Correlation of CT imaging features and tumor size with Fuhrman grade of clear cell renal cell carcinoma

Diagnostic value of contrast-enhanced CT in clear cell renal cell carcinoma: a systematic review and meta-analysis

OBJECTIVE

Contrast-enhanced computed tomography (CECT) improves lesion contrast with surrounding tissues through the injection of contrast agents. This enhancement allows for more precise lesion characterization, aiding in the early diagnosis of clear cell renal cell carcinoma (ccRCC). This meta-analysis aims to assess the diagnostic efficacy of CECT in ccRCC and to provide an ideal imaging examination method for the preoperative diagnosis of ccRCC.

METHODS

We conducted a comprehensive search across six major online databases: PubMed, Web of Science, Cochrane Library, WANFANG DATA, China National Knowledge Infrastructure, and Chinese BioMedical Literature Database (CBM). The objective was to collate and analyze studies that evaluate the diagnostic utility of CECT in the identification of ccRCC. Meta-disc 1.4 and Stata 16.0 were used to conduct a meta-analysis and evaluate the diagnostic accuracy of CECT for ccRCC.

RESULTS

The meta-analysis included 17 relevant studies investigating the diagnostic value of CECT for ccRCC. The combined sensitivity and specificity of CECT were 0.88 (95% confidence interval: 0.83-0.91) and 0.82 (95%CI: 0.75-0.87), respectively. Positive diagnostic likelihood ratio = 4.87 (95%CI: 3.47-6.84), negative diagnostic likelihood ratio = 0.15 (95%CI: 0.11-0.21), and diagnostic odds ratio = 32.67 (95%CI: 18.21-58.61). In addition, the area under the ROC curve was 0.92 (95%CI: 0.89-0.94), indicating that CECT has a decent discriminative ability in diagnosing ccRCC.

CONCLUSIONS

CECT is recognized as a highly effective imaging tool for diagnosing ccRCC. It provides valuable guidance in the preoperative assessment and planning of surgical strategies for patients with ccRCC.

CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma

OBJECTIVES

This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs).

METHODS

CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation.

RESULTS

There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76).

CONCLUSIONS

Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC.

ADVANCES IN KNOWLEDGE

This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management.

CT features based preoperative predictors of aggressive pathology for clinical T1 solid renal cell carcinoma and the development of nomogram model

BACKGROUND

We aimed to identify preoperative predictors of aggressive pathology for cT1 solid renal cell carcinoma (RCC) by combining clinical features with qualitative and quantitative CT parameters, and developed a nomogram model.

METHODS

We conducted a retrospective study of 776 cT1 solid RCC patients treated with partial nephrectomy (PN) or radical nephrectomy (RN) between 2018 and 2022. All patients underwent four-phase contrast-enhanced CT scans and the CT parameters were obtained by two experienced radiologists using region of interest (ROI). Aggressive pathology was defined as patients with nuclear grade III-IV; upstage to pT3a; type II papillary renal cell carcinoma (pRCC), collecting duct or renal medullary carcinoma, unclassified RCC or sarcomatoid/rhabdoid features. Univariate and multivariate logistic analyses were used to determine significant predictors and develop the nomogram model. To evaluate the accuracy and clinical utility of the nomogram model, we used the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis (DCA), risk stratification, and subgroup analysis.

RESULTS

Of the 776 cT1 solid RCC patients, 250 (32.2%) had aggressive pathological features. The interclass correlation coefficient (ICC) of CT parameters accessed by two reviewers ranged from 0.758 to 0.982. Logistic regression analyses showed that neutrophil-to-lymphocyte ratio (NLR), distance to the collecting system, CT necrosis, tumor margin irregularity, peritumoral neovascularity, and RER-NP were independent predictive factors associated with aggressive pathology. We built the nomogram model using these significant variables, which had an area under the curve (AUC) of 0.854 in the ROC curve.

CONCLUSIONS

Our research demonstrated that preoperative four-phase contrast-enhanced CT was critical for predicting aggressive pathology in cT1 solid RCC, and the constructed nomogram was useful in guiding patient treatment and postoperative follow-up.

