scholarly journals A Novel Radiogenomics Biomarker Based on Hypoxic-Gene Subset: Accurate Survival and Prognostic Prediction of Renal Clear Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiahao Gao ◽  
Fangdie Ye ◽  
Fang Han ◽  
Xiaoshuang Wang ◽  
Haowen Jiang ◽  
...  
Keyword(s):  
High Risk ◽  
Cell Carcinoma ◽  
Validation Cohort ◽  
Clear Cell ◽  
Gene Subset ◽  
Independent Validation ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Prognostic Prediction

PurposeTo construct a novel radiogenomics biomarker based on hypoxic-gene subset for the accurate prognostic prediction of clear cell renal cell carcinoma (ccRCC).Materials and MethodsInitially, we screened for the desired hypoxic-gene subset by analysis using the GSEA database. Through univariate and multivariate cox regression hazard ratio analysis, survival-related hypoxia genes were identified, and a genomics signature was constructed in the TCGA database. Building on this, a hypoxia-gene related radiogenomics biomarker (prediction of hypoxia-genes signature by contrast-enhanced CT radiomics) was constructed in the TCIA-KIRC database by extracting features in the venous phase of contrast-enhanced CT images, selecting features using the mRMR and LASSO algorithms, and building logistic regression models. Finally, we validated the prognostic capability of the new biomarker for patients with ccRCC in an independent validation cohort at Huashan Hospital of Fudan University, Shanghai, China.ResultsThe hypoxia-related genomics signature consisting of five genes (IFT57, PABPN1, RNF10, RNF19B and UBE2T) was shown to be significantly associated with survival for patients with ccRCC in the TCGA database, delineated by grouping of the signature expression as either low- or high-risk. In the TCIA database, we constructed a radiogenomics biomarker consisting of 13 radiomics features that were optimal predictors of hypoxia-gene signature expression levels (low- or high-risk) in patients at each institution, that demonstrated AUC values of 0.91 and 0.91 in the training and validation groups, respectively. In the independent validation cohort at Huashan Hospital, our radiogenomics biomarker was significantly associated with prognosis in patients with ccRCC (p=0.0059).ConclusionsThe novel prognostic radiogenomics biomarker that was constructed achieved excellent correlation with prognosis in both the cohort of TCGA/TCIA-KIRC database and the independent validation cohort of Huashan hospital patients with ccRCC. It is anticipated that this work may assist in clinical preferential treatment decisions and promote the process of precision theranostics in the future.

scholarly journals Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma

2019 ◽  
Vol 18 ◽  
pp. 153601211988316 ◽  
Author(s):  
Guangjie Yang ◽  
Aidi Gong ◽  
Pei Nie ◽  
Lei Yan ◽  
Wenjie Miao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Texture Analysis ◽  
Renal Cell ◽  
Texture Features ◽  
Regions Of Interest ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
2D And 3D

Objective: To evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from chromophobe renal cell carcinoma (chRCC). Methods: We retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture features were extracted from 2D and 3D regions of interest in triphasic CT images. The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models was evaluated with respect to calibration, discrimination, and clinical usefulness. Results: Of the 177 and 183 texture features extracted from 2D and 3D regions of interest, respectively, 5 2D features and 8 3D features were selected to build 2D and 3D CTTA models. The 2D CTTA model (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.695-0.927) and the 3D CTTA model (AUC, 0.915; 95% CI, 0.838-0.993) showed good discrimination and calibration ( P > .05). There was no significant difference in AUC between the 2 models ( P = .093). Decision curve analysis showed the 3D model outperformed the 2D model in terms of clinical usefulness. Conclusions: The CTTA models based on contrast-enhanced CT images had a high value in differentiating fpAML from chRCC.

