Contrast-enhanced CT in 100 clear cell renal cell cancers — an analysis of enhancement, tumour size, and survival

CT-based radiomics signature for differentiating pyelocaliceal upper urinary tract urothelial carcinoma from infiltrative renal cell carcinoma

Objectives

To develop a CT-based radiomics model and a combined model for preoperatively discriminating infiltrative renal cell carcinoma (RCC) and pyelocaliceal upper urinary tract urothelial carcinoma (UTUC), which invades the renal parenchyma.

Materials and methods

Eighty patients (37 pathologically proven infiltrative RCCs and 43 pathologically proven pyelocaliceal UTUCs) were retrospectively enrolled and randomly divided into a training set (n = 56) and a testing set (n = 24) at a ratio of 7:3. Traditional CT imaging characteristics in the portal venous phase were collected by two radiologists (SPH and ZXL, who have 4 and 30 years of experience in abdominal radiology, respectively). Patient demographics and traditional CT imaging characteristics were used to construct the clinical model. The radiomics score was calculated based on the radiomics features extracted from the portal venous CT images and the random forest (RF) algorithm to construct the radiomics model. The combined model was constructed using the radiomics score and significant clinical factors according to the multivariate logistic regression. The diagnostic efficacy of the models was evaluated using receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC).

Results

The RF score based on the eight validated features extracted from the portal venous CT images was used to build the radiomics model. Painless hematuria as an independent risk factor was used to build the clinical model. The combined model was constructed using the RF score and the selected clinical factor. Both the radiomics model and combined model showed higher efficacy in differentiating infiltrative RCC and pyelocaliceal UTUC in the training and testing cohorts with AUC values of 0.95 and 0.90, respectively, for the radiomics model and 0.99 and 0.90, respectively, for the combined model. The decision curves of the combined model as well as the radiomics model indicated an overall net benefit over the clinical model. Both the radiomics model and the combined model achieved a notable reduction in false-positive and false-negativerates, resulting in significantly higher accuracy compared to the visual assessments in both the training and testing cohorts.

Conclusion

The radiomics model and combined model had the potential to accurately differentiate infiltrative RCC and pyelocaliceal UTUC, which invades the renal parenchyma, and provide a new potentially non-invasive method to guide surgery strategies.

Computer tomography-based shape of tumor contour and texture of tumor heterogeneity are independent prognostic indicators for clinical T1b-T2 renal cell carcinoma

PURPOSE

To evaluate association between computer tomography (CT)-based features of renal cell carcinoma (RCC) and survival outcomes.

METHODS

Data of 958 patients with clinical T1b-T2 RCC who underwent partial/radical nephrectomy from June 2003 to March 2022 were retrospectively evaluated. CT images of patients were reviewed by two radiologists for texture analysis of tumor heterogeneity and shape analysis of tumor contour. Patients were divided into three groups according to patterns of CT-based features: (1) favorable feature group (n = 117); (2) intermediate feature group (n = 606); and (3) unfavorable feature group (n = 235). Kaplan-Meier survival analysis and multivariate Cox regression analysis were performed to evaluate overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS).

RESULTS

RCCs with unfavorable CT-based feature showed larger size on CT, higher nuclear grade, higher rate of histologic necrosis, and higher rate of capsular invasion than those in the other two groups (all p < 0.001). Unfavorable feature was associated with poorer OS (p = 0.001), CSS (p < 0.001), and RFS (p < 0.001) on Kaplan-Meier analysis. In multivariate analysis, intermediate and unfavorable features were independent predictors for recurrence (hazard ratio [HR] 2.51, 95% confidence interval [CI] 1.09-5.79, p = 0.031 and HR 3.71, 95% CI 1.58-8.73, p = 0.003, respectively), but not for overall death or RCC-specific death.

CONCLUSIONS

A combination of irregular tumor contour feature with heterogeneous tumor texture feature on CT is associated with poor RFS in clinical T1b-T2 RCC preoperatively.

Urinary collecting system invasion on multiphasic CT in renal cell carcinomas: prevalence, characteristics, and clinical significance

PURPOSE

The aim of this study was to determine the prevalence of collecting system invasion (CSI) on multiphasic CT, validate the pathological findings, and investigate the relationship between CSI and clinical outcomes in patients with renal cell carcinomas (RCC).

METHODS

Patients pathologically diagnosed with RCC between January 2008 and December 2017 were retrospectively enrolled in this study. They were divided into two groups according to the presence of CSI on multiphasic CT images. Patients' clinical characteristics, radiological findings, and overall survival (OS) and recurrence-free survival (RFS) rates were analyzed and compared between the groups. In addition, the correlation of radiological findings with pathological findings was investigated.