Head-to-head comparisons of enhanced CT, 68Ga-PSMA-11 PET/CT and 18F-FDG PET/CT in identifying adverse pathology of clear-cell renal cell carcinoma: a prospective study

OBJECTIVES

Accurate preoperative prediction of adverse pathology is crucial for treatment planning of renal cell carcinoma (RCC). Previous studies have emphasized the potential of prostate-specific membrane antigen positron emission tomography / computed tomography (PSMA PET/CT) in differentiating between benign and malignant localized renal tumors. However, there is a scarcity of case reports elucidating the identification of aggressive pathological features using PET/CT. Our study was designed to prospectively compare the diagnostic value of enhanced CT, 68Ga-PSMA-11 and 18F-fluorodeoxyglucose (18F-FDG) PET/CT in clear-cell renal cell carcinoma (ccRCC) with necrosis or sarcomatoid or rhabdoid differentiation.

MATERIALS AND METHODS

A prospective case series of patients with a newly diagnosed renal mass who underwent enhanced CT, 68Ga-PSMA-11 and 18F-FDG PET/CT within 30 days prior to nephrectomy was included. Complete preoperative and postoperative clinicopathological data were recorded. Patients who received neoadjuvant targeted therapy, declined enhanced CT or PET/CT scanning, refused surgical treatment or had non-ccRCC pathological indications were excluded. Radiological parameters were compared within subgroups of pathological characteristics. Bonferroni corrections were used to adjust for multiple testing and statistical significance was set at a p-value less than 0.017.

RESULTS

Seventy-two patients were available for the final analysis. Enhanced CT demonstrated poor performance in identifying necrosis, sarcomatoid or rhabdoid differentiation and adverse pathology (all P > 0.05). The maximum standardized uptake value (SUVmax) of 68Ga-PSMA-11 PET/CT was more effective than 18F-FDG PET/CT in identifying tumor necrosis and adverse pathology, with an area under the curve (AUC) of 0.85 (cutoff value=25.26, p<0.001; Delong test z=2.709, p=0.007) for tumor necrosis and AUC of 0.90 (cutoff value=25.26, p<0.001; Delong test z=3.433, p<0.001) for adverse pathology. However, no significant statistical difference was found between 68Ga-PSMA-11 and 18F-FDG PET/CT in predicting sarcomatoid or rhabdoid feature (AUC of 0.91 vs.0.75, Delong test z=1.998, p=0.046). Subgroup analyses based on age, sex, tumor location, maximal diameter, stage and WHO/ISUP grade demonstrated that 68Ga-PSMA-11 PET/CT SUVmax had a significant predictive value for adverse pathology. Enhanced CT value and SUVmax demonstrated strong reliability [intraclass correlation coefficient (ICC) > 0.80], indicating a robust correlation.

CONCLUSIONS

68Ga-PSMA-11 PET/CT demonstrates distinct advantages in identifying aggressive pathological features of primary ccRCC when compared to enhanced CT and 18F-FDG PET/CT. Further research and assessment are warranted to fully establish the clinical utility of 68Ga-PSMA-11 PET/CT in ccRCC.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma

Objective

This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC).

Methods

A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC).

Results

The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039).

Conclusion

The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

Characterization of renal artery variation in patients with clear cell renal cell carcinoma and the predictive value of accessory renal artery in pathological grading of renal cell carcinoma: a retrospective and observational study

Objective

To explore the characteristics of renal artery variation in patients with renal cell carcinoma and to evaluate the predicting value of accessory renal artery in the pathological grading of renal cell carcinoma.

Methods

The clinicopathological data of patients with clear cell renal cell carcinoma diagnosed in the Department of Urology of the First Hospital of Shanxi Medical University from September 2019 to March 2023 were retrospectively analyzed. All patients underwent visual three-dimensional model reconstruction from computed tomography images. All kidneys were divided into two groups: the affected kidney and the healthy kidney, and the incidence of renal artery variation in the two groups was analyzed. Then, according to the existence of accessory renal artery in the affected kidney, the patients were divided into two groups, and the relationship between accessory renal artery and clinicopathological features of patients with clear cell renal cell carcinoma was analyzed. Finally, univariate and multivariate logistic regression analyses were performed to determine the predictors of Fuhrman grading of clear cell renal cell carcinoma, and the predictive ability of the model was evaluated by the receiver operating characteristic curve.

