Can diffusion-weighted magnetic resonance imaging of clear cell renal carcinoma predict low from high nuclear grade tumors

Intra- and Peritumoral CT-Based Radiomics for Assessing Pathologic T-Staging in Clear Cell Renal Cell Carcinoma: A Multicenter Study

BACKGROUND

A radiomics model constructed from the intratumoral region of computed tomography (CT) can predict the pathologic T stage of clear cell renal cell carcinoma (ccRCC). However, the predictive capability of the radiomics model that incorporates both intra- and peritumoral regions of CT for the pathologic T stage in ccRCC patients has not been reported to date.

METHODS

This study enrolled 250 patients with ccRCC who underwent laparoscopic surgery. Three radiomics models were developed based on the intra- and peritumoral regions. The sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curves of each model were analyzed. Decision curve analysis (DCA) and calibration curves were used to assess the net benefit and calibration ability of the models. Additionally, the diagnostic performance of the different models were compared with that of radiologists.

RESULTS

The radiomics model based on the intra- and peritumoral regions at 5 mm exhibited the strongest performance, with area under curve values of 0.91 (95 % confidence interval [CI], 0.8551-0.9650), 0.85 (95 % CI, 0.7490-0.9517), and 0.873 (95 % CI, 0.7612-0.9839) in distinguishing high and low T stages of ccRCC across the training, validation, and test sets, respectively. The model's accuracy in the training, validation, and test sets was 0.798, 0.732, and 0.769, with corresponding sensitivity values of 0.921, 0.857, and 0.882, and specificity values of 0.747, 0.690, and 0.729. The calibration curve demonstrated a high level of agreement between the predicted and actual outcomes, whereas the DCA showed that the model provided a meaningful net benefit.

CONCLUSIONS

The radiomics model based on the intra- and peritumoral regions of CT has certain value in distinguishing between high and low T stages of ccRCC.

Development and validation of intravoxel incoherent motion diffusion weighted imaging-based model for preoperative distinguishing nuclear grade and survival of clear cell renal cell carcinoma complicated with venous tumor thrombus

OBJECTIVE

To assess the utility of multiparametric MRI and clinical indicators in distinguishing nuclear grade and survival of clear cell renal cell carcinoma (ccRCC) complicated with venous tumor thrombus (VTT).

MATERIALS AND METHODS

This study included 105 and 27 patients in the training and test sets, respectively. Preoperative MRI, including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), was performed. Renal lesions were evaluated for IVIM-DWI metrics and conventional MRI features. All the patients had postoperative histologically proven ccRCC and VTT. An expert uropathologist reviewed all specimens to confirm the nuclear grade of the World Health Organization/ International Society of Urological Pathology (WHO/ISUP) of the tumor. Univariate and multivariable logistic regression analyses were used to select the preoperative imaging features and clinical indicators. The predictive ability of the logistic regression model was assessed using receiver operating characteristic (ROC) analysis. Survival curves were plotted using the Kaplan-Meier method.

RESULTS

High WHO/ISUP nuclear grade was confirmed in 69 of 105 patients (65.7%) in the training set and 19 of 27 patients (70.4%) in the test set, respectively (P = 0.647). Dp_ROI_Low, tumor size, serum albumin, platelet count, and lymphocyte count were independently related to high WHO/ISUP nuclear grade in the training set. The model identified high WHO/ISUP nuclear grade well, with an AUC of 0.817 (95% confidence interval [CI]: 0.735-0.899), a sensitivity of 70.0%, and a specificity of 77.8% in the training set. In the independent test set, the model demonstrated an AUC of 0.766 (95% CI, 0.567-0.966), a sensitivity of 79.0%, and a specificity of 75.0%. Kaplan-Meier analysis showed that the predicted high WHO/ISUP nuclear grade group had poorer progression-free survival than the low WHO/ISUP nuclear grade group in both the training and test sets (P = 0.001 and P = 0.021).

CONCLUSIONS

IVIM-DWI-derived parameters and clinical indicators can be used to differentiate nuclear grades and predict progression-free survival of ccRCC and VTT.

Grading of clear cell renal cell carcinoma by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging

Objectives

To evaluate the diagnostic accuracy of monoexponential, biexponential and stretched-exponential diffusion-weighted imaging (DWI) models in the grading of clear cell renal cell carcinoma (ccRCC).

Materials and Methods

Fifty-one patients with pathologically proven ccRCC underwent DWI with fifteen b factors (0, 10, 30, 50, 70, 100, 150, 200, 300, 400, 600, 800, 1000, 1500, 2000 sec/mm²) on a 3.0T MR scanner. The isotropic apparent diffusion coefficient (ADC), true diffusion coefficient (ADCslow), pseudodiffusion coefficient (ADCfast), and fraction of perfusion (f) were derived from DWI using a biexponential model. The water diffusion heterogeneity index (α) and distributed diffusion coefficient (DDC) were derived from DWI using a stretched-exponential model. All values were calculated for the solid area of tumors and compared between high-grade and low-grade ccRCC. The Mann-Whitney U test and receiver operating characteristic (ROC) analysis were used for statistical analysis. The DeLong test was performed to compare the ROC curves.

Results

The mean ADC, DDC, ADCslow and α values were significantly lower in high-grade ccRCC than in low-grade ccRCC (P< 0.01). However, the ADCfast and f were not significantly different between the two groups (P > 0.05). According to the ROC analyses, the AUC for α was 0.941, which was significantly greater than those of the other parameters, with a sensitivity of 100% and a specificity of 84.2%. The DeLong test showed that there were significant differences in the ROCs among ADCfast/ADC, ADCfast/α, f/ADCslow, ADCfast/ADCslow, f/α, DDC/α, and f/ADC.

Conclusions

Diffusion-related parameters (ADC, DDC, ADCslow and α) could be used to distinguish between low- and high-grade ccRCC. The α derived from the stretched-exponential model may be the most promising parameter for grading ccRCC.

