T2WI影像组学鉴别卵巢成人型颗粒细胞瘤与DWI高信号纤维-卵泡膜细胞肿瘤  

T2WI-based radiomics for discriminating between ovarian adult-type granulosa cell tumor and ovarian fibroma-thecoma with high-signal intensity on DWI

作　　者：王丰[1] 秦思源 周延[1] 王奇政 刘剑羽[1] 郎宁[1] WANG Feng;QIN Siyuan;ZHOU Yan;WANG Qizheng;LIU Jianyu;LANG Ning(Department of Radiology,Peking University Third Hospital,Beijing 100191,China)

机构地区：[1]北京大学第三医院放射科,北京100191

出　　处：《磁共振成像》2024年第8期152-157,165,共7页Chinese Journal of Magnetic Resonance Imaging

基　　金：国家自然科学基金(编号:82371921)。

摘　　要：目的 探讨基于T2WI影像组学列线图鉴别诊断卵巢成人型颗粒细胞瘤与扩散加权成像(diffusion weighted imaging,DWI)高信号纤维-卵泡膜细胞肿瘤的效能。材料与方法 回顾性收集北京大学第三医院2019年1月至2023年10月经手术病理确诊的卵巢成人型颗粒细胞瘤29例和DWI呈高信号的纤维-卵泡膜细胞肿瘤61例。所有肿瘤按7:3的比例随机分为训练集和验证集。应用单因素分析和多因素logistic回归筛选出临床和常规MRI征象,建立临床模型。基于T2WI提取影像组学特征,应用K最佳和最小绝对收缩和选择算法进行特征筛选,构建影像组学模型并计算影像组学评分(radiomics score,Rad-score)。联合临床模型和Rad-score构建列线图模型。应用受试者工作特征曲线(receiver operating characteristic,ROC)分析各模型诊断效能,应用决策曲线分析(decision curve analysis,DCA)评价模型的临床价值。结果 经logistic回归分析,将“蜂窝样”小囊变[比值比(odds ratio,OR)值=0.20,95%置信区间(confidence interval,CI)=0.05~0.79,P=0.022]和肿瘤内出血(OR值=0.16,95%CI=0.03~0.98,P=0.048)用于构建临床模型。基于T2WI筛选保留了9个组学特征用于构建影像组学模型。由“蜂窝样”小囊变、肿瘤内出血和Rad-score构建列线图模型。影像组学模型和列线图模型的ROC曲线下面积(area under the curve,AUC)均高于临床模型(训练集:0.983 vs.0.742,Z=-4.058,P<0.001;0.969 vs.0.742,Z=-3.817,P<0.001。验证集:0.858 vs.0.731,Z=-1.388,P=0.165;0.883 vs.0.731,Z=-1.612,P=0.107),列线图和影像组学模型的AUC差异无统计学意义(训练集:Z=-1.040,P=0.298;验证集:Z=0.822,P=0.411)。DCA显示列线图和影像组学模型明显优于临床模型。结论 本研究所构建的基于T2WI的影像组学模型和列线图模型能有效鉴别卵巢颗粒细胞瘤和DWI高信号纤维-卵泡膜细胞肿瘤,且效能优于基于常规MRI征象的临床模型。Objective:To investigate the value of T2WI‑based radiomics nomogram for the preoperative differentiation of ovarian adult-type granulosa cell tumor and ovarian fibroma-thecoma with high-signal intensity on diffusion weighted imaging(DWI).Materials and Methods:This retrospective study included 29 patients with ovarian granulosa cell tumors and 61 cases with fibroma-thecomas with high-signal intensity on DWI,which were confirmed by surgical pathology in Peking University Third Hospital from January 2019 to October 2023.All tumors were randomly divided into a training set and a validation set at a ratio of 7∶3.The clinical model was constructed by clinical and routine MRI features which were selected by univariate analysis and multivariate logistic regression.Radiomics features were extracted from T2WI.Select K best and least absolute shrinkage and selection operator(LASSO)algorithm were used to reduce the dimension and then the radiomics model was constructed by selected features,and a radiomics score(Rad-score)was calculated.The nomogram model was constructed by combining with clinical model and Rad-score.The receiver operator characteristic(ROC)curves were used to evaluate the performance of three models.The decision curve analysis(DCA)was used to evaluate the clinical value.Results:The logistic regression results showed that a"honeycomb"cyst[odds ratio(OR)=0.20,95%confidence interval(CI)=0.05-0.79,P=0.022]and intratumoral hemorrhage(OR=0.16,95%CI=0.03-0.98,P=0.048)can be used to construct the clinical model.A total of 9 features were extracted from T2WI to build the radiomics model.Finally,the nomogram model incorporating Rad-score,a"honeycomb"cyst and intratumoral hemorrhage was established.The AUCs of radiomics model and nomogram model were higher than those of clinical model(training set:0.983 vs.0.742,Z=-4.058,P<0.001;0.969 vs.0.742,Z=-3.817,P<0.001.validation set:0.858 vs.0.731,Z=-1.388,P=0.165;0.883 vs.0.731,Z=-1.612,P=0.107).There was no significantly difference in AUCs between the radiomics model and

关 键 词：卵巢肿瘤 磁共振成像 影像组学 列线图 诊断 

分 类 号：R445.2[医药卫生—影像医学与核医学] R737.31[医药卫生—诊断学]