Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings.
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ABSTRACT:
SUBMITTER: Feng Z
PROVIDER: S-EPMC7058626 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings.
Feng Zhan Z Zhang Lixia L Qi Zhong Z Shen Qijun Q Hu Zhengyu Z Chen Feng F
Frontiers in oncology 20200228
To evaluate the potential application of computed tomography (CT) radiomics in the prediction of BRCA1-associated protein 1 (<i>BAP1</i>) mutation status in patients with clear-cell renal cell carcinoma (ccRCC). In this retrospective study, clinical and CT imaging data of 54 patients were retrieved from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma database. Among these, 45 patients had wild-type <i>BAP1</i> and nine patients had <i>BAP1</i> mutation. The texture features of tumor im ...[more]
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Project description:PurposeTo develop and validate the radiomics nomogram that combines clinical factors and radiomics features to estimate overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC), and assess the incremental value of radiomics for OS estimation.Materials and methodsOne hundred ninety-four ccRCC cases were included in the training cohort and 188 ccRCC patients from another hospital as the test cohort. Three-dimensional region-of-interest segmentation was manually segmented on multiphasic contrast-enhanced abdominal CT images. Radiomics score (Rad-score) was calculated from a formula generated via least absolute shrinkage and selection operator (LASSO) Cox regression, after which the association between the Rad-score and OS was explored. The radiomics nomogram (clinical factors + Rad-score) was developed to demonstrate the incremental value of the Rad-score to the clinical nomogram for individualized OS estimation, which was then evaluated in relation to calibration and discrimination.ResultsRad-score, calculated using a linear combination of the 11 screened features multiplied by their respective LASSO Cox coefficients, was significantly associated with OS. Calibration curves showed good agreement between the OS predicted by the nomograms and observed outcomes. The radiomics nomogram presented higher discrimination capability compared to clinical nomogram in the training (C-index: 0.884; 95% CI: 0.808-0.940 vs. 0.803; 95% CI: 0.705-0.899, P < 0.05) and test cohorts (C-index: 0.859; 95% CI: 0.800-0.921 vs. 0.846; 95% CI: 0.777-0.915, P < 0.05).ConclusionsThe radiomics nomogram may be used for predicting OS in patients with ccRCC, and radiomics is useful to assist quantitative and personalized treatment.
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Project description:OBJECTIVE:The presence of coagulative necrosis (CN) in clear cell renal cell carcinoma (ccRCC) indicates a poor prognosis, while the absence of CN indicates a good prognosis. The purpose of this study was to build and validate a radiomics signature based on preoperative CT imaging data to estimate CN status in ccRCC. MATERIALS AND METHODS:Altogether, 105 patients with pathologically confirmed ccRCC were retrospectively enrolled in this study and then divided into training (n = 72) and validation (n = 33) sets. Thereafter, 385 radiomics features were extracted from the three-dimensional volumes of interest of each tumor, and 10 traditional features were assessed by two experienced radiologists using triple-phase CT-enhanced images. A multivariate logistic regression algorithm was used to build the radiomics score and traditional predictors in the training set, and their performance was assessed and then tested in the validation set. The radiomics signature to distinguish CN status was then developed by incorporating the radiomics score and the selected traditional predictors. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance. RESULTS:The area under the ROC curve (AUC) of the radiomics score, which consisted of 7 radiomics features, was 0.855 in the training set and 0.885 in the validation set. The AUC of the traditional predictor, which consisted of 2 traditional features, was 0.843 in the training set and 0.858 in the validation set. The radiomics signature showed the best performance with an AUC of 0.942 in the training set, which was then confirmed with an AUC of 0.969 in the validation set. CONCLUSION:The CT-based radiomics signature that incorporated radiomics and traditional features has the potential to be used as a non-invasive tool for preoperative prediction of CN in ccRCC.
