Background To explore and evaluate value a preoperative diagnosis model with contrast-enhanced computed tomography (CECT) imaging-based radiomics analysis in differentiating benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early-stage malignant ovarian tumors (eMOTs). Results The retrospective research was conducted with pathologically confirmed 258 ovarian tumors patients from January 2014 to February 2021. All patients underwent preoperative CECT examination. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). A summary of 4238 radiomic features were extracted per patient. By providing a 3D characterization of the regions of interest (ROI) with ITK SNAP software at the maximum level of enhanced CT image, radiomic features were extracted from the ROI with an in-house software written in Python. The Wilcoxon–Mann–Whitney (WMW) test, least absolute shrinkage and selection operator logistic regression (LASSO-LR) and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models for characterizing ovarian tumors taking the selected radiomic features parameters. Leave-one-out cross-validation (LOOCV) that estimated performance in an ‘independent’ dataset was implemented to evaluate the performance of the radiomics models in the training cohort. An independent dataset, that is the test cohort, was used to verify the generalization ability of the radiomics models. The receiver operating characteristics (ROC) was used to evaluate diagnostic performance of radiomics model. Global diagnostic performance of five models were evaluated by average area under the ROC curve (AUC). Conclusion The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro average AUC, 0.98; macro average AUC, 0.99), which was then confirmed with by internal cross-validation (LOOCV) (micro average AUC, 0.89; macro average AUC, 0.88) and external validation (test cohort) (micro average AUC, 0.81; macro average AUC, 0.79). Our proposed CECT image-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs.
Objective This study was performed to examine the value of computed tomography-based texture assessment for characterizing different types of ovarian neoplasms. Methods This retrospective study involved 225 patients with histopathologically confirmed ovarian tumors after surgical resection. Two different data sets of thick (5-mm) slices (during regular and portal venous phases) were analyzed. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis were performed to classify ovarian tumors. The radiologist’s misclassification rate was compared with the MaZda (texture analysis software) findings. The results were validated with the neural network classifier. Receiver operating characteristic curves were analyzed to determine the performances of different parameters. Results Nonlinear discriminant analysis had a lower misclassification rate than the other analyses. Thirty texture parameters significantly differed between the two groups. In the training set, WavEnLH_s-3 and WavEnHL_s-3 were the optimal texture features during the regular phase, while WavEnHH_s-4 and Kurtosis seemed to be the most discriminative features during the portal venous phase. In the validation test, benign versus malignant tumors and benign versus borderline lesions were well-distinguished. Conclusions Computed tomography-based texture features provide a useful imaging signature that may assist in differentiating benign, borderline, and early-stage ovarian cancer.
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