.999) were selected. The accuracy of CMP, NP and the combination of CMP and NP ranged from 82.2%-85.9 %, 82.8%-94.5 % and 86.5%-90.8 % in the training cohort, and 90.7%-95.4%, 77.8%-79.6 % and 91.7%-93.5 % in the validation cohort. The AUC of CMP, NP and the combination of CMP and NP ranged from 0.901 to 0.938, 0.912 to 0.976, 0.948 to 0.968 in the training cohort, and 0.957 to 0.974, 0.856 to 0.875, 0.960 to 0.978 in the validation cohort.

CONCLUSIONS

ML-based CT radiomics analysis can be used to predict the WHO/ISUP grade of ccRCCs preoperatively.

FORCE dual-energy CT in pathological grading of clear cell renal cell carcinoma

The aim of the present study was to examine the value of FORCE dual-energy CT in grading the clear cell renal cell carcinoma (ccRCC). A total of 35 cases of ccRCC were included. Hematoxylin and eosin staining was performed, and the cases were divided into low- (Fuhrman I-II) and high-grade (Fuhrman III-IV) groups. FORCE dual-energy CT parameters, including virtual network computing CT value (VNCV), iodine overlay value (IOV), mixed energy CT value (MEV), iodine concentration (IC), normalized iodine concentration (NIC), NIC based on aorta (NICA), NIC based on cortex (NICC) and NIC based on medulla (NICM), were analyzed and compared. Receiver operating characteristic analysis was also performed. There were significant differences in the arterial phase IOV, MEV and IC, and the venous phase IOV and IC between the low- and high-grade groups. No significant differences were observed in VNCV and MEV between the low -and high-grade groups in the venous phase. Significant differences were observed in the NICA and NICC between these two groups, however no difference was observed in NICM. There were significant differences in the tumor CT values for the arterial phase at the 40, 60, 80 and 100 kiloelectron volt (keV) between the low- and high-grade groups, while no significant differences were observed at the 120-140 keV levels. The k-slope for the low-grade group was significantly higher than the high-grade group. In addition, the area under curve for the arterial phase IOV, arterial phase MEV, arterial phase IC, aortic NIC, cortical NIC, venous phase IOV, venous phase IC and curve slope K of mono-energy CT value suggested high value in diagnosis of low- and high-grade ccRCC cases.

CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma

PURPOSE

To predict the Fuhrman grade of clear cell renal cell carcinoma (ccRCC) with a machine learning classifier based on single- or three-phase computed tomography (CT) images.

MATERIALS AND METHODS

Patients with pathologically proven ccRCC from February 1, 2009 to September 31, 2018 who were not treated were retrospectively collected for machine learning-based analysis. The texture features were extracted and ranked from precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP) and three-phase CT images, and open-source gradient boosting from the decision tree library of CatBoost was used to establish a machine learning classifier to differentiate low- from high-grade ccRCC. The performances of machine learning classifiers based on features from single- and three-phase CT images were compared with each other.

RESULTS

A total of 231 patients with 232 pathologically proven ccRCC lesions were retrospectively collected. 35, 36, 41, and 22 Features were extracted and ranked from PCP, CMP, NP, and three-phase CT images, respectively. The machine learning model based on three-phase CT images [area under the ROC curve (AUC) = 0.87] achieved the best diagnostic performance for differentiating low- from high-grade ccRCC, followed by single-phase NP (AUC = 0.84), CMP (AUC = 0.80), and PCP images (AUC = 0.82).

CONCLUSION

Machine learning classifiers can be promising noninvasive techniques to differentiate low- and high-Fuhrman nuclear grade ccRCC, and classifiers based on three-phase CT images are superior to those based on features from each single phase.

Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade

OBJECTIVES

To discriminate low grade (Fuhrman I/II) and high grade (Fuhrman III/IV) clear cell renal cell carcinoma (CCRCC) by using CT-based radiomic features.

METHODS

161 and 99 patients diagnosed with low and high grade CCRCCs from January 2011 to May 2018 were enrolled in this study. 1029 radiomic features were extracted from corticomedullary (CMP), and nephrographic phase (NP) CT images of all patients. We used interclass correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO) regression method to select features, then the selected features were constructed three classification models (CMP, NP and with their combination) to discriminate high and low grades CCRCC. These three models were built by logistic regression method using 5-fold cross validation strategy, evaluated with receiver operating characteristics curve (ROC) and compared using DeLong test.

RESULTS

We found 11 and 24 CMP and NP features were independently significantly associated with the Fuhrman grades. The model of CMP, NP and Combined model using radiomic feature set showed diagnostic accuracy of 0.719 (AUC [area under the curve], 0.766; 95% CI [confidence interval]: 0.709-0.816; sensitivity, 0.602; specificity, 0.838), 0.738 (AUC, 0.818; 95% CI:0.765-0.838; sensitivity, 0.693; specificity, 0.838), 0.777(AUC, 0.822; 95% CI: 0.769-0.866; sensitivity, 0.677; specificity, 0.839). There were significant differences in AUC between CMP model and Combined model (P = 0.0208), meanwhile, the differences between CMP model and NP model, NP model and Combined model reached no significant (P = 0.0844, 0.7915).

CONCLUSIONS

Radiomic features could be used as biomarker for the preoperative evaluation of the CCRCC Fuhrman grades.

Delayed enhancement of the peritumoural cortex in clear cell renal cell carcinoma: correlation with Fuhrman grade

AIM

To assess the delayed enhancement of the peritumoural cortex (DEC) sign in clear cell renal cell carcinoma (ccRCC), and investigate a possible correlation among DEC and Fuhrman grade.