Grading Clear Cell Renal Cell Carcinoma Grade Using Diffusion Relaxation Correlated MR Spectroscopic Imaging

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC, and accurate grading is crucial for prognosis and treatment selection. Biopsy is the reference standard for grading, but MRI methods can improve and complement the grading procedure.

PURPOSE

Assess the performance of diffusion relaxation correlation spectroscopic imaging (DR-CSI) in grading ccRCC.

STUDY TYPE

Prospective.

SUBJECTS

79 patients (age: 58.1 +/- 11.5 years; 55 male) with ccRCC confirmed by histopathology (grade 1, 7; grade 2, 45; grade 3, 18; grade 4, 9) following surgery.

FIELD STRENGTH/SEQUENCE

3.0 T MRI scanner. DR-CSI with a diffusion-weighted echo-planar imaging sequence and T2-mapping with a multi-echo spin echo sequence.

ASSESSMENT

DR-CSI results were analyzed for the solid tumor regions of interest using spectrum segmentation with five sub-region volume fraction metrics (VA , VB , VC , VD , and VE ). The regulations for spectrum segmentation were determined based on the D-T2 spectra of distinct macro-components. Tumor size, voxel-wise T2, and apparent diffusion coefficient (ADC) values were obtained. Histopathology assessed tumor grade (G1-G4) for each case.

STATISTICAL TESTS

One-way ANOVA or Kruskal-Wallis test, Spearman's correlation (coefficient, rho), multivariable logistic regression analysis, receiver operating characteristic curve analysis, and DeLong's test. Significance criteria: P < 0.05.

RESULTS

Significant differences were found in ADC, T2, DR-CSI VB , and VD among the ccRCC grades. Correlations were found for ccRCC grade to tumor size (rho = 0.419), age (rho = 0.253), VB (rho = 0.553) and VD (rho = -0.378). AUC of VB was slightly larger than ADC in distinguishing low-grade (G1-G2) from high-grade (G3-G4) ccRCC (0.801 vs. 0.762, P = 0.406) and G1 from G2 to G4 (0.796 vs. 0.647, P = 0.175), although not significant. Combining VB , VD , and VE had better diagnostic performance than combining ADC and T2 for differentiating G1 from G2-G4 (AUC: 0.814 vs 0.643).

DATA CONCLUSION

DR-CSI parameters are correlated with ccRCC grades, and may help to differentiate ccRCC grades.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY STAGE: 2.

The Effectiveness of Volumetric MRI Histogram Analysis in Renal Cell Carcinoma

RATIONALE AND OBJECTIVES

This study investigated the utility of histogram parameters derived from diffusion-weighted imaging (DWI) for evaluating renal cell carcinoma (RCC) grading prior to surgery.

MATERIALS AND METHODS

This retrospective study included 88 patients who were histopathologically diagnosed with RCC and underwent magnetic resonance imaging (MRI) examinations. The patients were divided into two groups as well-differentiated (Group 1) and poorly differentiated (Group 2). Demographic data, preoperative MRI findings, MRI apparent diffusion coefficient (ADC) histogram analyzes, operation types, postoperative histopathological data and cancer stages of the patients were recorded. The histogram parameters of ADC values, comprising the mean, minimum, maximum, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles, as well as skewness, kurtosis, and variance, were calculated.

RESULTS

The study included 59 males and 29 women with an average age of 56.21 ± 1.33 years. There were 52 patients in Group 1 and 36 patients in Group 2. The ADCmin, ADCmean, ADCmax, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of ADC values of the poorly differentiated group were all lower than those of the well-differentiated group. ADCmin and the 5th percentile of ADC values, as well as ADCmean and the 10th, 25th, 50th, and 75th percentiles of ADC values, showed a statistically significant difference (p < 0.05). The AUC, sensitivity, and specificity of the ADCmin value were 0.703, 56.3%, and 75.7%, respectively.

CONCLUSION

The present study indicated that histogram parameters generated from DWI were capable of differentiating between high-grade and low-grade RCC.

WHO/ISUP grade and pathological T stage of clear cell renal cell carcinoma: value of ZOOMit diffusion kurtosis imaging and chemical exchange saturation transfer imaging

OBJECTIVES

To evaluate the value of ZOOMit diffusion kurtosis imaging (DKI) and chemical exchange saturation transfer (CEST) imaging in predicting WHO/ISUP grade and pathological T stage in clear cell renal cell carcinoma (ccRCC).

METHODS

Forty-six patients with ccRCC were included in this retrospective study. All participants underwent MRI including ZOOMit DKI and CEST. The non-Gaussian mean kurtosis (MK), mean diffusivity (MD), magnetization transfer ratio asymmetry (MTRasym (3.5 ppm)), and S_sat (3.5 ppm)/S₀ were analyzed based on different WHO/ISUP grades and pT stages. Binary logistic regression was used to identify the best combination of the parameters. Pearson's correlation coefficients were calculated between CEST and diffusion-related parameters.

RESULTS

The ADC, MD, and S_sat (3.5 ppm)/S₀ values were significantly lower for higher WHO/ISUP grade tumors, whereas the MK and MTRasym (3.5 ppm) were higher in higher WHO/ISUP grade and higher pT stage tumors. MTRasym (3.5 ppm) combined with MD (AUC, 0.930; 95% CI, 0.858-1.000) showed the best diagnostic efficacy in evaluating the WHO/ISUP grade. MTRasym (3.5 ppm) and MK were mildly positively correlated (r = 0.324, p = 0.028). S_sat (3.5 ppm)/S₀ was moderately positively correlated with ADC (r = 0.580, p < 0.001), mildly positively correlated with MD (r = 0.412, p = 0.005), and moderately negatively correlated with MK (r = -0.575, p < .001).

