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Lv D, Zhou H, Cui F, Wen J, Shuang W. 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. BMC Cancer 2023; 23:274. [PMID: 36966274 PMCID: PMC10039570 DOI: 10.1186/s12885-023-10756-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 03/20/2023] [Indexed: 03/26/2023] Open
Abstract
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.
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Affiliation(s)
- Dingyang Lv
- grid.452461.00000 0004 1762 8478Department of Urology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province China
- grid.452461.00000 0004 1762 8478First Clinical Medical College of Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province China
| | - Huiyu Zhou
- grid.452461.00000 0004 1762 8478Department of Urology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province China
- grid.452461.00000 0004 1762 8478First Clinical Medical College of Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province China
| | - Fan Cui
- grid.452461.00000 0004 1762 8478Department of Urology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province China
- grid.452461.00000 0004 1762 8478First Clinical Medical College of Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province China
| | - Jie Wen
- grid.452461.00000 0004 1762 8478Department of Urology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province China
- grid.452461.00000 0004 1762 8478First Clinical Medical College of Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province China
| | - Weibing Shuang
- grid.452461.00000 0004 1762 8478Department of Urology, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province China
- grid.452461.00000 0004 1762 8478First Clinical Medical College of Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province China
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Lai S, Sun L, Wu J, Wei R, Luo S, Ding W, Liu X, Yang R, Zhen X. Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma. Cancer Manag Res 2021; 13:999-1008. [PMID: 33568946 PMCID: PMC7869703 DOI: 10.2147/cmar.s290327] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 01/08/2021] [Indexed: 11/23/2022] Open
Abstract
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.
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Affiliation(s)
- Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong, 510520, People’s Republic of China
| | - Lei Sun
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China
| | - Jialiang Wu
- Department of Radiology, The University of Hong Kong Shenzhen Hospital, Shenzhen, Guangdong, 518000, People’s Republic of China
| | - Ruili Wei
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China
| | - Shiwei Luo
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China
| | - Wenshuang Ding
- Department of Pathology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China
| | - Xilong Liu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China
| | - Ruimeng Yang
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China
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Han D, Yu Y, Yu N, Dang S, Wu H, Jialiang R, He T. Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT. Br J Radiol 2020; 93:20200131. [PMID: 32706977 DOI: 10.1259/bjr.20200131] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
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.
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Affiliation(s)
- Dong Han
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Yong Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Nan Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Shan Dang
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Hongpei Wu
- Department of Pathology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | | | - Taiping He
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
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Deng Y, Soule E, Samuel A, Shah S, Cui E, Asare-Sawiri M, Sundaram C, Lall C, Sandrasegaran K. CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade. Eur Radiol 2019; 29:6922-6929. [PMID: 31127316 DOI: 10.1007/s00330-019-06260-2] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 04/01/2019] [Accepted: 04/30/2019] [Indexed: 12/20/2022]
Abstract
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.
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Affiliation(s)
- Yu Deng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Erik Soule
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Aster Samuel
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sakhi Shah
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Enming Cui
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun YAT-SEN University, Jiangmen, China
| | - Michael Asare-Sawiri
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Oncology, Hope Regional Cancer Center, Panama, FL, USA
| | - Chandru Sundaram
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Chandana Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Kumaresan Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology, Mayo Clinic, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.
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Giménez-Bachs JM, Salinas-Sánchez AS. Improving the diagnosis of renal masses: can we approach the histological diagnosis to the image? ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:56. [PMID: 30906760 DOI: 10.21037/atm.2018.12.58] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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