Fat suppression T2WI with radiomics analysis in differential diagnosis of leiomyoma and adenomyoma
CHEN Cheng1, YE Miaomiao1, CHEN Bo2, ZHU Xueqiong1
1.Department of Obstetrics and Gynecology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou 325027, China; 2.Department of Radiography, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou 325027, China
CHEN Cheng,YE Miaomiao,CHEN Bo, et al. Fat suppression T2WI with radiomics analysis in differential diagnosis of leiomyoma and adenomyoma[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2020, 50(8): 647-651.
Abstract：Objective: To identify the value of preoperative fat suppression T2WI of pelvic magnetic resonance imaging (MRI) for differential diagnosis of leiomyoma and adenomyoma with radiomics analysis. Methods: This retrospective study included 120 cases of patients with leiomyoma or adenomyoma pathologically
confirmed by surgery in the Second Affiliated Hospital of Wenzhou Medical University between April 2015 and September 2019. All patients underwent conventional MRI plain scan of pelvic before surgery. According to the ratio of 7:3, all the patients were randomly divided into training set (n=84) and verification set (n=36). Region of interest (ROI) was manually delineated on fat suppression T2WI, and subsequently the high-throughput data collection, radiomics features extraction and dimensionality reduction were performed. Multivariate logistic regression was used to build up the prediction model of the differential diagnosis of leiomyoma and adenomyoma. Moreover, the sensitivity, specificity, accuracy and the area under the receiver-operating characteristic curve (AUC) were used for evaluating the predictive performance of the model. Results: A total of 396 radiomics features were extracted and 6 radiomics features were included in the prediction model of the differential diagnosis of leiomyoma and adenomyoma. In the prediction model, AUC in the training set was 0.861, with sensitivity being 84.1% (37/44), specificity 82.5% (33/40) and accuracy 83.3% (70/84), while AUC in the verification set was 0.913, with sensitivity being 84.2% (16/19), specificity 94.1% (16/17) and accuracy 88.9% (32/36). Conclusion: The radiomics prediction model based on fat suppression T2WI can well differentiate the leiomyoma from adenomyoma, which potentially provides a favorable and non-invasive method for clinical differential diagnosis.