CONCLUSION

The microstructural and biochemical assessment of ZOOMit DKI and CEST allowed for the characterization of different WHO/ISUP grades and pT stages in ccRCC. MTRasym (3.5 ppm) combined with MD showed the best diagnostic performance for WHO/ISUP grading.

KEY POINTS

• Both diffusion kurtosis imaging (DKI) and chemical exchange saturation transfer (CEST) can be used to predict the WHO/ISUP grade and pathological T stage. • MTRasym (3.5 ppm) combined with MD showed the highest AUC (0.930; 95% CI, 0.858-1.000) in WHO/ISUP grading. • MTRasym at 3.5 ppm showed a positive correlation with mean kurtosis.

Comparative study of conventional diffusion-weighted imaging and introvoxel incoherent motion in assessment of pathological grade of clear cell renal cell carcinoma

OBJECTIVE

To quantitatively compare the diagnostic values of conventional diffusion-weighted imaging (DWI) and introvoxel incoherent motion (IVIM) analysis of microstructural differences for clear cell renal cell carcinoma (ccRCC).

METHODS

Multiple b value DWIs and IVIMs were performed in patients with 146 ccRCCs, 42 with Grade Ⅰ, 46 with Grade Ⅱ, 28 with Grade Ⅲ and 30 with Grade Ⅳ. These tumours were divided into low (Ⅰ+Ⅱ, n = 88) and high grades (Ⅲ+Ⅳ, n = 58). The diagnostic efficacy of various diffusion parameters for predicting ccRCC grades was compared.

RESULTS

The mean signal-to-noise ratios (SNRs) of IVIM images at b = 0, 800 and 1500 s/mm2 were 31.9, 12.3 and 8.4, respectively. The apparent diffusion coefficient (ADC), D and D* values correlated negatively with ccRCC grading (r = -0.786,-0.913, -0879, p < 0.05). f values correlated positively with ccRCC grading (r = 0.811, p < 0.05). The ADC, D and D* values were higher for Grade Ⅱ ccRCC than that of Grade Ⅲ ccRCC (p < 005), however, f values were higher for Grade Ⅲ ccRCC than that of Grade Ⅱ ccRCC (p < 005). Receiver operating characteristic curve analyses showed that D values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ ccRCC grading. The area under the curve, sensitivity, specificity and accuracy of the D values were 0.963, 0.960; 90.9%, 89.1%; 81.0%,78.6 and 89.0%, 87.8%, respectively. For pairwise comparisons of receiver operating characteristic curves and diagnostic efficacy, ADC was worse than IVIM (all p < 0.05).

CONCLUSION

IVIM parameters have better performance than ADC in differentiating ccRCC grading, given an adequate SNR of IVIM images.

ADVANCES IN KNOWLEDGE

1. D values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ ccRCC grading. 2. IVIM parameters have better performance than ADC in differentiating ccRCC grading, given an adequate SNR of IVIM images. 3. The ADC, D and D* values correlated negatively with ccRCC grading, however, f values correlated positively with ccRCC grading.

Predicting the WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma Through CT-Based Tumoral and Peritumoral Radiomics

Objectives

This 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.

Methods

A 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.

Results

There 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.

Conclusions

The 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.

Grading of clear cell renal cell carcinoma using diffusion MRI with a fractional order calculus model

BACKGROUND

The fractional order calculus (FROC) model has been developed to describe restrained motion of water molecules as well as microstructural heterogeneity, providing a novel tool for non-invasive tumor grading.

PURPOSE

To evaluate the role of the FROC model in characterizing clear cell renal cell carcinoma (ccRCC) grades.

MATERIAL AND METHODS

A total of 59 patients diagnosed with ccRCC were included in this prospective study. The diffusion metrics derived from the mono-exponential model (apparent diffusion coefficient [ADC]), intra-voxel incoherent motion [IVIM] model [D, D*, f], and FROC model [D_froc, β, μ]) were calculated and compared between low- and high-grade ccRCCs. Binary logistic regression analysis was performed to establish the diagnostic models. Receiver operating characteristic (ROC) analysis and DeLong test were performed to evaluate and compare the diagnostic performance of metrics in grading ccRCC.

RESULTS

All the metrics except D* and f exhibited statistical differences between low- and high-grade ccRCCs. ROC analysis showed individual FROC parameters, μ, D_froc, and β, outperformed ADC and IVIM parameters in grading ccRCC. For single parameter, μ demonstrated the highest AUC value, sensitivity, and diagnostic accuracy in discriminating the two ccRCC groups while β exhibited the optimal specificity. Importantly, the combination of D_froc, μ, and β could further improve the diagnostic performance.

CONCLUSION

The FROC parameters were superior to ADC and IVIM parameters in grading ccRCC, indicating the great potential of the FROC model in distinguishing low- and high-grade ccRCCs.

Association between nuclear grade of renal cell carcinoma and the aorta-lesion-attenuation-difference

INTRODUCTION AND BACKGROUND

Several features noted on renal mass biopsy (RMB) can influence treatment selection including tumor histology and nuclear grade. However, there is poor concordance between renal cell carcinoma (RCC) nuclear grade on RMB compared to nephrectomy specimens. Here, we evaluate the association of nuclear grade with aorta-lesion-attenuation-difference (ALAD) values determined on preoperative CT scan.

METHODS AND MATERIALS

A retrospective review of preoperative CT scans and surgical pathology was performed on patients undergoing nephrectomy for solid renal masses. ALAD was calculated by measuring the difference in Hounsfield units (HU) between the aorta and the lesion of interest on the same image slice on preoperative CT scan. The discriminative ability of ALAD to differentiate low-grade (nuclear grade 1 and 2) and high-grade (nuclear grade 3 and 4) tumors was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) using ROC analysis. Sub-group analysis by histologic sub-type was also performed.