Results

The incidence of renal artery variation and accessory renal artery in the affected kidney was significantly higher than them in the healthy kidney. The patients with accessory renal artery in the affected kidney had larger tumor maximum diameter, higher Fuhrman grade and more exophytic growth. The presence of accessory renal artery on the affected kidney and the maximum diameter of tumor are independent predictors of high-grade renal cell carcinoma. The receiver operating characteristic curve suggests that the model has a good predictive ability.

Conclusion

The existence of accessory renal artery on the affected kidney may be related to the occurrence and development of clear cell renal cell carcinoma, and can better predict Fuhrman grade of clear cell renal cell carcinoma. The finding provides a reference for the future diagnostic evaluation of RCC, and provides a new direction for the study of the pathogenesis of RCC.

Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas

This study was to assess the effect of the predictive model for distinguishing clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC) by establishing predictive radiomic models based on enhanced-computed tomography (CT) images of renal cell carcinoma (RCC). A total of 190 cases with RCC confirmed by pathology were retrospectively analyzed, with the patients being randomly divided into two groups, including the training set and testing set according to the ratio of 7:3. A total of 396 radiomic features were computationally obtained and analyzed with the Correlation between features, Univariate Logistics and Multivariate Logistics. Finally, 4 features were selected, and three machine models (Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR)) were established to discriminate RCC subtypes. The radiomics performance was compared with that of radiologist diagnosis. In the testing set, the RF model had an area under the curve (AUC) value of 0.909, a sensitivity of 0.956, and a specificity of 0.538. The SVM model had an AUC value of 0.841, a sensitivity of 1.0, and a specificity of 0.231, in the testing set. The LR model had an AUC value of 0.906, a sensitivity of 0.956, and a specificity of 0.692, in the testing set. The sensitivity and specificity of radiologist diagnosis to differentiate ccRCC from non-ccRCC were 0.850 and 0.581, respectively, with the AUC value of the radiologist diagnosis as 0.69. In conclusion, radiomics models based on CT imaging data show promise for augmenting radiological diagnosis in renal cancer, especially for differentiating ccRCC from non-ccRCC.

Tumor grade estımatıon of clear cell and papıllary renal cell carcınomas usıng contrast-enhanced MDCT and FSE T2 weıghted MR ımagıng: radıology-pathology correlatıon

BACKGROUND

Discrimination of low grade (grade 1-2) renal tumors from high grade (grade 3-4) ones carries crucial importance in terms of the management of these patients and also in the decision-making of appropriate treatment strategies. Our aim was to investigate whether contrast-enhanced multidetector computed tomography (MDCT) and T2 weighted fast spin echo (FSE) magnetic resonance imaging (MRI) could play a specific role in the discrimination of low grade versus high grade tumors in clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) patients.

METHODS

In this study, we retrospectively evaluated 66 RCC patients based on histopathologic findings who had underwent either partial or total nephrectomies. Our cohort consisted of 52 ccRCC and 14 pRCC patients, of whom 50 were male (%76) and 16 were female (%24). Among the 52 ccRCC patients, 18 had both cortico-medullary phase contrast-enhanced CT and MRI, 15 had only cortico-medullary phase CT and 19 had only MRI examination. In the pRCC group, 8 patients had both cortico-medullary phase contrast-enhanced CT and MRI, 3 had only cortico-medullary phase CT and 3 had only MRI. We both calculated mean tumor attenuation values on cortico-medullary phase MDCT images as HU (hounsfield unit) and also tumor mean signal intensity values on FSE T2 weighted MR images, using both region of interest and whole lesion measurements including normal renal cortex. The obtained values were compared with the grading results of the ccRCC and pRCC tumors according to the WHO/International Society of Urological Pathology grading system.

