scholarly journals Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features

2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Claudia-Gabriela Moldovanu ◽  
Bianca Boca ◽  
Andrei Lebovici ◽  
Attila Tamas-Szora ◽  
Diana Sorina Feier ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Predictive Ability ◽  
Nuclear Grade ◽  
Training Set ◽  
Cell Renal Cell Carcinoma ◽  
Validation Set ◽  
Sensitivity Specificity

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92–1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

scholarly journals Profiles of overall survival-related gene expression-based risk signature and their prognostic implications in clear cell renal cell carcinoma

2020 ◽  
Vol 40 (9) ◽  
Author(s):  
Zihao He ◽  
Tuo Deng ◽  
Xiaolu Duan ◽  
Guohua Zeng
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Family Member ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Training Set ◽  
Cell Renal Cell Carcinoma ◽  
Validation Set

Abstract The present work aimed to evaluate the prognostic value of overall survival (OS)-related genes in clear cell renal cell carcinoma (ccRCC) and to develop a nomogram for clinical use. Transcriptome data from The Cancer Genome Atlas (TCGA) were collected to screen differentially expressed genes (DEGs) between ccRCC patients with OS &gt; 5 years (149 patients) and those with &lt;1 year (52 patients). In TCGA training set (265 patients), seven DEGs (cytochrome P450 family 3 subfamily A member 7 (CYP3A7), contactin-associated protein family member 5 (CNTNAP5), adenylate cyclase 2 (ADCY2), TOX high mobility group box family member 3 (TOX3), plasminogen (PLG), enamelin (ENAM), and collagen type VII α 1 chain (COL7A1)) were further selected to build a prognostic risk signature by the least absolute shrinkage and selection operator (LASSO) Cox regression model. Survival analysis confirmed that the OS in the high-risk group was dramatically shorter than their low-risk counterparts. Next, univariate and multivariate Cox regression revealed the seven genes-based risk score, age, and Tumor, lymph Node, and Metastasis staging system (TNM) stage were independent prognostic factors to OS, based on which a novel nomogram was constructed and validated in both TCGA validation set (265 patients) and the International Cancer Genome Consortium cohort (ICGC, 84 patients). A decent predictive performance of the nomogram was observed, the C-indices and corresponding 95% confidence intervals of TCGA training set, validation set, and ICGC cohort were 0.78 (0.74–0.82), 0.75 (0.70–0.80), and 0.70 (0.60–0.80), respectively. Moreover, the calibration plots of 3- and 5 years survival probability indicated favorable curve-fitting performance in the above three groups. In conclusion, the proposed seven genes signature-based nomogram is a promising and robust tool for predicting the OS of ccRCC, which may help tailor individualized therapeutic strategies.

scholarly journals A Risk Score Model Based on Nine Differentially Methylated mRNAs for Predicting Prognosis of Patients with Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jingmin Zhou ◽  
Guanghua Liu ◽  
Xingcheng Wu ◽  
Zhien Zhou ◽  
Jialin Li ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Clear Cell ◽  
Good Prognosis ◽  
Training Set ◽  
Cell Renal Cell Carcinoma ◽  
Mrna Methylation ◽  
Score Model ◽  
Validation Set

Purpose. DNA methylation alterations play important roles in initiation and progression of clear cell renal cell carcinoma (ccRCC). In this study, we attempted to identify differentially methylated mRNA signatures with prognostic value for ccRCC. Methods. The mRNA methylation and expression profiling data of 306 ccRCC tumors were downloaded from The Cancer Genome Atlas (TCGA) to screen differentially methylated lncRNAs and mRNAs (DMLs and DMMs) between bad and good prognosis patients. Uni- and multivariable Cox regression analyses and LASSO Cox-PH regression analysis were used to select prognostic lncRNAs and mRNAs. Corresponding risk scores were calculated and compared for predictive performance in the training set using Kaplan-Meier OS and ROC curve analyses. The optimal risk score was then identified and validated in the validation set. Function enrichment analysis was conducted. Results. This study screened 461 DMMs and 63 DMLs between good prognosis and bad prognosis patients, and furthermore, nine mRNAs and six lncRNAs were identified as potential prognostic molecules. Compared to nine-mRNA status risk score model, six-lncRNA methylation risk score model, and six-lncRNA status risk score model, the nine-mRNA methylation risk score model showed superiority for prognosis stratification of ccRCC patients in the training set. The prognostic ability of the nine-mRNA methylation risk score model was validated in the validation set. The nine prognostic mRNAs were functionally associated with neuroactive ligand receptor interaction and inflammation-related pathways. Conclusion. The nine-mRNA methylation signature (DMRTA2, DRGX, FAM167A, FGGY, FOXI2, KRTAP2-1, TCTEX1D1, TTBK1, and UBE2QL1) may be a useful prognostic biomarker and tool for ccRCC patients. The present results would be helpful to elucidate the possible pathogenesis of ccRCC.

