Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective.
Objectives: To develop a radiomics model with all-relevant imaging features from multiphasic computed tomography (CT) for differentiating clear cell renal cell carcinoma (ccRCC) from non-ccRCC and to investigate the possible radiogenomics link between the imaging features and a key ccRCC driver gene...
| Publicado en: | European Radiology Vol. 29; no. 8; pp. 3996 - 4008 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137304101&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137304101 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Aug2019 vid: 29 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137304101 137304101 NLM30523454 137304101 10.1007/s00330-018-5872-6 NLM30523454 137304101 ppf: 3996 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective. aug: au: Li, Zhi-Cheng Zhai, Guangtao Zhang, Jinheng Wang, Zhongqiu Liu, Guiqin Wu, Guang-yu Liang, Dong Zheng, Hairong affil: Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China sug: subj: Multidetector Computed Tomography Methods Proteins Kidney Neoplasms Diagnosis Carcinoma, Renal Cell Diagnosis Neoplasm Staging Methods Mutation DNA Sequence Analysis Middle Age Young Adult Aged, 80 and Over Male Carcinoma, Renal Cell Metabolism Diagnosis, Differential ROC Curve Human Adult Carcinoma, Renal Cell Kidney Neoplasms Metabolism Cell Differentiation Kidney Neoplasms Female Proteins Metabolism Aged Retrospective Design Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Funding Source Middle Aged: 45-64 years Aged, 80 & over Adult: 19-44 years Aged: 65+ years Male Female ab: Objectives: To develop a radiomics model with all-relevant imaging features from multiphasic computed tomography (CT) for differentiating clear cell renal cell carcinoma (ccRCC) from non-ccRCC and to investigate the possible radiogenomics link between the imaging features and a key ccRCC driver gene-the von Hippel-Lindau (VHL) gene mutation.Methods: In this retrospective two-center study, two radiomics models were built using random forest from a training cohort (170 patients), where one model was built with all-relevant features and the other with minimum redundancy maximum relevance (mRMR) features. A model combining all-relevant features and clinical factors (sex, age) was also built. The radiogenomics association between selected features and VHL mutation was investigated by Wilcoxon rank-sum test. All models were tested on an independent validation cohort (85 patients) with ROC curves analysis.Results: The model with eight all-relevant features from corticomedullary phase CT achieved an AUC of 0.949 and an accuracy of 92.9% in the validation cohort, which significantly outperformed the model with eight mRMR features (seven from nephrographic phase and one from corticomedullary phase) with an AUC of 0.851 and an accuracy of 81.2%. Combining age and sex did not benefit the performance. Five out of eight all-relevant features were significantly associated with VHL mutation, while all eight mRMR features were significantly associated with VHL mutation (false discovery rate-adjusted p < 0.05).Conclusions: All-relevant features in corticomedullary phase CT can be used to differentiate ccRCC from non-ccRCC. Most subtype-discriminative imaging features were found to be significantly associated with VHL mutation, which may underlie the molecular basis of the radiomics features.Key Points: • All-relevant features in corticomedullary phase CT can be used to differentiate ccRCC from non-ccRCC with high accuracy. • Most RCC-subtype-discriminative CT features were associated with the key RCC-driven gene-the VHL gene mutation. • Radiomics model can be more accurate and interpretable when the imaging features could reflect underlying molecular basis of RCC. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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