首页|期刊导航|磁共振成像|基于磁共振成像影像组学的肿瘤内异质性评分模型在卵巢肿瘤恶性风险评估中的应用价值

基于磁共振成像影像组学的肿瘤内异质性评分模型在卵巢肿瘤恶性风险评估中的应用价值OA

Utility of an MRI-based radiomics intratumoral heterogeneity scoring model for malignancy risk assessment of ovarian neoplasms

中文摘要英文摘要

目的 基于磁共振成像(magnetic resonance imaging,MRI)影像组学方法计算肿瘤内异质性(intratumoral heterogeneity,ITH)评分,并联合临床及常规MRI特征构建卵巢肿瘤术前恶性风险评估模型,评估其在卵巢-附件报告和数据系统(Ovarian-Adnexal Reporting and Data System,O-RADS)MRI评分4分病灶风险分层及辅助初级医师诊断中的价值.材料与方法 回顾性分析120例经病理证实的卵巢肿瘤患者,其中良性52例,恶性68例(含交界性肿瘤).按7∶3比例随机分为训练集(84例)和测试集(36例).对T2加权脂肪抑制序列图像进行预处理,并勾画瘤内及瘤周3 mm感兴趣区后,采用滑动窗口提取影像组学特征,结合K-means聚类分别计算ITH评分和联合ITH评分.整合临床特征、常规MRI特征及ITH评分,经单因素分析和最小绝对收缩和选择算子回归筛选后,再采用多因素logistic回归筛选独立预测因子,分别构建临床模型、瘤内模型和联合模型.依据O-RADS MRI对全部病灶进行评分,并对O-RADS MRI 4分病灶进一步分层分析;同时评估模型对初级医师诊断的辅助价值.结果 ITH评分具有良好的观察者内和观察者间一致性,组内相关系数分别为0.86和0.84.基于卵巢恶性肿瘤风险算法(risk of ovarian malignancy algorithm,ROMA)指数、肿瘤成分及联合ITH评分构建的联合模型在3种模型中诊断效能最高.测试集中,临床模型、瘤内模型和联合模型的受试者工作特征曲线下面积分别为0.805[95%置信区间(confidence interval,CI):0.662~0.948]、0.867(95%CI:0.750~0.984)和 0.923(95%CI:0.827~0.994).O-RADS MRI评分与联合模型总体诊断效能相近;在4分亚组分析中,联合模型显示出进一步风险分层的潜力,且诊断准确率和特异度较高.结论 基于ROMA指数、肿瘤成分及联合ITH评分构建的联合模型在卵巢肿瘤术前恶性风险评估中的诊断性能最高,并在O-RADS MRI 4分病灶中具有分层的潜力,可为临床诊断提供辅助参考.

Objective:To calculate an intratumoral heterogeneity(ITH)score using a magnetic resonance imaging(MRI)-based radiomics approach,to construct a preoperative malignancy risk assessment model for ovarian neoplasms by integrating clinical variables and conventional MRI features,and to evaluate its adjunctive value for risk stratification of Ovarian-Adnexal Reporting and Data System(O-RADS)MRI category 4 lesions and for diagnosis by junior physicians.Materials and Methods:This retrospective study included 120 patients with pathologically confirmed ovarian neoplasms,including 52 benign and 68 malignant tumors,the latter including borderline tumors.The patients were randomly assigned to a training set(n=84)and a test set(n=36)in a 7∶3 ratio.After preprocessing T2-weighted images with spectral attenuated inversion recovery fat suppression,intratumoral and 3-mm peritumoral regions of interest were delineated.Radiomics features were extracted using a sliding-window approach,and K-means clustering was used to calculate the ITH score and the combined ITH score.Clinical features,conventional MRI features,and ITH scores were integrated.After screening by univariable analysis and least absolute shrinkage and selection operator regression,independent predictors were identified by multivariable logistic regression to construct a clinical model,an intratumoral model,and a combined model,respectively.All lesions were scored according to O-RADS MRI,and O-RADS MRI category 4 lesions were further analyzed for risk stratification;the adjunctive value of the model for diagnosis by junior physicians was also evaluated.Results:The ITH score showed good intra-and interobserver agreement,with intra-class correlation coefficients of 0.86 and 0.84,respectively.Among the three models,the combined model based on the risk of ovarian malignancy algorithm(ROMA)index,tumor composition,and the combined ITH score achieved the highest diagnostic performance.In the test set,the areas under the receiver operating characteristic curve were 0.805[95%confidence interval(CT):0.662 to 0.948],0.867(95%CI:0.750 to 0.984),and 0.923(95%CT:0.827 to 0.994)for the clinical,intratumoral,and combined models,respectively.The overall diagnostic performance of the O-RADS MRI score was comparable to that of the combined model.In the subgroup analysis of category 4 lesions,the combined model showed potential for further risk stratification,with relatively high diagnostic accuracy and specificity.Conclusion:The combined model based on the ROMA index,tumor composition,and the combined ITH score showed the highest diagnostic performance for preoperative malignancy risk assessment of ovarian neoplasms.It also has the potential to stratify O-RADS MRI category 4 lesions and may assist clinicians in diagnosis.

国明君;张娣;范华;谭淑宇;孙思雨;张传臣

山东第一医科大学(山东省医学科学院)研究生部,济南 250117||聊城市人民医院医学影像中心,聊城 252000聊城市人民医院医学影像中心,聊城 252000聊城市人民医院医学影像中心,聊城 252000山东第一医科大学(山东省医学科学院)研究生部,济南 250117||聊城市人民医院医学影像中心,聊城 252000山东第一医科大学(山东省医学科学院)研究生部,济南 250117||聊城市人民医院医学影像中心,聊城 252000聊城市人民医院医学影像中心,聊城 252000

医药卫生

卵巢肿瘤肿瘤异质性影像组学列线图磁共振成像卵巢-附件报告和数据系统风险分层

ovarian neoplasmstumor heterogeneityradiomicsnomogrammagnetic resonance imagingOvarian-Adnexal Reporting and Data Systemrisk stratification

《磁共振成像》 2026 (4)

79-87,9

国家自然科学基金(编号:62476155)山东省医药卫生科技项目(编号:202309011571)National Natural Science Foundation of China(No.62476155)Shandong Provincial Medical and Health Technology Project(No.202309011571).

10.12015/issn.1674-8034.2026.04.011

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