RESULTS

A significant positive correlation was found between the mean attenuation values of both tumor subtypes on cortico-medullary phase contrast-enhanced CT and their grades (p < 0.001). High grade tumors exhibited higher mean attenuation values (74.3 ± 22.3 HU) than the low grade tumors (55.2 ± 23.7 HU) in both subtypes. However, a statistically significant correlation was not found between the mean signal intensity values of the two tumor subtypes on FSE T2 weighted MR images and their grades (p > 0.05). Low grade tumors had a mean signal intensity value of 408.9 ± 44.6, while high grade tumors showed a value of 382.1 ± 44.2. The analysis of the ccRCC group patients, yielded a statistically significant correlation between the mean signal intensity values on T2 weighted images and tumor grading (p < 0.001). Low grade (grade 1-2) ccRCC patients exhibited higher mean signal intensity values (475.7 ± 51.3), as compared to those of high grade (grade 3-4) (418.5 ± 45.7) tumors. On the other hand, analysis of the pRCC group patients revealed that there was a significant correlation between the mean attenuation values of tumors on cortico-medullary phase contrast-enhanced CT and their grades (p < 0.001). High grade papillary subtype tumors (54.2 ± 25.2) showed higher mean attenuation values than the low grade (35.5 ± 18.8) ones.

CONCLUSIONS

Contrast-enhanced MDCT and T2 weighted FSE MRI can play a considerable role in the discrimination of low grade versus high grade tumors of both subtype RCC patients. Thus, these non-invasive evaluation techniques may have positive impact on the determination of the management and treatment strategies of these patients.

Comparison of Different Machine Models Based on Contrast-Enhanced Computed Tomography Radiomic Features to Differentiate High From Low Grade Clear Cell Renal Cell Carcinomas

Purpose

This study was to investigate the role of different radiomics models with enhanced computed tomography (CT) scan in differentiating low from high grade renal clear cell carcinomas.

Materials and Methods

CT data of 190 cases with pathologically confirmed renal cell carcinomas were collected and divided into the training set and testing set according to different time periods, with 122 cases in the training set and 68 cases in the testing set. The region of interest (ROI) was delineated layer by layer.

Results

A total of 402 radiomics features were extracted for analysis. Six of the radiomic parameters were deemed very valuable by univariate analysis, rank sum test, LASSO cross validation and correlation analysis. From these six features, multivariate logistic regression model, support vector machine (SVM), and decision tree model were established for analysis. The performance of each model was evaluated by AUC value on the ROC curve and decision curve analysis (DCA). Among the three prediction models, the SVM model showed a high predictive efficiency. The AUC values of the training set and the testing set were 0.84 and 0.83, respectively, which were significantly higher than those of the decision tree model and the multivariate logistic regression model. The DCA revealed a better predictive performance in the SVM model that possessed the highest degree of coincidence.

Conclusion

Radiomics analysis using the SVM radiomics model has highly efficiency in discriminating high- and low-grade clear cell renal cell carcinomas.

Learnable image histograms-based deep radiomics for renal cell carcinoma grading and staging

Fuhrman cancer grading and tumor-node-metastasis (TNM) cancer staging systems are typically used by clinicians in the treatment planning of renal cell carcinoma (RCC), a common cancer in men and women worldwide. Pathologists typically use percutaneous renal biopsy for RCC grading, while staging is performed by volumetric medical image analysis before renal surgery. Recent studies suggest that clinicians can effectively perform these classification tasks non-invasively by analyzing image texture features of RCC from computed tomography (CT) data. However, image feature identification for RCC grading and staging often relies on laborious manual processes, which is error prone and time-intensive. To address this challenge, this paper proposes a learnable image histogram in the deep neural network framework that can learn task-specific image histograms with variable bin centers and widths. The proposed approach enables learning statistical context features from raw medical data, which cannot be performed by a conventional convolutional neural network (CNN). The linear basis function of our learnable image histogram is piece-wise differentiable, enabling back-propagating errors to update the variable bin centers and widths during training. This novel approach can segregate the CT textures of an RCC in different intensity spectra, which enables efficient Fuhrman low (I/II) and high (III/IV) grading as well as RCC low (I/II) and high (III/IV) staging. The proposed method is validated on a clinical CT dataset of 159 patients from The Cancer Imaging Archive (TCIA) database, and it demonstrates 80% and 83% accuracy in RCC grading and staging, respectively.

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 Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.

Methods

Patients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.

