Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings

Zhan, Feng; Zhang, Lixia; Zhong, Qian; Shen, Qijun; Hu, Zhengyu; Chen, Feng

doi:10.3389/fonc.2020.00279

Cited by 35 publications

(26 citation statements)

References 38 publications

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“…Recently, several studies of immense value in exploring the biological progress of KIRC via the construction of radiomic models by CT images have been published. Zhan Feng and Burak Kocak B et al, respectively proved that CT radiomic has the potential to predict BRCA1associated protein 1 (BAP1) mutation status in KIRC patients (34,35). Payel Ghosh et al provided a radiomic-genetics pipeline that can extract 3D intra-tumor heterogeneity features from CECT images and explore associations between features and gene mutation status (36).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

et al. 2021

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PurposeThe present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC).MethodsA total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer–Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan–Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis.ResultsA total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation.ConclusionsThe nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

et al. 2021

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“…In ccRCC, computed tomography (CT) is routinely used to capture the physical characteristics of the whole tumor volume. Radiomics features extracted from CT scans have showed significant ability to predict mutation status of VHL , BAP1 and PBRM1 in ccRCC [ 24 , 25 ]. To our knowledge, no study has applied CT image features to identify molecular subtypes, and performed multi-omics analysis to predict prognosis of ccRCC patients.…”

Section: Introductionmentioning

confidence: 99%

Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma

Zeng

Chen

Wang

et al. 2021

Aging

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Objectives: To assess the feasibility of predicting molecular characteristics by computed tomography (CT) radiomics features, and predicting overall survival (OS) using combination of omics data in clear cell renal cell carcinoma (ccRCC). Methods: Genetic data of 207 ccRCC patients was retrieved from The Cancer Genome Atlas (TCGA) and matched contrast-enhanced CT images were obtained from The Cancer Imaging Archive (TCIA). Another cohort of 175 ccRCC patients from West China Hospital was used as external validation. We first applied radiomics features and machine learning algorithms to predict genetic mutations and mRNA-based molecular subtypes. Next, we established predictive models for OS based on single omics, combined omics (radiomics+genomics, radiomics+transcriptomics, radiomics+proteomics) and all features (multi-omics). Results: Using radiomics features, random forest algorithm showed good capacity to identify the mutations VHL (AUC=0.971), BAP1 (AUC=0.955), PBRM1 (AUC=0.972), SETD2 (AUC=0.949), and molecular subtypes m1 (AUC=0.973), m2 (AUC=0.968), m3 (AUC=0.961), m4 (AUC=0.953). The TCGA cohort was divided into training (n=104) and validation (n=103) sets. The radiomics model had promising prognostic value for OS in validation set (5-year AUC=0.775) and external validation set (5-year AUC=0.755). In the validation set, the radiomics+omics models enhanced predictive accuracy than single-omics models, and the multi-omics model made further improvement (5-year AUC=0.846). High-risk group of validation set predicted by multi-omics model showed significantly poorer OS (HR=6.20, 95%CI: 3.19-8.44, p<0.0001). Conclusions: CT radiomics might be a feasible approach to predict genetic mutations, molecular subtypes and OS in ccRCC patients. Integrative analysis of radiogenomics may improve the survival prediction of ccRCC patients.

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“…To our limited knowledge, no study has been conducted to predict PD-L1 and PD-L1-TILs expressions in GC based on CT radiomics. Moreover, there is a substantial interest in the use of machine learning algorithms for selecting optimal radiomic features from medical images and applying them to tumor evaluation, as well as in the improvement of diagnostic e cacy [20,21]. Therefore, we sought to explore the capability of the signatures based on CT radiomics complement morphological characteristics to predict PD-L1 and PD-L1-TILs status in GC.…”

Section: Introductionmentioning

confidence: 99%

CT radiomics and morphological characteristics for predicting PD-L1 expression on tumor cells and tumor infiltrating lymphocytes in gastric cancer

Qiao

Liu

et al. 2021

Preprint

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Purpose To explore CT radiomics and morphological characteristics for predicting programmed cell death ligand 1 on tumor cells (PD-L1) and tumor infiltrating lymphocytes (PD-L1-TILs) status in gastric cancer (GC).Methods From March 2019 to October 2019, 101 patients identified with GC who underwent surgery at our hospital were enrolled in this study retrospectively. Radiomic features were extracted from regions of interest manually drawn on venous CT images. Besides, 4 morphological characteristics were evaluated. The signatures based on radiomics and morphological characteristics were built using multiple classifiers (Support Vector Machine [SVM], Naive Bayes [NB], Decision Trees, and Random Forest). Receiver operating characteristic (ROC) curve was performed to assess diagnostic efficiency.Results The adjacent adipose tissue (p=0.009) and numerous radiomic features (all p<0.05) differed significantly between GCs with different PD-L1 status. Six radiomic features showed significant differences between different PD-L1-TILs status (all p<0.05). The highest areas under the ROC curves (AUCs) of signatures generated by classifiers were 0.807 (SVM) and 0.729 (NB) for the prediction of PD-L1 and PD-L1-TILs status, respectively.Conclusion It was promising to predict PD-L1 status in GCs noninvasively using CT radiomics combined with morphological characteristics. It might help to improve clinical decision making with regard to immunotherapy. However, the prediction for PD-L1-TILs needs to be explored further.

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Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings

Cited by 35 publications

References 38 publications

Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma

CT radiomics and morphological characteristics for predicting PD-L1 expression on tumor cells and tumor infiltrating lymphocytes in gastric cancer

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