RESULTS

Among the included 347 kidneys of 340 patients, CSI was observed in 11 kidneys (3%; 95% confidence interval, 1.3-5.0%). In all the 11 kidneys, the tumors were pathologically diagnosed as clear cell RCC, and in one kidney, the tumor also had sarcomatoid features. When pathological CSI served as the standard of reference, the sensitivity, specificity, and accuracy of CSI on CT were 50%, 99.7%, and 97.1%, respectively. The OS and RFS rates were not significantly different between patients with CSI on CT and those without CSI.

CONCLUSION

This study found that the prevalence of RCC-related CSI was 3%. Because of the low prevalence, we cannot exclude the possibility that CSI on CT would be associated with the OS and RFS. Further studies are needed to determine whether CSI on CT can be an independent prognostic factor for survival in patients with RCC.

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.

Presence of Intratumoral Calcifications and Vasculature Is Associated With Poor Overall Survival in Clear Cell Renal Cell Carcinoma

PURPOSE

The objective of this study was to explore the prognostic significance of the preoperative computed tomography (CT) features in clear cell renal cell carcinoma.

PATIENTS AND METHODS

The clinical data and CT data from 210 patients (1 grade 1, 84 grade 2, 92 grade 3, and 32 grade 4) generated with The Cancer Imaging Archive were reviewed. Overall survival was assessed using Kaplan-Meyer analysis. The relationship between CT features and survivals were evaluated using univariate and multivariable Cox regression analysis.

RESULTS

The follow-up occurred between 13 and 3989 days (median, 1405 days; mean, 1434 days).On univariate Cox regressions, 4 preoperative CT features (intratumoral calcifications: yes vs no hazard ratio [HR], 2.054; 95% confidence interval [CI], 1.231-3.428; renal vein invasion: yes vs no HR, 2.013; 95% CI, 1.218-3.328; collecting system invasion: yes vs no HR, 2.139; 95% CI, 1.286-3.558; gross appearance of intratumoral vasculature: yes vs no HR, 2.385; 95% CI, 1.454-3.915) were significantly associated with overall survival (all P < 0.05). On multivariable Cox regression analysis, predictors of mortality in clear cell renal cell carcinoma were the presence of intratumoral calcifications (HR, 1.718; 95% CI, 1.014-2.911; P = 0.044) and gross appearance of intratumoral vasculature (HR, 2.137; 95% CI, 1.284-3.557; P = 0.003).

CONCLUSIONS

Presence of intratumoral calcifications and vasculature can be potential prognostic features to screen patients for unfavorable prognosis.

Quantitative computer-aided diagnostic algorithm for automated detection of peak lesion attenuation in differentiating clear cell from papillary and chromophobe renal cell carcinoma, oncocytoma, and fat-poor angiomyolipoma on multiphasic multidetector computed tomography

OBJECTIVE

To evaluate the performance of a novel, quantitative computer-aided diagnostic (CAD) algorithm on four-phase multidetector computed tomography (MDCT) to detect peak lesion attenuation to enable differentiation of clear cell renal cell carcinoma (ccRCC) from chromophobe RCC (chRCC), papillary RCC (pRCC), oncocytoma, and fat-poor angiomyolipoma (fp-AML).

MATERIALS AND METHODS

We queried our clinical databases to obtain a cohort of histologically proven renal masses with preoperative MDCT with four phases [unenhanced (U), corticomedullary (CM), nephrographic (NP), and excretory (E)]. A whole lesion 3D contour was obtained in all four phases. The CAD algorithm determined a region of interest (ROI) of peak lesion attenuation within the 3D lesion contour. For comparison, a manual ROI was separately placed in the most enhancing portion of the lesion by visual inspection for a reference standard, and in uninvolved renal cortex. Relative lesion attenuation for both CAD and manual methods was obtained by normalizing the CAD peak lesion attenuation ROI (and the reference standard manually placed ROI) to uninvolved renal cortex with the formula [(peak lesion attenuation ROI - cortex ROI)/cortex ROI] × 100%. ROC analysis and area under the curve (AUC) were used to assess diagnostic performance. Bland-Altman analysis was used to compare peak ROI between CAD and manual method.

RESULTS

The study cohort comprised 200 patients with 200 unique renal masses: 106 (53%) ccRCC, 32 (16%) oncocytomas, 18 (9%) chRCCs, 34 (17%) pRCCs, and 10 (5%) fp-AMLs. In the CM phase, CAD-derived ROI enabled characterization of ccRCC from chRCC, pRCC, oncocytoma, and fp-AML with AUCs of 0.850 (95% CI 0.732-0.968), 0.959 (95% CI 0.930-0.989), 0.792 (95% CI 0.716-0.869), and 0.825 (95% CI 0.703-0.948), respectively. On Bland-Altman analysis, there was excellent agreement of CAD and manual methods with mean differences between 14 and 26 HU in each phase.

CONCLUSION

A novel, quantitative CAD algorithm enabled robust peak HU lesion detection and discrimination of ccRCC from other renal lesions with similar performance compared to the manual method.