RESULTS

A total of 368 preoperative CT scans in patients with RCC on nephrectomy specimen were reviewed. Median patient age was 61 years (IQR 52-68). The majority of patients were male, 66% (243/368). Tumor histology was chromophobe RCC in 7.6%, papillary RCC in 15.5%, and clear cell RCC in 76.9%. The majority, 69.3% (253/365) of tumors, were stage T1a. Nuclear grade was grade 1 in 5.46% (19/348), grade 2 in 64.7% (225/348), grade 3 in 26.2% (91/348), and grade 4 in 3.2% (11/348). Nephrographic ALAD values for grade 1, 2, 3, and 4 were 73.7, 46.5, 36.4, and 43.1, respectively (p = 0.0043). Nephrographic ALAD was able to differentiate low-grade from high-grade RCC with a sensitivity of 32%, specificity of 89%, PPV of 86%, and NPV of 36%. ROC analysis demonstrated the predictive utility of nephrographic ALAD to predict high- versus low-grade RCC with an AUC of 0.60 (95% CI 0.51-0.69).

CONCLUSION

ALAD was significantly associated with nuclear grade in our nephrectomy series. Strong specificity and PPV for the nephrographic phrase demonstrate a potential role for ALAD in the pre-operative setting that may augment RMB findings in assessing nuclear grade of RCC. Although this association was statistically significant, the clinical utility is limited at this time given the results of the statistical analysis (relatively poor ROC analysis). Sub-group analysis by histologic subtype yielded very similar diagnostic performance and limitations of ALAD. Further studies are necessary to evaluate this relationship further.

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

Objective

To 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 Methods

We 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.

Results

The 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.

Conclusions

A machine learning-based grading model was developed that can aid in the clinical diagnosis of clear cell renal cell carcinoma using MRI images.

Can texture analysis based on single unenhanced CT accurately predict the WHO/ISUP grading of localized clear cell renal cell carcinoma?

OBJECTIVE

The purpose was to investigate the value of texture analysis in predicting the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of localized clear cell renal cell carcinoma (ccRCC) based on unenhanced CT (UECT).

MATERIALS AND METHODS

Pathologically confirmed subjects (n = 104) with localized ccRCC who received UECT scanning were collected retrospectively for this study. All cases were classified into low grade (n = 53) and high grade (n = 51) according to the WHO/ISUP grading and were randomly divided into training set and test set as a ratio of 7:3. Using 3D-ROI segmentation on UECT images and extracted ninety-three texture features (first-order, gray-level co-occurrence matrix [GLCM], gray-level run length matrix [GLRLM], gray-level size zone matrix [GLSZM], neighboring gray tone difference matrix [NGTDM] and gray-level dependence matrix [GLDM] features). Univariate analysis and the least absolute shrinkage selection operator (LASSO) regression were used for feature dimension reduction, and logistic regression classifier was used to develop the prediction model. Using receiver operating characteristic (ROC) curve, bar chart and calibration curve to evaluate the performance of the prediction model.

RESULTS

Dimension reduction screened out eight optimal texture features (maximum, median, dependence variance [DV], long run emphasis [LRE], run entropy [RE], gray-level non-uniformity [GLN], gray-level variance [GLV] and large area low gray-level emphasis [LALGLE]), and then the prediction model was developed according to the linear combination of these features. The accuracy, sensitivity, specificity, and AUC of the model in training set were 86.1%, 91.4%, 81.1%, and 0.937, respectively. The accuracy, sensitivity, specificity, and AUC of the model in test set were 81.2%, 81.2%, 81.2%, and 0.844, respectively. The calibration curves showed good calibration both in training set and test set (P > 0.05).

CONCLUSION

This study has demonstrated that the radiomics model based on UECT texture analysis could accurately evaluate the WHO/ISUP grading of localized ccRCC.

MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study

The Fuhrman nuclear grade is a recognized prognostic factor for patients with clear cell renal cell carcinoma (CCRCC) and its pre-treatment evaluation significantly affects decision-making in terms of management. In this study, we aimed to assess the feasibility of a combined approach of radiomics and machine learning based on MR images for a non-invasive prediction of Fuhrman grade, specifically differentiation of high- from low-grade tumor and grade assessment. Images acquired on a 3-Tesla scanner (T2-weighted and post-contrast) from 32 patients (20 with low-grade and 12 with high-grade tumor) were annotated to generate volumes of interest enclosing CCRCC lesions. After image resampling, normalization, and filtering, 2438 features were extracted. A two-step feature reduction process was used to between 1 and 7 features depending on the algorithm employed. A J48 decision tree alone and in combination with ensemble learning methods were used. In the differentiation between high- and low-grade tumors, all the ensemble methods achieved an accuracy greater than 90%. On the other end, the best results in terms of accuracy (84.4%) in the assessment of tumor grade were achieved by the random forest. These evidences support the hypothesis that a combined radiomic and machine learning approach based on MR images could represent a feasible tool for the prediction of Fuhrman grade in patients affected by CCRCC.

Comparison of diffusion-weighted imaging mono-exponential mode with diffusion kurtosis imaging for predicting pathological grades of clear cell renal cell carcinoma

PURPOSE

To evaluate the role of diffusion kurtosis imaging (DKI¹) in the characterization of clear cell renal cell carcinoma (ccRCC²) compared with standard diffusion-weighted imaging (DWI³).

METHODS

89 patients with histologically proven ccRCC were evaluated by DKI and DWI on a 3-T scanner. All ccRCCs were classified as grade 1-4 according to the Fuhrman classification system. The apparent diffusion coefficient (ADC⁴), fractional anisotropy (FA⁵), mean diffusivity (MD⁶), mean kurtosis (MK⁷), axial kurtosis (Ka⁸) and radial kurtosis (Kr⁹) values were recorded. The differences in DWI and DKI parameters were evaluated by independent-sample t test and a receiver operating characteristic (ROC¹⁰) analysis was performed. The DeLong test was performed to compare the ROCs.

