Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images

Background The objective of this study is to explore the value of two-dimensional (2D) and three-dimensional (3D) radiomics models based on enhanced computed tomography (CT) images in predicting BRCA gene mutations in patients with epithelial ovarian cancer. Methods The clinical and imaging data of 106 patients with ovarian cancer confirmed by surgery and pathology were retrospectively analyzed and genetic testing was performed. Radiomics features extracted from the 2D and 3D regions of interest of the patients’ primary tumor lesions were selected in the training set using the maximum correlation and minimum redundancy method. Then, the best features were selected through Lasso tenfold cross-validation. Feature subsets were employed to establish a radiomics model. The model’s performance was evaluated via area under the receiver operating characteristic curve analysis and its clinical validity was assessed by using the model’s decision curve. Results On the validation set, the area under the curve values of the 2D, 3D, and 2D + 3D combined models were 0.78 (0.61–0.96), 0.75 (0.55–0.92), and 0.82 (0.61–0.96), respectively. However, the DeLong test P values between the three pairs of models were all > 0.05. The decision curve analysis showed that the radiomics model had a high net benefit across all high-risk threshold probabilities. Conclusions The three radiomics models can predict the BRCA gene mutation in ovarian cancer, and there were no statistically significant differences between the prediction performance of the three models.

Prediction of BRCA Gene Mutation Status in Epithelial Ovarian Cancer by Radiomics Models based on 2D and 3D CT Images

Background The objective of this study is to explore the value of two-dimensional (2D) and three-dimensional (3D) radiomics models based on enhanced computed tomography(CT) images in predicting BRCA gene mutations in patients with epithelial ovarian cancer. Methods A retrospective analysis of the clinical and imaging data of 122 patients with ovarian cancer confirmed by surgery and pathology and on which genetic testing was performed. Radiomics features were extracted from the 2D and 3D regions of interest of the patients’ primary tumor lesions, and features were selected in the training set using the maximum correlation and minimum redundancy method. Then, the best features were selected through Lasso 10-fold cross-validation. Feature subsets were used to establish a radiomics model. We used area under the receiver operating characteristic curve analysis to evaluate the model’s performance and then used the model’s decision curve to evaluate its clinical validity. Results On the validation set, the area under the curve values of the 2D, 3D, and 2D+3D combined models was 0.78 (0.61–0.96), 0.75 (0.55–0.92), and 0.82 (0.61–0.96), respectively. However, the DeLong test P values between the three pairs of models were all >0.05. The decision curve analysis showed that the radiomics model had a high net benefit across all high-risk threshold probabilities. Conclusions The three radiomics models can predict the BRCA gene mutation in ovarian cancer, and there were no statistically significant differences between the three models’ prediction performance.

Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer

PurposeTo evaluate the predictive value of routine CT features combined with 3D texture analysis for prediction of BRCA gene mutation status in advanced epithelial ovarian cancer.MethodRetrospective analysis was performed on patients with masses occupying the pelvic space confirmed by pathology and complete preoperative images in our hospital, including 37 and 58 cases with mutant type and wild type BRCA, respectively (total: 95 cases). The enrolled patients’ routine CT features were analyzed by two radiologists. Then, ROIs were jointly determined through negotiation, and the ITK-SNAP software package was used for 3D outlining of the third-stage images of the primary tumor lesions and obtaining texture features. For routine CT features and texture features, Mann-Whitney U tests, single-factor logistic regression analysis, minimum redundancy, and maximum correlation were used for feature screening, and the performance of individual features was evaluated by ROC curves. Multivariate logistic regression analysis was used to further screen features, find independent predictors, and establish the prediction model. The established model’s diagnostic efficiency was evaluated by ROC curve analysis, and the histogram was obtained to conduct visual analysis of the prediction model.ResultsAmong the routine CT features, the type of peritoneal metastasis, mesenteric involvement, and supradiaphragmatic lymph node enlargement were correlated with BRCA gene mutation (P < 0.05), whereas the location of the peritoneal metastasis (in the gastrohepatic ligament) was not significantly correlated with BRCA gene mutation (P > 0.05). Multivariate logistic regression analysis retained six features, including one routine CT feature and five texture features. Among them, the type of peritoneal metastasis was used as an independent predictor (P < 0.05), which had the highest diagnostic efficiency. Its AUC, accuracy, specificity, and sensitivity were 0.74, 0.79, 0.90, and 0.62, respectively. The prediction model based on the combination of routine CT features and texture features had an AUC of 0.86 (95% CI: 0.79–0.94) and accuracy, specificity, and sensitivity of 0.80, 0.76, and 0.81, respectively, indicating a better performance than that of any single feature.ConclusionsBoth routine CT features and texture features had value for predicting the mutation state of the BRCA gene, but their predictive efficiency was low. When the two types of features were combined to establish a predictive model, the model’s predictive efficiency was significantly higher than that of independent features.