Results

A total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765–0.9585) and 0.8088 (95% CI: 0.7064–0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353–0.8987) and 0.8017 (95% CI: 0.6878–0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646–0.9824) and an AUC of 0.9099 (95% CI: 0.8324–0.9873) for the training cohort and validation cohort, respectively.

Conclusion

We developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.

Effects of LncRNA HCP5/miR-214-3p/MAPK1 Molecular Network on Renal Cell Carcinoma Cells

Background

Recent researches have shown that long non-coding RNA (LncRNA) is often disordered and acts in many carcinomas. Clear cell renal cell carcinoma (ccRCC) is the main reason for carcinoma-related deaths, which are mainly caused by the metastasis. HCP5 is a newly discovered LcnRNA. Early studies have found that HCP5 acts in neoplasm metastasis, but the mechanism of HCP5 in ccRCC is still unclear.

Methods

The expression of HCP5 in human renal cell carcinoma (RCC) was detected by real-time quantitative PCR. The biological effect of LncRNAs in proliferation, migration, invasion and metastasis of RCC cells was explored by gain-of-function and loss-of-function tests. The molecular mechanism of LncRNAs was explored by RNA immunoprecipitation and Western blot.

Results

qRT-PCR revealed that HCP5 was enhanced in neoplasm tissues of ccRCC patients and correlated with the metastatic characteristics of RCC. Over-expression of HCP5 promoted the proliferation, migration and invasion of renal carcinoma cells. The deletion of HCP5 inhibited the proliferation, migration and invasion of RCC in vitro and the metastasis of RCC in vivo. Mechanically, HCP5 inhibited the growth and metastasis of ccRCC cells by regulating miR-214-3p/MAPK1 axis.

Conclusion

HCP5, as a key LncRNA, can promote ccRCC metastasis by regulating miR-214-3p/MAPK1 axis and may be a biomarker and be helpful for judging the prognosis of ccRCC.

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.

Using texture analysis as a predictive factor of subtype, grade and stage of renal cell carcinoma

OBJECTIVE

The aim of this study was to evaluate the correlation between the tissue texture analysis and the histological subtypes, grade and stage of the disease in patients with renal cell carcinoma (RCC).

MATERIALS AND METHODS

Seventy-seven patients who underwent computed tomography due to renal mass and diagnosed with RCC as a result of pathological examination were retrospectively analyzed. In these analyses, the demographic characteristics, pathological and radiological findings of the patients were evaluated. The masses were introduced to the Radiomics extension of the software and the first- and second-order texture analysis parameters were obtained. The correlation of these parameters with histological subtype, Fuhrman grade and TNM stage was investigated.

RESULTS

In the comparison of the Radiomics values by stages, "minimum", "Long Run Low Gray-level Emphasis" values were higher in the stage 1-2 group, while "Energy", "Total energy", "Range", "Joint Average", "Sum Average", "Gray-Level Non-Uniformity", "Short-Run High Gray-level Emphasis ", "Run Length Non-Uniformity "and "High Gray-Level Run Emphasis "values were higher in the stage 3-4 group. Of these parameters, only "Gray-Level Non-Uniformity" and "Run Length Non-Uniformity'' values were significantly lower in tumors with low Fuhrman grade (1-2) and low TNM stage (1-2). There was no statistically significant correlation between the parameters found to be significant in histological subtype differentiation and Fuhrman grade and TNM stage.

CONCLUSION

This study demonstrates that "Gray-Level Non-Uniformity" and "Run Length Non-Uniformity "parameters in the texture analysis method can be used to evaluate the prognosis in patients with RCC.

2-[18F]FDG PET/CT parameters associated with WHO/ISUP grade in clear cell renal cell carcinoma

PURPOSE

To explore the potential parameters from preoperative 2-[¹⁸F]FDG PET/CT that might associate with the World Health Organization/the International Society of Urological Pathology (WHO/ISUP) grade in clear cell renal cell carcinoma (ccRCC).

METHODS

One hundred twenty-five patients with newly diagnosed ccRCC who underwent 2-[¹⁸F]FDG PET/CT prior to surgery or biopsy were retrospectively reviewed. The metabolic parameters and imaging features obtained from 2-[¹⁸F]FDG PET/CT examinations were analyzed in combination with clinical characteristics. Univariate and multivariate logistic regression analyses were performed to identify the predictive factors of WHO/ISUP grade.