RESULTS

Compared to normal renal parenchyma, ADC and MD values of ccRCC decreased and MK, Ka, and Kr values increased (p < 0.05). ADC and MD values of ccRCC decreased with the increase in pathological grade, while MK, Ka, and Kr values were increased (p < 0.05). ADC could discriminate G1 vs G3, G1 vs G4, G2 vs G3, G2 vs G4, and G3 vs G4 (p < 0.05) except for G1 vs G2 (p > 0.05). Ka and Kr could discriminate G1 vs G2, G1 vs G3, G1 vs G4, G2 vs G4, and G3 vs G4 (p < 0.05) except for G2 vs G3 (p > 0.05). MD and MK could discriminate G1 vs G2, G1 vs G3, G1 vs G4, G2 vs G3, G2 vs G4, and G3 vs G4 (p < 0.05). The AUC of MK was the highest. The DeLong test showed that there were significant differences regarding ROCs between ADC/MK, ADC/Ka, ADC/Kr in grading G1/G2, and ADC/MK, MK/Ka in grading G3/G4 (p < 0.05).

CONCLUSION

DKI was superior compared to the mono-exponential mode of DWI in grading ccRCC.

Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma

BACKGROUND

Nuclear grade is of importance for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC).

PURPOSE

To develop and validate an MRI-based radiomic model for preoperative predicting WHO/ISUP nuclear grade in ccRCC.

STUDY TYPE

Retrospective.

POPULATION

In all, 379 patients with histologically confirmed ccRCC. Training cohort (n = 252) and validation cohort (n = 127) were randomly assigned.

FIELD STRENGTH/SEQUENCE

Pretreatment 3.0T renal MRI. Imaging sequences were fat-suppressed T₂ WI, contrast-enhanced T₁ WI, and diffusion weighted imaging.

ASSESSMENT

Three prediction models were developed using selected radiomic features, radiomic and clinicoradiologic characteristics, and a model containing only clinicoradiologic characteristics. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the predictive performance of these models in predicting high-grade ccRCC.

STATISTICAL TESTS

The least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) method were used for the selection of radiomic features and clinicoradiologic characteristics, respectively. Multivariable logistic regression analysis was used to develop the radiomic signature of radiomic features and clinicoradiologic model of clinicoradiologic characteristics.

RESULTS

The radiomic signature showed good performance in discriminating high-grade (grades 3 and 4) from low-grade (grades 1 and 2) ccRCC, with sensitivity, specificity, and AUC of 77.3%, 80.0%, and 0.842, respectively, in the validation cohort. The radiomic model, combining radiomic signature and clinicoradiologic characteristics, displayed good predictive ability for high-grade with sensitivity, specificity, and accuracy of 63.6%, 93.3%, and 88.2%, respectively, in the validation cohort. The radiomic model showed a significantly better performance than the clinicoradiologic model (P < 0.05).

DATA CONCLUSION

Multiparametric MRI-based radiomic model can predict WHO/ISUP grade in patients with ccRCC with satisfying performance, and thus could help the physician to improve treatment decisions.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma

PURPOSE

To investigate the effects of different methodologies on the performance of deep learning (DL) model for differentiating high- from low-grade clear cell renal cell carcinoma (ccRCC).

METHOD

Patients with pathologically proven ccRCC diagnosed between October 2009 and March 2019 were assigned to training or internal test dataset, and external test dataset was acquired from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. The effects of different methodologies on the performance of DL-model, including image cropping (IC), setting the attention level, selecting model complexity (MC), and applying transfer learning (TL), were compared using repeated measures analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis. The performance of DL-model was evaluated through accuracy and ROC analyses with internal and external tests.

RESULTS

In this retrospective study, patients (n = 390) from one hospital were randomly assigned to training (n = 370) or internal test dataset (n = 20), and the other 20 patients from TCGA-KIRC database were assigned to external test dataset. IC, the attention level, MC, and TL had major effects on the performance of the DL-model. The DL-model based on the cropping of an image less than three times the tumor diameter, without attention, a simple model and the application of TL achieved the best performance in internal (ACC = 73.7 ± 11.6%, AUC = 0.82 ± 0.11) and external (ACC = 77.9 ± 6.2%, AUC = 0.81 ± 0.04) tests.

CONCLUSIONS

CT-based DL model can be conveniently applied for grading ccRCC with simple IC in routine clinical practice.

Diagnostic test accuracy of ADC values for identification of clear cell renal cell carcinoma: systematic review and meta-analysis

OBJECTIVES

To perform a systematic review on apparent diffusion coefficient (ADC) values of renal tumor subtypes and meta-analysis on the diagnostic performance of ADC for differentiation of localized clear cell renal cell carcinoma (ccRCC) from other renal tumor types.

METHODS

Medline, Embase, and the Cochrane Library databases were searched for studies published until May 1, 2019, that reported ADC values of renal tumors. Methodological quality was evaluated. For the meta-analysis on diagnostic test accuracy of ADC for differentiation of ccRCC from other renal lesions, we applied a bivariate random-effects model and compared two subgroups of ADC measurement with vs. without cystic and necrotic areas.

RESULTS

We included 48 studies (2588 lesions) in the systematic review and 13 studies (1126 lesions) in the meta-analysis. There was no significant difference in ADC of renal parenchyma using b values of 0-800 vs. 0-1000 (p = 0.08). ADC measured on selected portions (sADC) excluding cystic and necrotic areas differed significantly from whole-lesion ADC (wADC) (p = 0.002). Compared to ccRCC, minimal-fat angiomyolipoma, papillary RCC, and chromophobe RCC showed significantly lower sADC while oncocytoma exhibited higher sADC. Summary estimates of sensitivity and specificity to differentiate ccRCC from other tumors were 80% (95% CI, 0.76-0.88) and 78% (95% CI, 0.64-0.89), respectively, for sADC and 77% (95% CI, 0.59-0.90) and 77% (95% CI, 0.69-0.86) for wADC. sADC offered a higher area under the receiver operating characteristic curve than wADC (0.852 vs. 0.785, p = 0.02).