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Project description:This study was aimed at building a computed tomography- (CT-) based radiomics approach for the differentiation of sarcomatoid renal cell carcinoma (SRCC) and clear cell renal cell carcinoma (CCRCC). It involved 29 SRCC and 99 CCRCC patient cases, and to each case, 1029 features were collected from each of the corticomedullary phase (CMP) and nephrographic phase (NP) image. Then, features were selected by using the least absolute shrinkage and selection operator regression method and the selected features of the two phases were explored to build three radiomics approaches for SRCC and CCRCC classification. Meanwhile, subjective CT findings were filtered by univariate analysis to construct a radiomics model and further selected by Akaike information criterion for integrating with the selected image features to build the fifth model. Finally, the radiomics models utilized the multivariate logistic regression method for classification and the performance was assessed with receiver operating characteristic curve (ROC) and DeLong test. The radiomics models based on the CMP, the NP, the CMP and NP, the subjective findings, and the combined features achieved the AUC (area under the curve) value of 0.772, 0.938, 0.966, 0.792, and 0.974, respectively. Significant difference was found in AUC values between each of the CMP radiomics model (0.0001 ≤ p ≤ 0.0051) and the subjective findings model (0.0006 ≤ p ≤ 0.0079) and each of the NP radiomics model, the CMP and NP radiomics model, and the combined model. Sarcomatoid change is a common pathway of dedifferentiation likely occurring in all subtypes of renal cell carcinoma, and the CT-based radiomics approaches in this study show the potential for SRCC from CCRCC differentiation.
| S-EPMC7397414 | biostudies-literature
Project description:IntroductionThis study aimed to construct a radiomics-based machine learning (ML) model for differentiation between non-clear cell and clear cell renal cell carcinomas (ccRCC) that is robust against institutional imaging protocols and scanners.Materials and methodsPreoperative unenhanced (UN), corticomedullary (CM), and excretory (EX) phase CT scans from 209 patients diagnosed with RCCs were retrospectively collected. After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. For the ML analysis, the cases were randomly split into training and test sets with a 3:1 ratio. Highly correlated RFs were filtered out based on Pearson's correlation coefficient (r > 0.95). Intraclass correlation coefficient analysis was used to select RFs with excellent reproducibility (ICC ≥ 0.90). The most predictive RFs were selected by the least absolute shrinkage and selection operator (LASSO). A support vector machine algorithm-based binary classifier (SVC) was constructed to predict tumor types and its performance was evaluated based-on receiver operating characteristic curve (ROC) analysis. The "Kidney Tumor Segmentation 2019" (KiTS19) publicly available dataset was used during external validation of the model. The performance of the SVC was also compared with an expert radiologist's.ResultsThe training set consisted of 121 ccRCCs and 38 non-ccRCCs, while the independent internal test set contained 40 ccRCCs and 13 non-ccRCCs. For external validation, 50 ccRCCs and 23 non-ccRCCs were identified from the KiTS19 dataset with the available UN, CM, and EX phase CTs. After filtering out the highly correlated and poorly reproducible features, the LASSO algorithm selected 10 CM phase RFs that were then used for model construction. During external validation, the SVC achieved an area under the ROC curve (AUC) value, accuracy, sensitivity, and specificity of 0.83, 0.78, 0.80, and 0.74, respectively. UN and/or EX phase RFs did not further increase the model's performance. Meanwhile, in the same comparison, the expert radiologist achieved similar performance with an AUC of 0.77, an accuracy of 0.79, a sensitivity of 0.84, and a specificity of 0.69.ConclusionRadiomics analysis of CM phase CT scans combined with ML can achieve comparable performance with an expert radiologist in differentiating ccRCCs from non-ccRCCs.
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Project description:PURPOSE:To investigate associations between computed tomographic (CT) features of clear cell renal cell carcinoma (RCC) and mutations in VHL, PBRM1, SETD2, KDM5C, or BAP1 genes. MATERIALS AND METHODS:The institutional review board approved this retrospective, hypothesis-generating study of 233 patients with clear cell RCC and waived the informed consent requirement. The study was HIPAA compliant. Three radiologists independently reviewed pretreatment CT images of all clear cell RCCs without knowledge of their genomic profile. One radiologist measured largest diameter and enhancement parameters of each clear cell RCC. Associations between CT features and mutations in VHL, PBRM1, SETD2, KDM5C, and BAP1 genes were tested by using the Fisher exact test. Associations between mutations and size and enhancement were assessed by using the independent t test. Interreader agreement was calculated by using the Fleiss ?. RESULTS:Mutation frequencies among clear cell RCCs were as follows: VHL, 53.2% (124 of 233); PBRM1, 28.8% (67 of 233); SETD2, 7.3% (17 of 233); KDM5C, 6.9% (16 of 233); and BAP1, 6.0% (14 of 233). Mutations of VHL were significantly associated with well-defined tumor margins (P = .013), nodular tumor enhancement (P = .021), and gross appearance of intratumoral vascularity (P = .018). Mutations of KDM5C and BAP1 were significantly associated with evidence of renal vein invasion (P = .022 and .046, respectively). The genotype of solid clear cell RCC differed significantly from the genotype of multicystic clear cell RCC. While mutations of SETD2, KDM5C, and BAP1 were absent in multicystic clear cell RCC, mutations of VHL (P = .016) and PBRM1 (P = .017) were significantly more common among solid clear cell RCC. Interreader agreement for CT feature assessments ranged from substantial to excellent (? = 0.791-0.912). CONCLUSION:This preliminary radiogenomics analysis of clear cell RCC revealed associations between CT features and underlying mutations that warrant further investigation and validation.