RESULTS

Metabolic parameters of primary tumor maximum standardized uptake value (SUVmax), tumor-to-liver SUV ratio (TLR), and tumor-to-kidney SUV ratio (TKR) were significantly different between any two of the four different WHO/ISUP grades, except those between the WHO/ISUP grade 3 and grade 4. The optimal cutoff values to predict high WHO/ISUP grade for SUVmax, TLR, and TKR were 4.15, 1.63, and 1.59, respectively. TLR (AUC: 0.841) was superior to TKR (AUC: 0.810) in distinguishing high and low WHO/ISUP grades (P = 0.0042). In univariate analysis, SUVmax, TLR, TKR, primary tumor size, tumor thrombus, distant metastases, and clinical symptoms could discriminate between the high and low WHO/ISUP grades (P < 0.05). In multivariate analysis, TLR (P < 0.001; OR: 1.732; 95%CI: 1.289-2.328) and tumor thrombus (P < 0.001; OR: 6.199; 95%CI: 2.499-15.375) were significant factors for differentiating WHO/ISUP grades.

CONCLUSION

Elevated TLR (> 1.63) and presence of tumor thrombus from preoperative 2-[¹⁸F]FDG PET/CT can distinguish high WHO/ISUP grade ccRCC effectively. 2-[¹⁸F]FDG PET/CT may be a feasible method for noninvasive assessment of WHO/ISUP grade.

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.

Preoperative CT Radiomics Predicting the SSIGN Risk Groups in Patients With Clear Cell Renal Cell Carcinoma: Development and Multicenter Validation

Objective: The stage, size, grade, and necrosis (SSIGN) score can facilitate the assessment of tumor aggressiveness and the personal management for patients with clear cell renal cell carcinoma (ccRCC). However, this score is only available after the postoperative pathological evaluation. The aim of this study was to develop and validate a CT radiomic signature for the preoperative prediction of SSIGN risk groups in patients with ccRCC in multicenters.

Methods: In total, 330 patients with ccRCC from three centers were classified into the training, external validation 1, and external validation 2 cohorts. Through consistent analysis and the least absolute shrinkage and selection operator, a radiomic signature was developed to predict the SSIGN low-risk group (scores 0–3) and intermediate- to high-risk group (score ≥ 4). An image feature model was developed according to the independent image features, and a fusion model was constructed integrating the radiomic signature and the independent image features. Furthermore, the predictive performance of the above models for the SSIGN risk groups was evaluated with regard to their discrimination, calibration, and clinical usefulness.

Results: A radiomic signature consisting of sixteen relevant features from the nephrographic phase CT images achieved a good calibration (all Hosmer–Lemeshow p > 0.05) and favorable prediction efficacy in the training cohort [area under the curve (AUC): 0.940, 95% confidence interval (CI): 0.884–0.973] and in the external validation cohorts (AUC: 0.876, 95% CI: 0.811–0.942; AUC: 0.928, 95% CI: 0.844–0.975, respectively). The radiomic signature performed better than the image feature model constructed by intra-tumoral vessels (all p < 0.05) and showed similar performance with the fusion model integrating radiomic signature and intra-tumoral vessels (all p > 0.05) in terms of the discrimination in all cohorts. Moreover, the decision curve analysis verified the clinical utility of the radiomic signature in both external cohorts.

Conclusion: Radiomic signature could be used as a promising non-invasive tool to predict SSIGN risk groups and to facilitate preoperative clinical decision-making for patients with ccRCC.

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.

CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade

OBJECTIVE

CT texture analysis (CTTA) using filtration-histogram-based parameters has been associated with tumor biologic correlates such as glucose metabolism, hypoxia, and tumor angiogenesis. We investigated the utility of these parameters for differentiation of clear cell from papillary renal cancers and prediction of Fuhrman grade.