CONCLUSIONS

ADC values of kidney tumors that exclude cystic or necrotic areas more accurately differentiate ccRCC from other renal tumor types than whole-lesion ADC values.

KEY POINTS

• Selective ADC of renal tumors, excluding cystic and necrotic areas, provides better discriminatory ability than whole-lesion ADC to differentiate clear cell RCC from other renal lesions, with area under the receiver operating characteristic curve (AUC) of 0.852 vs. 0.785, respectively (p = 0.02). • Selective ADC of renal masses provides moderate sensitivity and specificity of 80% and 78%, respectively, for differentiation of clear cell renal cell carcinoma (RCC) from papillary RCC, chromophobe RCC, oncocytoma, and minimal-fat angiomyolipoma. • Selective ADC excluding cystic and necrotic areas are preferable to whole-lesion ADC as an additional tool to multiphasic MRI to differentiate clear cell RCC from other renal lesions whether the highest b value is 800 or 1000.

Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics

OBJECTIVE

To investigate externally validated magnetic resonance (MR)-based and computed tomography (CT)-based machine learning (ML) models for grading clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

Patients with pathologically proven ccRCC in 2009-2018 were retrospectively included for model development and internal validation; patients from another independent institution and The Cancer Imaging Archive dataset were included for external validation. Features were extracted from T1-weighted, T2-weighted, corticomedullary-phase (CMP), and nephrographic-phase (NP) MR as well as precontrast-phase (PCP), CMP, and NP CT. CatBoost was used for ML-model investigation. The reproducibility of texture features was assessed using intraclass correlation coefficient (ICC). Accuracy (ACC) was used for ML-model performance evaluation.

RESULTS

Twenty external and 440 internal cases were included. Among 368 and 276 texture features from MR and CT, 322 and 250 features with good to excellent reproducibility (ICC ≥ 0.75) were included for ML-model development. The best MR- and CT-based ML models satisfactorily distinguished high- from low-grade ccRCCs in internal (MR-ACC = 73% and CT-ACC = 79%) and external (MR-ACC = 74% and CT-ACC = 69%) validation. Compared to single-sequence or single-phase images, the classifiers based on all-sequence MR (71% to 73% in internal and 64% to 74% in external validation) and all-phase CT (77% to 79% in internal and 61% to 69% in external validation) images had significant increases in ACC.

CONCLUSIONS

MR- and CT-based ML models are valuable noninvasive techniques for discriminating high- from low-grade ccRCCs, and multiparameter MR- and multiphase CT-based classifiers are potentially superior to those based on single-sequence or single-phase imaging.

KEY POINTS

• Both the MR- and CT-based machine learning models are reliable predictors for differentiating high- from low-grade ccRCCs. • ML models based on multiparameter MR sequences and multiphase CT images potentially outperform those based on single-sequence or single-phase images in ccRCC grading.

Comparison of Monoexponential, Biexponential, Stretched-Exponential, and Kurtosis Models of Diffusion-Weighted Imaging in Differentiation of Renal Solid Masses

Objective

To compare various models of diffusion-weighted imaging including monoexponential apparent diffusion coefficient (ADC), biexponential (fast diffusion coefficient [D_f], slow diffusion coefficient [D_s], and fraction of fast diffusion), stretched-exponential (distributed diffusion coefficient and anomalous exponent term [α]), and kurtosis (mean diffusivity and mean kurtosis [MK]) models in the differentiation of renal solid masses.

Materials and Methods

A total of 81 patients (56 men and 25 women; mean age, 57 years; age range, 30–69 years) with 18 benign and 63 malignant lesions were imaged using 3T diffusion-weighted MRI. Diffusion model selection was investigated in each lesion using the Akaike information criteria. Mann-Whitney U test and receiver operating characteristic (ROC) analysis were used for statistical evaluations.

Results

Goodness-of-fit analysis showed that the stretched-exponential model had the highest voxel percentages in benign and malignant lesions (90.7% and 51.4%, respectively). ADC, D_s, and MK showed significant differences between benign and malignant lesions (p < 0.05) and between low- and high-grade clear cell renal cell carcinoma (ccRCC) (p < 0.05). α was significantly lower in the benign group than in the malignant group (p < 0.05). All diffusion measures showed significant differences between ccRCC and non-ccRCC (p < 0.05) except D_f and α (p = 0.143 and 0.112, respectively). α showed the highest diagnostic accuracy in differentiating benign and malignant lesions with an area under the ROC curve of 0.923, but none of the parameters from these advanced models revealed significantly better performance over ADC in discriminating subtypes or grades of renal cell carcinoma (RCC) (p > 0.05).

Conclusion

Compared with conventional diffusion parameters, α may provide additional information for differentiating benign and malignant renal masses, while ADC remains the most valuable parameter for differentiation of RCC subtypes and for ccRCC grading.

Can MRI be used to diagnose histologic grade in T1a (< 4 cm) clear cell renal cell carcinomas?

OBJECTIVE

To assess whether MRI can differentiate low-grade from high-grade T1a cc-RCC.

MATERIALS AND METHODS

With IRB approval, 49 consecutive solid < 4 cm cc-RCC (low grade [Grade 1 or 2] N = 38, high grade [Grade 3] N = 11) with pre-operative MRI before nephrectomy were identified between 2013 and 2018. Tumor size, apparent diffusion coefficient (ADC) histogram analysis, enhancement wash-in and wash-out rates, and chemical shift signal intensity index (SI index) were assessed by a blinded radiologist. Subjectively, two blinded Radiologists also assessed for (1) microscopic fat, (2) homogeneity (5-point Likert scale), and (3) ADC signal (relative to renal cortex); discrepancies were resolved by consensus. Outcomes were studied using Chi square, multivariate analysis, logistic regression modeling, and ROC. Inter-observer agreement was assessed using Cohen's kappa.

