Imaging features and clinic value of mri and ct in diagnosis of clear cell renal cell carcinoma

Background and objectives: The use of non-invasive techniques to predict the histological type of renal masses can avoid a renal mass biopsy, thus being of great clinical interest. The aim of our study was to assess if quantitative multiphasic multidetector computed tomography (MDCT) enhancement patterns of renal masses (malignant and benign) may be useful to enable lesion differentiation by their enhancement characteristics. Materials and Methods: A total of 154 renal tumors were retrospectively analyzed with a four-phase MDCT protocol. We studied attenuation values using the values within the most avidly enhancing portion of the tumor (2D analysis) and within the whole tumor volume (3D analysis). A region of interest (ROI) was also placed in the adjacent uninvolved renal cortex to calculate the relative tumor enhancement ratio. Results: Significant differences were noted in enhancement and de-enhancement (diminution of attenuation measurements between the postcontrast phases) values by histology. The highest areas under the receiver operating characteristic curves (AUCs) of 0.976 (95% CI: 0.924–0.995) and 0.827 (95% CI: 0.752–0.887), respectively, were demonstrated between clear cell renal cell carcinoma (ccRCC) and papillary RCC (pRCC)/oncocytoma. The 3D analysis allowed the differentiation of ccRCC from chromophobe RCC (chrRCC) with a AUC of 0.643 (95% CI: 0.555–0.724). Wash-out values proved useful only for discrimination between ccRCC and oncocytoma (43.34 vs 64.10, p < 0.001). However, the relative tumor enhancement ratio (corticomedullary (CM) and nephrographic phases) proved useful for discrimination between ccRCC, pRCC, and chrRCC, with the values from the CM phase having higher AUCs of 0.973 (95% CI: 0.929–0.993) and 0.799 (95% CI: 0.721–0.864), respectively. Conclusions: Our observations point out that imaging features may contribute to providing prognostic information helpful in the management strategy of renal masses.

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Clear Cell Renal Cell Carcinoma: Multiphasic Multidetector CT Imaging Features Help Predict Genetic Karyotypes

Radiology ◽

10.1148/radiol.11101508 ◽

2011 ◽

Vol 261 (3) ◽

pp. 854-862 ◽

Cited By ~ 28

Author(s):

Steven C. Sauk ◽

Margaret S. Hsu ◽

Daniel J. A. Margolis ◽

David S. K. Lu ◽

Nagesh P. Rao ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Multidetector Ct ◽

Ct Imaging ◽

Imaging Features ◽

Cell Renal Cell Carcinoma

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Re: Radiogenomics of Clear Cell Renal Cell Carcinoma: Associations Between mRNA-based Subtyping and CT Imaging Features

Academic Radiology ◽

10.1016/j.acra.2019.08.020 ◽

2020 ◽

Vol 27 (2) ◽

pp. 310

Author(s):

Dongzhi Cen ◽

An Xian ◽

Bao Lv ◽

Wenhao Zeng

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Ct Imaging ◽

Imaging Features ◽

Cell Renal Cell Carcinoma

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

Acta Radiologica ◽

10.1177/0284185116649795 ◽

2016 ◽

Vol 58 (3) ◽

pp. 376-384 ◽

Cited By ~ 17

Author(s):

Saelin Oh ◽

Deuk Jae Sung ◽

Kyung Sook Yang ◽

Ki Choon Sim ◽

Na Yeon Han ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Roc Curve ◽

Tumor Size ◽

Renal Cell ◽

Clear Cell ◽

Ct Imaging ◽

Imaging Features ◽

Cell Renal Cell Carcinoma ◽

Fuhrman Grade

Background Identification of clinical features to determine the aggressive potential of tumors is highly warranted to stratify patients for adequate treatment. Computed tomography (CT) imaging features of clear cell renal cell carcinoma (ccRCC) may contribute to personalized risk assessment. Purpose To assess the correlation between CT imaging features and Fuhrman grade of ccRCC, and to identify the predictors of high Fuhrman grade in conjunction with tumor size. Material and Methods CT scans of 169 patients with 173 pathologically proven ccRCCs were retrospectively reviewed in consensus by two radiologists for the presence of intratumoral necrosis and intratumoral cyst and tumor size. Histologic grade was classified as either low (Fuhrman grade I or II) or high (Fuhrman grade III or IV). Statistical significance was evaluated by using univariate, multivariate regression, receiver operating characteristic (ROC) curve, and Spearman correlation analyses. Results On CT, 20 of the 173 tumors had intratumoral cysts, 60 had intratumoral necrosis, and 93 showed entirely solid tumors. The odds of high grade were higher with intratumoral necrosis and entirely solid tumor than with intratumoral cyst ( P < 0.03). Intratumoral necrosis showed a significantly high odds ratio of 25.73 for high Fuhrman grade. The ROC curve showed a threshold tumor size of 36 mm to predict high Fuhrman grade for overall tumors (area under the ROC curve, 0.70). In ccRCCs with intratumoral necrosis or cyst, tumor size did not significantly correlate with Fuhrman grade. Conclusion Intratumoral necrosis on CT was a strong and independent predictor of biologically aggressive ccRCCs, irrespective of tumor size.

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Radiogenomics of Clear Cell Renal Cell Carcinoma: Associations between CT Imaging Features and Mutations

Radiology ◽

10.1148/radiol.13130663 ◽

2014 ◽

Vol 270 (2) ◽

pp. 464-471 ◽

Cited By ~ 160

Author(s):

Christoph A. Karlo ◽

Pier Luigi Di Paolo ◽

Joshua Chaim ◽

A Ari Hakimi ◽

Irina Ostrovnaya ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Ct Imaging ◽

Imaging Features ◽

Cell Renal Cell Carcinoma

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Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.640881 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yeqian Huang ◽

Hao Zeng ◽

Linyan Chen ◽

Yuling Luo ◽

Xuelei Ma ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Prognostic Model ◽

Clear Cell ◽

Expression Patterns ◽

Machine Learning Algorithms ◽

Imaging Features ◽

Clinical Predictors ◽

Cell Renal Cell Carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients.MethodWe extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algorithm was then applied to integrate PRIF and the genes into a combined imaging-genomics prognostic factors (IGPF) model. Furthermore, we constructed a nomogram incorporating IGPF and clinical predictors as the integrative prognostic model for ccRCC patients.ResultsA total of four PRIF and four genes were identified as IGPF and were represented by corresponding risk score in RF model. The integrative IGPF model presented a better prediction performance than the PRIF model alone (average AUCs for 1-, 3-, and 5-year were 0.814 vs. 0.837, 0.74 vs. 0.806, and 0.689 vs. 0.751 in test set). Clinical characteristics including gender, TNM stage and IGPF were independent risk factors. The nomogram integrating clinical predictors and IGPF provided the best net benefit among the three models.ConclusionIn this study we established an integrative prognosis-related nomogram model incorporating imaging-genomic features and clinical indicators. The results indicated that IGPF may contribute to a comprehensive prognosis assessment for ccRCC patients.

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