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Project description:PURPOSE:In clear cell renal cell carcinoma BAP1 and PBRM1 are 2 of the most commonly mutated genes (10% to 15% and 40% to 50%, respectively). We sought to determine the prognostic significance of PBRM1 and BAP1 expression in clear cell renal cell carcinoma. MATERIALS AND METHODS:We used immunohistochemistry to assess PBRM1 protein expression in 1,479 primary clear cell renal cell carcinoma tumors that were previously stained for BAP1. A centralized pathologist reviewed all cases and categorized tumors as positive or deficient for PBRM1 and BAP1. Kaplan-Meier and Cox regression models were used to evaluate association of PBRM1 and BAP1 expression with the risk of death from renal cell carcinoma and the risk of metastasis after adjustment for age and the Mayo Clinic SSIGN (stage, size, grade and necrosis) score. RESULTS:PBRM1 and BAP1 expression was PBRM1+ BAP1+ in 40.1% of tumors, PBRM1- BAP1+ in 48.6%, PBRM1+ BAP1- in 8.7% and PBRM1- BAP1- in 1.8%. The incidence of PBRM1 and BAP1 loss in the same tumor was significantly lower than expected (actual 1.8% vs expected 5.3%, p <0.0001). Compared to patients with PBRM1+ BAP1+ tumors those with PBRM1- BAP1+ lesions were more likely to die of renal cell carcinoma (HR 1.39, p = 0.035), followed by those with PBRM1+ BAP1- and PBRM1- BAP1- tumors (HR 3.25 and 5.2, respectively, each p <0.001). PBRM1 and BAP1 expression did not add independent prognostic information to the SSIGN score. CONCLUSIONS:PBRM1 and BAP1 expression identified 4 clinical subgroups of patients with clear cell renal cell carcinoma who had divergent clinical outcomes. The clinical value of these biomarkers will be fully realized when therapies targeting pathways downstream of PBRM1 and BAP1 are developed.
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Project description:PurposeDifferentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs.MethodsWe conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011-2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034).ResultsOur literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562-0.907] and 0.817 [95% CI: 0.663-0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814-0.978]and 0.926 [95% CI: 0.854-0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742-0.927] and 0.886 [95% CI: 0.819-0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan.ConclusionThis study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy.