scholarly journals Computed Tomography-Based Radiomics Model for Predicting the WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma Preoperatively: A Multicenter Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Ruihui Wang ◽  
Zhengyu Hu ◽  
Xiaoyong Shen ◽  
Qidong Wang ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Logistic Regression ◽  
Prediction Model ◽  
Clear Cell ◽  
External Validation ◽  
Pathological Grade ◽  
Training Set ◽  
Cell Renal Cell Carcinoma ◽  
Validation Set

PurposeTo examine the ability of computed tomography radiomic features in multivariate analysis and construct radiomic model for identification of the the WHO/ISUP pathological grade of clear cell renal cell carcinoma (ccRCC).MethodsThis was a retrospective study using data of four hospitals from January 2018 to August 2019. There were 197 patients with a definitive diagnosis of ccRCC by post-surgery pathology or biopsy. These subjects were divided into the training set (n = 122) and the independent external validation set (n = 75). Two phases of Enhanced CT images (corticomedullary phase, nephrographic phase) of ccRCC were used for whole tumor Volume of interest (VOI) plots. The IBEX radiomic software package in Matlab was used to extract the radiomic features of whole tumor VOI images. Next, the Mann–Whitney U test and minimum redundancy-maximum relevance algorithm(mRMR) was used for feature dimensionality reduction. Next, logistic regression combined with Akaike information criterion was used to select the best prediction model. The performance of the prediction model was assessed in the independent external validation cohorts. Receiver Operating Characteristic curve (ROC) was used to evaluate the discrimination of ccRCC in the training and independent external validation sets.ResultsThe logistic regression prediction model constructed with seven radiomic features showed the best performance in identification for WHO/ISUP pathological grades. The Area Under Curve (AUC) of the training set was 0.89, the sensitivity comes to 0.85 and specificity was 0.84. In the independent external validation set, the AUC of the prediction model was 0.81, the sensitivity comes to 0.58, and specificity was 0.95.ConclusionA radiological model constructed from CT radiomic features can effectively predict the WHO/ISUP pathological grade of CCRCC tumors and has a certain clinical generalization ability, which provides an effective value for patient prognosis and treatment.

scholarly journals Sonographic Evaluation of Clear Cell Renal Cell Carcinoma

2018 ◽  
Vol 35 (2) ◽  
pp. 147-151
Author(s):  
Emily Rail ◽  
Janell Stormo
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Additional Information ◽  
Sonographic Evaluation ◽  
Proper Diagnosis ◽  
Convoluted Tubules

Clear cell renal cell carcinoma (RCC) is proposed to arise from the proximal convoluted tubules within the kidney. Diagnostic imaging plays a role in the detection of RCC. A case is presented of a clear cell RCC that was initially identified sonographically. Sonography detected and characterized the mass, which was further evaluated with computed tomography (CT) to obtain additional information, prior to a radical nephrectomy. The use of both CT and sonography led to proper diagnosis and treatment of this suspected tumor.

scholarly journals A Novel Immune-Related lncRNA-Based Model for Survival Prediction in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-37
Author(s):  
Zedan Zhang ◽  
Yanlin Tang ◽  
Yanjun Liu ◽  
Hongkai Zhuang ◽  
Enyu Lin ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
T Cells ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Functional Enrichment ◽  
Clinical Parameters ◽  
Training Set ◽  
Cell Renal Cell Carcinoma

Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer whose incidence and mortality rate are increasing. Identifying immune-related lncRNAs and constructing a model would probably provide new insights into biomarkers and immunotherapy for ccRCC and aid in the prognosis prediction. Methods. The transcription profile and clinical information were obtained from The Cancer Genome Atlas (TCGA). Immune-related gene sets and transcription factor genes were downloaded from GSEA website and Cistrome database, respectively. Tumor samples were divided into the training set and the testing set. Immune-related differentially expressed lncRNAs (IDElncRNAs) were identified from the whole set. Univariate Cox regression, LASSO, and stepwise multivariate Cox regression were performed to screen out ideal prognostic IDElncRNAs (PIDElncRNAs) from the training set and develop a multi-lncRNA signature. Results. Consequently, AC012236.1, AC078778.1, AC078950.1, AC087318.1, and AC092535.4 were screened to be significantly related to the prognosis of ccRCC patients, which were used to establish the five-lncRNA signature. Its wide diagnostic capacity was revealed in different subgroups of clinical parameters. Then AJCC-stage, Fuhrman-grade, pharmaceutical, age, and risk score regarded as independent prognostic factors were integrated to construct a nomogram, whose good performance in predicting 3-, 5-, and 7-year overall survival of ccRCC patients was revealed by time-dependent ROC curves and verified by the testing sets and ICGC dataset. The calibration plots showed great agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis showed the signature and each lncRNA were mainly enriched in pathways associated with regulation of immune response. Several kinds of tumor-infiltrating immune cells like regulatory T cells, T follicular helper cells, CD8+ T cells, resting mast cells, and naïve B cells were significantly correlated with the signature. Conclusion. Therefore, we constructed a five-lncRNA model integrating clinical parameters to help predict the prognosis of ccRCC patients. The five immune-related lncRNAs could potentially be therapeutic targets for immunotherapy in ccRCC in the future.

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