RESULTS

Tumor size was 24 ± 7 (13-39) mm with no association to grade (p = 0.45). Among quantitative features studied, corticomedullary phase wash-in index (p = 0.015), SI index (p = 0.137), and tenth-centile ADC (p = 0.049) were higher in low-grade tumors. 36.8% (14/38) low-grade tumors versus zero high-grade tumors demonstrated microscopic fat (p = 0.015; Kappa = 0.67). Microscopic fat was specific for low-grade disease (100.0% [71.5-100.0]) with low sensitivity (36.8% [21.8-54.6]). Other subjective features did not differ between groups (p > 0.05). A logistic regression model combining microscopic fat + wash-in index + tenth-centile-ADC yielded area under ROC curve 0.98 (Confidence Intervals 0.94-1.0) with sensitivity/specificity 87.5%/100%.

CONCLUSION

The combination of microscopic fat, higher corticomedullary phase wash-in and higher tenth-centile ADC is highly accurate for diagnosis of low-grade disease among T1a clear cell RCC.

Unenhanced CT Texture Analysis of Clear Cell Renal Cell Carcinomas: A Machine Learning-Based Study for Predicting Histopathologic Nuclear Grade

OBJECTIVE. The purpose of this study is to investigate the predictive performance of machine learning (ML)-based unenhanced CT texture analysis in distinguishing low (grades I and II) and high (grades III and IV) nuclear grade clear cell renal cell carcinomas (RCCs). MATERIALS AND METHODS. For this retrospective study, 81 patients with clear cell RCC (56 high and 25 low nuclear grade) were included from a public database. Using 2D manual segmentation, 744 texture features were extracted from unenhanced CT images. Dimension reduction was done in three consecutive steps: reproducibility analysis by two radiologists, collinearity analysis, and feature selection. Models were created using artificial neural network (ANN) and binary logistic regression, with and without synthetic minority oversampling technique (SMOTE), and were validated using 10-fold cross-validation. The reference standard was histopathologic nuclear grade (low vs high). RESULTS. Dimension reduction steps yielded five texture features for the ANN and six for the logistic regression algorithm. None of clinical variables was selected. ANN alone and ANN with SMOTE correctly classified 81.5% and 70.5%, respectively, of clear cell RCCs, with AUC values of 0.714 and 0.702, respectively. The logistic regression algorithm alone and with SMOTE correctly classified 75.3% and 62.5%, respectively, of the tumors, with AUC values of 0.656 and 0.666, respectively. The ANN performed better than the logistic regression (p < 0.05). No statistically significant difference was present between the model performances created with and without SMOTE (p > 0.05). CONCLUSION. ML-based unenhanced CT texture analysis using ANN can be a promising noninvasive method in predicting the nuclear grade of clear cell RCCs.

Renal cell carcinoma: preoperative evaluate the grade of histological malignancy using volumetric histogram analysis derived from magnetic resonance diffusion kurtosis imaging

Background

To investigate the value of histogram analysis of magnetic resonance (MR) diffusion kurtosis imaging (DKI) in the assessment of renal cell carcinoma (RCC) grading before surgery.

Methods

A total of 73 RCC patients who had undergone preoperative MR imaging and DKI were classified into either a low- grade group or a high-grade group. Parametric DKI maps of each tumor were obtained using in-house software, and histogram metrics between the two groups were analyzed. Receiver operating characteristic (ROC) curve analysis was used for obtaining the optimum diagnostic thresholds, the area under the ROC curve (AUC), sensitivity, specificity and accuracy of the parameters.

Results

Significant differences were observed in 3 metrics of ADC histogram parameters and 8 metrics of DKI histogram parameters (P<0.05). ROC curve analyses showed that K_app mean had the highest diagnostic efficacy in differentiating RCC grades. The AUC, sensitivity, and specificity of the K_app mean were 0.889, 87.9% and 80%, respectively.

Conclusions

DKI histogram parameters can effectively distinguish high- and low- grade RCC. K_app mean is the best parameter to distinguish RCC grades.

Differentiation of Papillary Renal Cell Carcinoma Subtypes on MRI: Qualitative and Texture Analysis

OBJECTIVE

The objective of this study was to determine whether quantitative texture analysis of MR images would improve the ability to distinguish papillary renal cell carcinoma (RCC) subtypes, compared with analysis of qualitative MRI features alone.

MATERIALS AND METHODS

A total of 47 pathologically proven papillary RCC tumors were retrospectively evaluated, with 31 (66%) classified as type 1 tumors and 16 (34%) classified as type 2 tumors. MR images were reviewed by two readers to determine tumor size, signal intensity, heterogeneity, enhancement pattern, margins, perilesional stranding, vein thrombosis, and metastasis. Quantitative texture analysis of gray-scale images was performed. A logistic regression was derived from qualitative and quantitative features. Model performance was compared with and without texture features.

RESULTS

The significant qualitative MR features noted were necrosis, enhancement appearance, perilesional stranding, and metastasis. A multivariable model based on qualitative features did not identify any factor as an independent predictor of a type 2 tumor. The logistic regression model for predicting papillary RCCs on the basis of qualitative and quantitative analysis identified probability of the 2D volumetric interpolated breath-hold examination (VIBE) sequence (AUC value, 0.87; 95% CI, 0.77-0.98) as an independent predictor of a type 2 tumor. No difference in the model AUC value was noted when texture features were included in the analysis; however, the model had increased sensitivity and an improved predictive value without loss of specificity.

CONCLUSION

The addition of texture analysis to analysis of conventional qualitative MRI features increased the probability of predicting a type 2 papillary RCC tumor, which may be clinically important.