MATERIALS AND METHODS

This retrospective study included 506 patients with 511 histopathologically proven ccRCCs evaluated by computed tomography (CT) angiography. DEC was detected and compared in groups divided by Fuhrman grades (low grade: 1 and 2, high grade: 3 and 4) using univariate and multivariate analyses.

RESULTS

DEC was detected in 89 of 511 (17.4%) ccRCCs (grade 1: 5.7%, 2/35; grade 2: 16.2%, 70/433; grade 3: 31.4%, 11/35; grade 4: 75%, 6/8; p<0.001). The incidence was higher in high-grade ccRCCs (39.5%, 17/43) than in low-grade ccRCCs (15.4%, 72/468; p<0.001). In multivariate analysis, tumour size >5.4 cm (p<0.001, odds ratio [OR]=3.57, 95% confidence interval [CI]: 1.76-7.23) and detection of DEC (p=0.021, OR=2.33, 95% CI: 1.13-4.80) were independent predictors of high-grade ccRCC. For all ccRCCs, the area under the receiver operating characteristic (ROC) curve (AUC) of DEC in predicting high-grade ccRCC was 0.62 (95% CI: 0.53-0.72) with 39.5% sensitivity and 84.6% specificity, while for ccRCCs of >5.4 cm diameter, the AUC was 0.66 (95% CI: 0.52-0.80) with 68.4% sensitivity and 62.7% specificity.

CONCLUSIONS

The DEC sign may predict aggressive biological behaviour of ccRCC, irrespective of tumour size.

Characterization of small (<4cm) solid renal masses by computed tomography and magnetic resonance imaging: Current evidence and further development

Diagnosis of renal cell carcinomas (RCC) subtypes on computed tomography (CT) and magnetic resonance imaging (MRI) is clinically important. There is increased evidence that confident imaging diagnosis is now possible while standardization of the protocols is still required. Fat-poor angiomyolipoma show homogeneously increased unenhanced attenuation, homogeneously low signal on T2-weighted MRI and apparent diffusion coefficient (ADC) map, may contain microscopic fat and are classically avidly enhancing. Papillary RCC are also typically hyperattenuating and of low signal on T2-weighted MRI and ADC map; however, their gradual progressive enhancement after intravenous administration of contrast material is a differentiating feature. Clear cell RCC are avidly enhancing and may show intracellular lipid; however, these tumors are heterogeneous and are of characteristically increased signal on T2-weighted MRI. Oncocytomas and chromophobe tumors (collectively oncocytic neoplasms) show intermediate imaging findings on CT and MRI and are the most difficult subtype to characterize accurately; however, both show intermediately increased signal on T2-weighted with more gradual enhancement compared to clear cell RCC. Chromophobe tumors tend to be more homogeneous compared to oncocytomas, which can be heterogeneous, but other described features (e.g. scar, segmental enhancement inversion) overlap considerably between tumors. Tumor grade is another important consideration in small solid renal masses with emerging studies on both CT and MRI suggesting that high grade tumors may be separated from lower grade disease based upon imaging features.

Diagnostic Accuracy of Unenhanced CT Analysis to Differentiate Low-Grade From High-Grade Chromophobe Renal Cell Carcinoma

OBJECTIVE

The objective of our study was to evaluate tumor attenuation and texture on unenhanced CT for potential differentiation of low-grade from high-grade chromophobe renal cell carcinoma (RCC).

MATERIALS AND METHODS

A retrospective study of 37 consecutive patients with chromophobe RCC (high-grade, n = 13; low-grade, n = 24) who underwent preoperative unenhanced CT between 2011 and 2016 was performed. Two radiologists (readers 1 and 2) blinded to the histologic grade of the tumor and outcome of the patients subjectively evaluated tumor homogeneity (3-point scale: completely homogeneous, mildly heterogeneous, or mostly heterogeneous). A third radiologist, also blinded to tumor grade and patient outcome, measured attenuation and contoured tumors for quantitative texture analysis. Comparisons were performed between high-grade and low-grade tumors using the chi-square test for subjective variables and sex, independent t tests for patient age and tumor attenuation, and Mann-Whitney U tests for texture analysis. Logistic regression models and ROC curves were computed.

RESULTS

There were no differences in age or sex between the groups (p = 0.652 and 0.076). High-grade tumors were larger (mean ± SD, 62.6 ± 34.9 mm [range, 17.0-141.0 mm] vs 39.0 ± 17.9 mm [16.0-72.3 mm]; p = 0.009) and had higher attenuation (mean ± SD, 45.5 ± 8.2 HU [range, 29.0-55.0 HU] vs 35.3 ± 8.5 HU [14.0-51.0 HU]; p = 0.001) than low-grade tumors. CT size and attenuation achieved good accuracy to diagnose high-grade chromophobe RCC: The AUC ± standard error was 0.85 ± 0.08 (p < 0.0001) with a sensitivity of 69.0% and a specificity of 100%. Subjectively, high-grade tumors were more heterogeneous (mildly or markedly heterogeneous: 69.2% [9/13] for reader 1 and 76.9% [10/13] for reader 2; reader 1, p = 0.024; reader 2, p = 0.001) with moderate agreement (κ = 0.57). Combined texture features diagnosed high-grade tumors with a maximal AUC of 0.84 ± 0.06 (p < 0.0001).

CONCLUSION

Tumor attenuation and heterogeneity assessed on unenhanced CT are associated with high-grade chromophobe RCC and correlate well with the histopathologic chromophobe tumor grading system.