METHODS

A retrospective study was performed by applying CTTA to pretreatment contrast-enhanced CT scans in 290 patients with 298 histopathologically confirmed renal cell cancers of clear cell and papillary types. The largest cross section of the tumor on portal venous phase axial CT was chosen to draw a region of interest. CTTA comprised of an initial filtration step to extract features of different sizes (fine, medium, coarse spatial scales) followed by texture quantification using histogram analysis.

RESULTS

A significant increase in entropy with fine and medium spatial filters was demonstrated in clear cell RCC (p = 0.047 and 0.033, respectively). Area under the ROC curve of entropy at fine and medium spatial filters was 0.804 and 0.841, respectively. An increased entropy value at coarse filter correlated with high Fuhrman grade tumors (p = 0.01). The other texture parameters were not found to be useful.

CONCLUSION

Entropy, which is a quantitative measure of heterogeneity, is increased in clear cell renal cancers. High entropy is also associated with high-grade renal cancers. This parameter may be considered as a supplementary marker when determining aggressiveness of therapy.

KEY POINTS

• CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis may help to separate different types of renal cancers. • CT texture analysis may enhance individualized treatment of renal cancers.

Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images

BACKGROUND

To explore whether radiomics combined with computed tomography (CT) images can be used to establish a model for differentiating high grade (International Society of Urological Pathology [ISUP] grade III-IV) from low-grade (ISUP I-II) clear cell renal cell carcinoma (ccRCC).

METHODS

For this retrospective study, 3-phase contrast-enhanced CT images were collected from 227 patients with pathologically confirmed ISUP-grade ccRCC (155 cases in the low-grade group and 72 cases in the high-grade group). First, we delineated the largest dimension of the tumor in the corticomedullary and nephrographic CT images to obtain the region of interest. Second, variance selection, single variable selection, and the least absolute shrinkage and selection operator were used to select features in the corticomedullary phase, nephrographic phase, and 2-phase union samples, respectively. Finally, a model was constructed using the optimal features, and the receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the predictive performance of the features in the training and validation queues. A Z test was employed to compare the differences in AUC values.

RESULTS

The support vector machine (SVM) model constructed using the screening features for the 2-stage joint samples can effectively distinguish between high- and low-grade ccRCC, and obtained the highest prediction accuracy. Its AUC values in the training queue and the validation queue were 0.88 and 0.91, respectively. The results of the Z test showed that the differences between the 3 groups were not statistically significant.

CONCLUSION

The SVM model constructed by CT-based radiomic features can effectively identify the ISUP grades of ccRCC.

Renal cell carcinoma: predicting RUNX3 methylation level and its consequences on survival with CT features

PURPOSE

To investigate associations between CT imaging features, RUNX3 methylation level, and survival in clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

Patients were divided into high RUNX3 methylation and low RUNX3 methylation groups according to RUNX3 methylation levels (the threshold was identified by using X-tile). The CT scanning data from 106 ccRCC patients were retrospectively analyzed. The relationship between RUNX3 methylation level and overall survivals was evaluated using the Kaplan-Meyer analysis and Cox regression analysis (univariate and multivariate). The relationship between RUNX3 methylation level and CT features was evaluated using chi-square test and logistic regression analysis (univariate and multivariate).

RESULTS

β value cutoff of 0.53 to distinguish high methylation (N = 44) from low methylation tumors (N = 62). Patients with lower levels of methylation had longer median overall survival (49.3 vs. 28.4) months (low vs. high, adjusted hazard ratio [HR] 4.933, 95% CI 2.054-11.852, p < 0.001). On univariate logistic regression analysis, four risk factors (margin, side, long diameter, and intratumoral vascularity) were associated with RUNX3 methylation level (all p < 0.05). Multivariate logistic regression analysis found that three risk factors (side: left vs. right, odds ratio [OR] 2.696; p = 0.024; 95% CI 1.138-6.386; margin: ill-defined vs. well-defined, OR 2.685; p = 0.038; 95% CI 1.057-6.820; and intratumoral vascularity: yes vs. no, OR 3.286; p = 0.008; 95% CI 1.367-7.898) were significant independent predictors of high methylation tumors. This model had an area under the receiver operating characteristic curve (AUC) of 0.725 (95% CI 0.623-0.827).

