[Prediction of platinum-based chemotherapy sensitivity for epithelial ovarian cancer by multi-sequence MRI-based...

Objective: To investigate a preoperative multi-sequence MRI-based radiomic nomogram for prediction of platinum-based chemotherapy sensitivity in patients with epithelial ovarian cancer (EOC). Methods: The complete data of 114 patients with EOC confirmed by surgery and pathology in Nantong Tumor Hospital of Nantong University from January 2015 to May 2020 were retrospectively analyzed, with an average age of 32-76 (57±8) years. All patients underwent platinum-based chemotherapy after maximal cytoreductive surgery. According to whether relapse occurred within 6 months, those patients were divided into platinum-resistant disease (PR, n=39) group and platinum-sensitive disease group (PS, n=75).All patients underwent MRI examination before treatment, and the 3-dimensional solid component of the tumor area of interest (ROI) on T2-weighted image (T₂WI), diffusion weighted imaging (DWI) and T₁-weighted image-enhanced image (T₁CE) were manually delineated using Itk-snap software.Then AK software was imported for radiomics features extracting. They were randomly divided into training group (n=80) and validation group (n=34) in a ratio of 7∶3 (stratified sampling method). Firstly, the radiomics features were initially screened by the method of maximum correlation and minimum redundancy (mRMR), and features with the greatest predictive power were retained. Then, the LASSO regression analysis was performed to select the best features and construct the radiomics model. Univariate analysis was used to screen out clinical relevant factors, which combined with radiomic score (Radscore) was applied to develop a radiomics nomogram by multivariable logistic regression. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the predictive ability and clinical application value of radiomics model, clinical related factor model and radiomics nomogram. Results: Compared with the radiomics model (12 optimal radiomics features) and the clinical relevant factors model (residual disease, neutrophil count, carbohydrate antigen 199), the radiomics nomogram model demonstrated the best prediction performance: in the training groups, the AUC (Area Under the ROC Curve), accuracy, sensitivity, and specificity were 0.90 (95%CI:0.82-0.99), 90.0%, 89.0%, and 92.0%, respectively. In the validation groups, the AUC, accuracy, sensitivity, and specificity were 0.89 (95%CI:0.78-1.00), 85.0%, 87.0%, and 80.0%, respectively. DCA shows that the use of nomograms with a threshold in the range of 0.01 to 0.90 has a greater clinical application value in predicting the sensitivity of platinum chemotherapy in patients with EOC. Conclusion: The multi-sequence MRI-based radiomics nomogram has a high diagnostic value in predicting the sensitivity of platinum-based chemotherapy in patients with EOC.