Imaging of Solid Kidney Tumours in Children

1995 ◽  
Vol 36 (3) ◽  
pp. 254-260 ◽  
Author(s):  
C. Hugosson ◽  
R. Nyman ◽  
B. Jacobsson ◽  
H. Jorulf ◽  
K. Sackey ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Clear Cell ◽  
Wilms Tumour ◽  
Rhabdoid Tumour ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Renal Tumours ◽  
Enhanced Ct ◽  
Mr Characteristics

Eighteen children aged 6 months to 12 years with 20 solid renal tumours; 13 Wilms' tumours (WT), 2 clear cell sarcomas of the kidney, 1 malignant rhabdoid tumour of the kidney and 2 cases of bilateral nephroblastomatosis with Wilms' tumour underwent evaluation with US, CT and MR imaging. Contrast-enhanced CT and non-enhanced MR were equally accurate in determining the size and origin of the tumour but were unreliable in separation of stages I, II and III. US could only accurately assess the size of the tumours. MR characteristics varied somewhat between WTs and non-WTs but contrast-enhanced MR imaging might be useful for separation of WTs from nephroblastomatosis.

Predictive model for microvascular invasion of hepatocellular carcinoma among candidates for either hepatectomy or liver transplantation.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 4079-4079
Author(s):  
Hidetoshi Nitta ◽  
Marc Antoine Allard ◽  
Mylene Sebagh ◽  
Gabriella Pittau ◽  
Oriana Ciacio ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Predictive Model ◽  
Validation Cohort ◽  
Microvascular Invasion ◽  
Roc Curve Analysis ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

4079 Background: Microvascular invasion (MVI) is the strongest prognostic factor following surgery of hepatocellular carcinoma (HCC). However, it is usually not available on the preoperative setting. A predictive model of MVI in patients scheduled for hepatic resection (HR) or liver transplantation (LT) would thus help guiding treatment strategy. The aim of this study was to develop a predictive model for MVI of HCC before either HR or LT. Methods: HCC patients who consecutively performed HR or LT from January 1994 to June 2016 at a single institution were subdivided into a training and validation cohort. Risk factors for MVI in the training cohort were used to develop a predictive model for MVI, to be validated in the validation cohort. The outcomes of the HR and LT patients with high or low MVI probability based on the model, were compared using propensity score matching (PSM). Cut-off values for continuous factors were determined based on ROC curve analysis. Results: A total of 910 patients (425 HR, 485 LT) were included in the training (n = 637) and validation (n = 273) cohorts. In the training cohort, multivariate analysis demonstrated that alpha-fetoprotein ≥100ng/ml ( p < 0.0001), largest tumor size ≥40mm ( p = 0.0002), non-boundary HCC type on contrast-enhanced CT ( p = 0.001), neutrophils-to-lymphocytes ratio ≥3.2 ( p = 0.002), aspartate aminotransferase ≥62U/l ( p = 0.02) were independently associated with MVI. Combinations of these 5 factors varied the MVI probability from 15.5% to 91.1%. This predictive model achieved a good c-index of 0.76 in the validation cohort. In PSM (109 HR, 109 LT), there was no difference in survival between HR and LT patients among the high MVI probability (≥50%) patients, (5y-OS; 46.3% vs 42.2%, p = 0.77, 5y-RFS; 54.0% vs 28.8%, p = 0.21). Among the low probability ( < 50%), survival was significantly decreased following HR compared with LT (5y-OS; 54.1% vs 78.8%, p = 0.007, 5y-RFS; 17.3% vs 86.1%, p< 0.0001). Conclusions: This model developed from preoperative data allows reliable prediction of MVI, and may thus help with preoperative decisions about the suitability of HR or LT in patients with HCC.

scholarly journals Contrast-Enhanced CT Density Predicts Response to Sunitinib Therapy in Metastatic Renal Cell Carcinoma Patients

2017 ◽  
Vol 10 (4) ◽  
pp. 679-685 ◽  
Author(s):  
Simon Matoori ◽  
Yeeliang Thian ◽  
Dow-Mu Koh ◽  
Aslam Sohaib ◽  
James Larkin ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Ct Density

18 F-FDG PET/contrast enhanced CT in the standard surveillance of high risk colorectal cancer patients

2014 ◽  
Vol 83 (12) ◽  
pp. 2224-2230 ◽  
Author(s):  
Germán Andrés Jiménez Londoño ◽  
Ana María García Vicente ◽  
Victoria Sánchez Pérez ◽  
Fátima Jiménez Aragón ◽  
Alberto León Martin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Fdg Pet ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Colorectal Cancer Patients
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