Study of the Genetic Variants in BRCA1/2 and Non-BRCA Genes in a Population-Based Cohort of 2155 Breast/Ovary Cancer Patients, Including 443 Triple-Negative Breast Cancer Patients, in Argentina

Gene/s sequencing in hereditary breast/ovary cancer (HBOC) in routine diagnosis is challenged by the analysis of panels. We aim to report a retrospective analysis of BRCA1/2 and non-BRCA gene sequencing in patients with breast/ovary cancer (BOC), including triple-negative breast cancer (TNBC), in our population. In total 2155 BOC patients (1900 analyzed in BRCA1/2 and 255 by multigenic panels) gave 372 (17.2.6%) and 107 (24.1%) likely pathogenic/pathogenic variants (LPVs/PVs), including BRCA and non-BRCA genes, for the total and TNBC patients, respectively. When BOC was present in the same proband, a 51.3% rate was found for LPVs/PVs in BRCA1/2. Most of the LPVs/PVs in the panels were in BRCA1/2; non-BRCA gene LPVs/PVs were in CDH1, CHEK2, CDKN2A, MUTYH, NBN, RAD51D, and TP53. TNBC is associated with BRCA1/2 at a higher rate than the rest of the breast cancer types. The more prevalent PVs in BRCA1/2 genes (mostly in BRCA1) do not rule out the importance to panels of genes, although they are certainly far from shedding light on the gap of the 85% predicted linkage association of BOC with BRCA1/2 and are still not elucidated.

An overview on breast cancer genetics and recent innovations: Literature survey

Breast cancer is one of the leading cancers nowadays. The genetical mechanism behind breast cancer development is an intricate one. In this review, the genetical background of breast cancer, particularly BRCA 1 and BRCA 2 had been included. Moreover, to summarize the genetics of breast cancer, the recent and ongoing preclinical and clinical studies on the treatment of BRCA-associated breast cancer had also been included. A prime knowledge is that the BRCA gene is the basis of breast cancer risk. How it mediates cell proliferation and associated mechanisms are reviewed here. BRCA 1 gene can influence all phases of the cell cycle and regulate cell cycle progression. BRCA 1 gene can also respond to DNA damages and induce responsive mechanisms. The action of the BRCA gene on associated protein has a wide consideration in breast cancer development. Heterogeneity in breast cancer makes them a fascinating and challenging stream to diagnose and treat. Several clinical therapies are available for breast cancer treatments. Chemotherapy, endocrine therapy, radiation therapy and immunotherapy are the milestones in the cancer treatments. Ral binding protein 1 is a promising target for breast cancer treatment and the platinum-based chemotherapies are the other remarkable fields. In immunotherapy, the usage of anti-programmed death (PD)-1 antibody is a new class of cancer immunotherapy that hinders immune effecter inhibition and potentially expanding preexisting anticancer immune responses. Breast cancer genetics and treatment strategies are crucial in escalating survival rates.