| S-EPMC10374097 | biostudies-literature
Project description:ObjectivesThis study aims to establish predictive logistic models for the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grades of clear cell renal cell carcinoma (ccRCC) based on tumoral and peritumoral radiomics.MethodsA cohort of 370 patients with pathologically confirmed ccRCCs were included in this retrospective study between January 2014 and December 2020 according to the WHO/ISUP grading system. The volume of interests of triphasic computed tomography images were depicted manually using the "itk-SNAP" software, and the radiomics features were calculated. The cohort was segmented into the training cohort and validation cohort with a random proportion of 7:3. After extraction of radiomics features by analysis of variance (ANOVA) or Mann-Whitney U test, correlation analysis, and the least absolute shrinkage and selection operator (LASSO) method, the logistic models of tumoral radiomics (LR-tumor) and peritumoral radiomics (LR-peritumor) were developed. The LR-peritumor was subdivided into LR-peritumor-2mm, LR-peritumor-5mm, and LR-peritumor-10mm, and the LR-peritumor-2mm was subdivided into LR-peritumor-kid and LR-peritumor-fat based on the neighboring tissues of ccRCCs. Finally, an integrative model of tumoral and peritumoral radiomics (LR-tumor/peritumor) was built. The value of areas under the receiver operator characteristics curve (AUCs) was calculated to assess the efficacy of the models.ResultsThere were 209 low-grade and 161 high-grade ccRCCs enrolled. The AUCs of LR-tumor in CT images of venous phase were 0.802 in the training cohort and 0.796 in the validation cohort. The AUCs were higher in the LR-peritumor-2mm than those in LR-peritumor-5mm and LR-peritumor-10mm (training cohort: 0.788 vs. 0.788 and 0.759; validation cohort: 0.787 vs. 0.785 and 0.758). Moreover, the AUCs of LR-peritumor-fat were higher compared with those of LR-peritumor-kid. The LR-tumor/peritumor displayed the highest AUCs of 0.812 in the training cohort and 0.804 in the validation cohort.ConclusionsThe tumoral and peritumoral radiomics helped to predict the WHO/ISUP grades of ccRCCs. On the diagnostic performance of peritumoral radiomics, better results were seen for the LR-peritumor-2mm and LR-peritumor-fat.
| S-EPMC8884273 | biostudies-literature
Project description:The genetic cause of some familial nonsyndromic renal cell carcinomas (RCC) defined by at least two affected first-degree relatives is unknown. By combining whole-exome sequencing and tumor profiling in a family prone to cases of RCC, we identified a germline BAP1 mutation c.277A>G (p.Thr93Ala) as the probable genetic basis of RCC predisposition. This mutation segregated with all four RCC-affected relatives. Furthermore, BAP1 was found to be inactivated in RCC-affected individuals from this family. No BAP1 mutations were identified in 32 familial cases presenting with only RCC. We then screened for germline BAP1 deleterious mutations in familial aggregations of cancers within the spectrum of the recently described BAP1-associated tumor predisposition syndrome, including uveal melanoma, malignant pleural mesothelioma, and cutaneous melanoma. Among the 11 families that included individuals identified as carrying germline deleterious BAP1 mutations, 6 families presented with 9 RCC-affected individuals, demonstrating a significantly increased risk for RCC. This strongly argues that RCC belongs to the BAP1 syndrome and that BAP1 is a RCC-predisposition gene.
| S-EPMC3675229 | biostudies-literature
Project description:BackgroundRecurrent mutations in polybromo-1 (PBRM1, ~40%) and BRCA1-associated protein-1 (BAP1, ~10%) occur in clear cell renal cell carcinoma (ccRCC), but their prevalence in non-ccRCC or renal oncocytoma (RO) is unknown. We evaluated loss of PBRM1 and BAP1 staining in ccRCC, papillary RCC (pRCC), chromophobe RCC (chRCC), and RO tumors using an immunohistochemistry assay in which negative staining was associated with loss-of-function mutations.MethodsWe identified 458 patients treated surgically for ccRCC, pRCC, chRCC, and RO between 2004 and 2012. We performed immunohistochemistry assays to evaluate PBRM1 and BAP1 protein expression to classify tumors as PBRM1 or BAP1 negative. We compared loss of staining of these 2 proteins in ccRCC and non-ccRCC using the Fisher exact test.ResultsFor the total cohort of 458 patients, we successfully stained both PBRM1 and BAP1 in 408 tumor samples. Consistent with the mutation rate, loss of PBRM1 and BAP1 staining occurred in 43% (80/187) and 10% (18/187) of ccRCC cases, respectively. However, loss of PBRM1 staining occurred in only 3% (2/59), 6% (1/17), and 0% (0/34) of pRCC, chRCC, and RO tumors, respectively (P<0.0001). BAP1 loss was not observed in any of the pRCC (n = 61), chRCC (n = 17), or RO (n = 34) tumors, (P = 0.00021).ConclusionOur data suggest that biallelic inactivation of PBRM1 or BAP1 is less common in non-ccRCC when compared with ccRCC tumors. These findings suggest that loss of PBRM1 or BAP1 are key events in ccRCC, whereas other pathways may support tumorigenesis in non-ccRCC subtypes.
| S-EPMC4274200 | biostudies-literature