CONCLUSIONS

Higher levels of RUNX3 methylation are associated with shorter survival in ccRCC patients. And presence of intratumoral vascularity, ill-defined margin, and left side tumor were significant independent predictors of high methylation level of RUNX3 gene.

KEY POINTS

• RUNX3 methylation level is negatively associated with overall survival in ccRCC patients. • Presence of intratumoral vascularity, ill-defined margin, and left side tumor were significant independent predictors of high methylation level of RUNX3 gene.

Association of qualitative and quantitative imaging features on multiphasic multidetector CT with tumor grade in clear cell renal cell carcinoma

PURPOSE

The purpose of the study was to determine if enhancement features and qualitative imaging features on multiphasic multidetector computed tomography (MDCT) were associated with tumor grade in patients with clear cell renal cell carcinoma (ccRCC).

METHODS

In this retrospective, IRB approved, HIPAA-compliant, institutional review board-approved study with waiver of informed consent, 127 consecutive patients with 89 low grade (LG) and 43 high grade (HG) ccRCCs underwent preoperative four-phase MDCT in unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases. Previously published quantitative (absolute peak lesion enhancement, absolute peak lesion enhancement relative to normal enhancing renal cortex, 3D whole lesion enhancement and the wash-in/wash-out of enhancement within the 3D whole lesion ROI) and qualitative (enhancement pattern; presence of necrosis; pattern of; tumor margin; tumor-parenchymal interface, tumor-parenchymal interaction; intratumoral vascularity; collecting system infiltration; renal vein invasion; and calcification) assessments were obtained for each lesion independently by two fellowship-trained genitourinary radiologists. Comparisons between variables included χ², ANOVA, and student t test. p values less than 0.05 were considered to be significant. Inter-reader agreement was obtained with the Gwet agreement coefficient (AC1) and standard error (SE) was reported.

RESULTS

No significant differences were observed between the LG and HG ccRCC cohorts with respect to absolute peak lesion enhancement and relative lesion enhancement ratio. There was a significant inverse correlation between low and high grade ccRCC and tumor enhancement the NP (71 HU vs. 54 HU, p < 0.001) and EX (52 HU vs. 39 HU, p < 0.001) phases using the 3D whole lesion ROI method. The percent wash-in of 3D enhancement from the UN to the CM phase was also significantly different between LG and HG ccRCCs (352% vs. 255%, p = 0.003). HG lesions showed significantly more calcification, necrosis, collecting system infiltration and ill-defined tumor margins (p < 0.05). Overall agreement between the two readers had a mean AC1 of 0.8172 (SE 0.0235).

CONCLUSIONS

Quantitatively, high grade ccRCC had significantly lower whole lesion enhancement in the NP and EX phases on MDCT. Qualitatively, high grade ccRCC were significantly more likely to be associated with calcifications, necrosis, collecting system infiltration, and an ill-defined tumor margin.

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.

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 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.

Factors associated with postoperative renal sinus invasion and perinephric fat invasion in renal cell cancer: treatment planning implications

In patients with renal cell carcinoma (RCC), postoperative upstaging including perinephric fat invasion (PNI) and renal sinus invasion (RSI) leads to unfavorable oncological outcomes. Determining the preoperative risk factors for postoperative upstaging could be beneficial for treatment planning. In this study, 267 RCC patients who underwent radical nephrectomy were studied retrospectively. The RSI incidence was significantly greater than that of PNI. Kaplan-Meier analysis revealed that patients with RSI, PNI, and RSI plus PNI had poorer disease-free-survival than those with neither RSI nor PNI. Univariate and multivariate logistic regression analyses indicated that a tumor extension into the sinus, an irregular tumor-sinus border, and an irregular tumor shape in CT/MRI imaging were independent risk factors for RSI. And a tumor larger than 5 cm, an irregular tumor-perinephric fat border, and a tumor necrosis were independent risk factors for PNI. Subgrouping of patients into low-, moderate-, and high-risk groups according to these factors, revealed a direct association between the risk factors and PNI/RSI incidence. In conclusion, in patients with RCC, preoperative risk factors associated with postoperative upstaging could be assessed by imaging data obtained using CT or MRI. Preoperative Risk group classification would be clinically useful for patient counseling and treatment planning.