山西医科大学学报2025，Vol.56Issue(10)：1109-1115,7.DOI:10.13753/j.issn.1007-6611.2025.10.002
MRI影像组学特征列线图在鉴别卵巢囊肿和单房囊腺瘤中的应用价值
Application of MRI radiomic features-based nomogram in identifying ovarian cysts and unilocular cystadenomas
摘要
Abstract
Objective To evaluate the diagnostic efficacy of a radiomics-based nomogram using female pelvic MR-T2WI and contrast-enhanced sequences in distinguishing ovarian cysts and unilocular cystadenoma.Methods A retrospective study was conducted on 113 patients,121 lesions(49 ovarian cysts and 72 unilocular cystadenomas),who underwent pelvic MRI scans(both non-contrast and contrast-enhanced)for ovarian masses and were surgically confirmed as ovarian cysts or unilocular cystadenomas.The MRI images of T2WI and contrast-enhanced sequences for each ovarian lesion were analyzed,and clinical data of patients were collected.From T2-weighted and contrast-enhanced images,1 316 radiomic features were extracted per sequence,producing 2 632 features in total,to se-lect the optimal features,calculate the radiomics score,and establish a radiomics model.Patients were randomly divided into training group(n=80,84 lesions)and validation group(n=33,37 lesions)at a ratio of 7∶3.Logistic regression was employed to identify inde-pendent clinical risk factors and construct a clinical model.Finally,a combined model of morphologic parameters and radiomics score was built and visualized as a nomogram.Receiver operating characteristic(ROC)curves were used to evaluate the discriminatory perfor-mance of each model in distinguishing ovarian cysts from unilocular cystadenomas,while decision curve analysis(DCA)was per-formed to assess the clinical value of the combined model.Results The difference in age was not statistically significant between the training set and the testing set(P>0.05).Based on the imaging characteristics of T2WI and enhanced sequences,10 features were identified from each sequence and a single-sequence imaging omics model was established.After integrating the imaging features from both T2WI and enhanced sequences,a fusion feature set was created for selection of characteristic features,and finally 6 features were obtained.A combined imaging omics model for T2WI and enhanced sequences was established using these 6 key features.The model showed an area under the curve(AUC)of 0.946 in training group and 0.927 in validation group for predicting unilocular cysta-denoma.In the clinical features,the anteroposterior and craniocaudal diameters of the lesion were identified as independent MRI imag-ing factors for distinguishing cysts from unilocular cystadenomas(P<0.05).The AUC of the combined model based on APD,SID and ra-diomics was 0.955 in the training set and 0.942 in the testing set,higher than that of the imaging omics model(Z=-3.451,P<0.001).The combined model yielded a greater clinical net benefit than the radiomics-alone model across the entire threshold probability range of 0-1.0.Conclusion The radiomics model based on MR-T2WI and contrast-enhanced sequences is effective in the differential diag-nosis of ovarian cysts and unilocular cystadenomas.关键词
卵巢囊肿/单房囊腺瘤/囊性病变/MRI影像组学/列线图/鉴别诊断Key words
ovarian cyst/unilocular cystadenoma/cystic lesions/MRI imaging genomics/nomogram/differential diagnosis分类
临床医学引用本文复制引用
杨洁,孙惠苗,全帅,胡磊,高凯,王思洁..MRI影像组学特征列线图在鉴别卵巢囊肿和单房囊腺瘤中的应用价值[J].山西医科大学学报,2025,56(10):1109-1115,7.基金项目
山西省高等学校科技创新项目(2022L200) （2022L200）
