The best prognosis for advanced ovarian cancer is provided by no residual disease after primary cytoreductive surgery. It is thus important to be able to predict resectability that will result in complete cytoreduction, while avoiding unnecessary surgery that may leave residual disease. No single procedure appears to be sufficiently accurate and reliable to predict resectability. The process should include a preoperative workup based on clinical examination, biomarkers, especially tumor markers, and imaging, for which computed tomography, as well as sonography, magnetic resonance imaging and positron-emission tomography, can be used. This workup should provide sufficient information to determine whether complete cytoreduction is possible or if not, to propose neoadjuvant chemotherapy which is preferable in this case. For the remaining patients, laparoscopy is broadly recommended as an ultimate triage step. However, its modalities are still debated, and several scores have been proposed for standardization and improving accuracy. The risk of false negatives requires a final assessment of resectability as the first stage of cytoreductive surgery by laparotomy. Composite models, consisting of several criteria of workup and, sometimes, laparoscopy have been proposed to improve the accuracy of the predictive process. Regardless of the modality, the process appears to be accurate and reliable for predicting residual disease but less so for predicting complete cytoreduction and thus avoiding unnecessary surgery and an inappropriate treatment strategy. Overall, the proposed procedures are heterogeneous, sometimes unvalidated, or do not consider advances in surgery. Future techniques and/or models are still needed to improve the prediction of complete resectability.

An update on preoperative assessment of the resectability of advanced ovarian cancer

Purpose To 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. Method Retrospective 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. Results Among 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). 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. Conclusions Both 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.

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

Imaging of Ovarian Cancer: From Early Detection to Post-treatment Relapse

A prospective trial evaluating the ability of preoperative CT scan and serum CA-125 to predict suboptimal cytoreduction at primary debulking surgery for advanced ovarian, fallopian tube, and peritoneal cancer

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A prospective trial evaluating the ability of preoperative CT scan and serum CA-125 to predict suboptimal cytoreduction at primary debulking surgery for advanced ovarian, fallopian tube, and peritoneal cancer

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An update on preoperative assessment of the resectability of advanced ovarian cancer

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

Imaging of Ovarian Cancer: From Early Detection to Post-treatment Relapse