Body Image in BRCA-Positive Young Women Following Bilateral Risk-Reducing Mastectomy: A Review of the Literature

Background/Significance: The presence of a breast cancer (BRCA) gene mutation increases a woman’s lifetime risk of developing breast cancer. Bilateral risk-reducing mastectomy is a proactive treatment option which lowers that risk. However, breast removal can create a change in physical appearance. It is unclear if BRCA-positive women undergoing this surgery in young adulthood, a life stage where intimate relationships, families, and careers are being established, have the same experience with body image as women in later stages of life.Purpose: The aim of this literature review is to assess how bilateral risk-reducing mastectomy impacts body image in young BRCA-positive women less than 40 years of age, with no history of breast cancer.Methods: Database searches were performed, yielding 402 results. Studies were considered if participants had an increased lifetime breast cancer risk/BRCA-positive diagnosis and history of bilateral risk-reducing mastectomy, body image was evaluated, and mean age was less than 40 years. A total of three qualitative studies and three quantitative studies were identified as relevant for this review.Results: A dearth of information exists on body image in young women following bilateral risk-reducing mastectomy. It was found in this review that some women experienced a decline in body image following surgery, while in others body image was maintained or improved.Conclusion: Understanding factors that impact body image following this risk-reducing surgery will allow clinicians to support this unique population. Open and informative discussion should be encouraged with young women considering, or who have undergone, bilateral risk-reducing mastectomy.

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.