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The Value of Texture Analysis of Multi-parameter MRI Images in Distinguishing Benign and Malignant Lesions of O-RADS MRI 4 Lesions

Year: 2025

Type: article

Abstract: Objectives: To investigate the diagnostic performance of texture analysis using multi-parameter MRI in distinguishing between benign and malignant lesions with ovarian-adnexal magnetic resonance imaging report and data system (O-RADS MRI) score 4. Methods: A retrospective analysis was conducted of 57 lesions with an O-RADS MRI score of 4, of which 26 were benign and 31 were malignant.Based on the T2WI, ADC, and CE_T1WI, the textural features of the entire lesion were extracted.The minimum redundancy maximum relevance (mRMR) method was used to select features, and the random forest (RF) algorithm was used to construct four prediction models: T2WI, ADC, CE_T1WI, and the combined models.Ten-fold cross-validation was used to verify the model prediction performance, and receiver operating characteristic (ROC) analysis was used to evaluate the model performance, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).Results: 3474 texture features were extracted from the ADC, T2WI, and CE_T1WI images.ADC, T2WI, CE_T1WI, and combined models were constructed.Each model contained ten texture features.The AUC of the ADC, T2WI, CE_T1WI, and combined models were 0.749 (95% CI: 0.621-0.876),0.671 (95% CI: 0.524-0.818),0.786 (95% CI: 0.662-0.909),and 0.860 (95% CI: 0.76-0.959),respectively.The AUC of the combined model was significantly higher than those of the other three groups.The accuracy, sensitivity, specificity, PPV, and NPV of the combined model in distinguishing benign and malignant lesions with an O-RADS MRI score of 4 were 75.9%, 77.8%, 74.1%, 72.4%, and 79.3%, respectively.Conclusion: Texture analysis of multi-parameter MRI can improve the diagnostic efficiency of distinguishing benign and malignant lesions with an O-RADS MRI score of 4 and provide some help in clinical decision-making.

Source: International Journal of Medical Sciences

DOI: 10.7150/ijms.107452