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade

OBJECTIVE

To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs).

MATERIALS AND METHODS

This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis. Mean age was 57.5 years. Thirty-four (64.1%) patients were male and 19 were female (35.9%). Mean tumour size based on the maximum diameter was 57.4 mm (range, 16-145 mm). Forty patients underwent radical nephrectomy and 13 underwent partial nephrectomy. Following pre-processing steps, two-dimensional CT texture features were extracted using portal-phase contrast-enhanced CT. Reproducibility of texture features was assessed with the intra-class correlation coefficient (ICC). Nested cross-validation with a wrapper-based algorithm was used in feature selection and model optimisation. The ML classifiers were support vector machine (SVM), multilayer perceptron (MLP, a sort of neural network), naïve Bayes, k-nearest neighbours, and random forest. The performance of the classifiers was compared by certain metrics.

RESULTS

Among 279 texture features, 241 features with an ICC equal to or higher than 0.80 (excellent reproducibility) were included in the further feature selection process. The best model was created using SVM. The selected subset of features for SVM included five co-occurrence matrix (ICC range, 0.885-0.998), three run-length matrix (ICC range, 0.889-0.992), one gradient (ICC = 0.998), and four Haar wavelet features (ICC range, 0.941-0.997). The overall accuracy, sensitivity (for detecting high-grade cc-RCCs), specificity (for detecting high-grade cc-RCCs), and overall area under the curve of the best model were 85.1%, 91.3%, 80.6%, and 0.860, respectively.

CONCLUSIONS

The ML-based CT texture analysis can be a useful and promising non-invasive method for prediction of low and high Fuhrman nuclear grade cc-RCCs.

KEY POINTS

• Based on the percutaneous biopsy literature, ML-based CT texture analysis has a comparable predictive performance with percutaneous biopsy. • Highest predictive performance was obtained with use of the SVM. • SVM correctly classified 85.1% of cc-RCCs in terms of nuclear grade, with an AUC of 0.860.

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

PURPOSE

To compare the predictive models that can incorporate a set of CT image features for preoperatively differentiating the high grade (Fuhrman III-IV) from low grade (Fuhrman I-II) clear cell renal cell carcinoma (ccRCC).

MATERIAL AND METHODS

One hundred and fourteen patients with ccRCC treated with a partial or radical nephrectomy were enrolled in the training cohort. The six non-texture features, including Pseudocapsule, Round mass, maximal tumor diameter (Diameter_max), intratumoral artery (Artery_tumor), enhancement value of the tumor (TEV) and relative TEV (rTEV), were assessed for each tumor. The texture features were extracted from the CT images of the section with the largest area of renal mass at both corticomedullary and nephrographic phases. The least absolute shrinkage and selection operator (LASSO) was used to screen the most valuable texture features to calculate a texture score (Texture-score) for each patient. A logistic regression model was used in the training cohort to discriminate the high from low grade ccRCC at nephrectomy. The predictors would include all non-texture features in Model 1, all non-texture features and Texture-score in Model 2, and Texture-score in Model 3. The performance of the predictive models were tested and compared in an independent validation cohort composed of 92 cases with ccRCC.

RESULTS

Inter-rater agreement was good for each non-texture feature and Texture-score (the concordance correlation coefficient or Kappa coefficient > 0.70). The Texture-score was calculated via a linear combination of the 4 selected texture features. The three models shown good discrimination of the high from low grade ccRCC in the training cohort and the area under receiver operating characteristic curve (AUC) was 0.826 in Mode 1, 0.878 in Model 2 and 0.843 in Model 3, and a significant different AUC was found between Model 1 and Model 2. Application of the predictive models in the validation cohort still gave a discrimination (AUC > 0.670), and the Texture-score based models with or without the non-texture features (Model 2 and 3) showed a better discrimination of the high from low grade ccRCC (P < 0.05).

CONCLUSION

This study presented the Texture-score based models can facilitate the preoperative discrimination of the high from low grade ccRCC.

Diagnostic Performance of DWI for Differentiating High- From Low-Grade Clear Cell Renal Cell Carcinoma: A Systematic Review and Meta-Analysis

OBJECTIVE

The purpose of our study was to review the diagnostic performance of DWI for differentiating high- from low-grade clear cell renal cell carcinoma (RCC).

MATERIALS AND METHODS

MEDLINE, EMBASE, and Cochrane library databases were searched up to March 15, 2017. We included diagnostic accuracy studies that used DWI for differentiating high- from low-grade clear cell RCC compared with histopathologic results of Fuhrman grade based on nephrectomy or biopsy specimens in original research articles. Two independent reviewers assessed methodologic quality using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Sensitivity and specificity of the included studies were pooled and graphically presented using a hierarchic summary ROC plot. For heterogeneity exploration, we assessed the presence of a threshold effect and performed meta-regression analyses.

RESULTS

Eight retrospective studies (394 patients with 397 clear cell RCCs) were included. Pooled sensitivity was 0.78 (95% CI, 0.68-0.85) with a specificity of 0.86 (95% CI, 0.70-0.94). A considerable threshold effect was observed with a correlation coefficient of 0.811 (95% CI, 0.248-0.964) between the sensitivity and false-positive rate. At meta-regression analysis, apparent diffusion coefficient (× 10 mm²/s) cutoff value (< 1.57 vs ≥ 1.57; p = 0.03) and location of ROI (solid portion vs whole tumor; p = 0.04) were significant factors affecting heterogeneity. Other factors with regard to patients and tumors, study, and MRI characteristics were not significant (p = 0.17-0.91).

CONCLUSION

DWI shows moderate diagnostic performance for differentiating high-from low-grade clear cell RCC. Substantial heterogeneity was observed because of a threshold effect. Further prospective studies may be needed; all included studies were retrospective.