基于对比增强能谱乳腺X线摄影病变内部与周围区域影像组学联合临床因素预测乳腺影像报告和数据系统4类乳腺病变良性与恶性的价值  

The value of radiomics based on contrast-enhanced spectral mammography of internal and peripheral regions combined with clinical factors in predicting benign and malignant breast lesions of breast imaging reporting and data system category 4

作　　者：张诗杰 毛宁[1] 张海程 林凡 王思敏 高婧[4] 张涵[1] 王中一 顾雅佳 谢海柱[1] Zhang Shijie;Mao Ning;Zhang Haicheng;Lin Fan;Wang Simin;Gao Jing;Zhang Han;Wang Zhongyi;Gu Yajia;Xie Haizhu(Department of Radiology,Yantai Yuhuangding Hospital,Affiliated Hospital of Qingdao University,Yantai 264000,China;Big Data&Artificial Intelligence Laboratory,Yantai Yuhuangding Hospital,Affiliated Hospital of Qingdao University,Yantai 264000,China;Department of Radiology,Fudan University Shanghai Cancer Center,Shanghai 200032,China;School of Medical Imaging,Binzhou Medical University,Yantai 264003,China)

机构地区：[1]青岛大学附属烟台毓璜顶医院影像科,烟台264000 [2]青岛大学附属烟台毓璜顶医院大数据与人工智能实验室,烟台264000 [3]复旦大学附属肿瘤医院放射科,上海200032 [4]滨州医学院医学影像学院,烟台264003

出　　处：《中华放射学杂志》2023年第2期173-180,共8页Chinese Journal of Radiology

基　　金：国家自然科学基金(82001775);山东省自然科学基金(ZR2021MH120)。

摘　　要：目的探讨基于对比增强能谱乳腺X线摄影(CESM)乳腺病变内部与周围区域的影像组学特征联合临床因素预测乳腺影像报告和数据系统(BI-RADS)4类乳腺病变性质的价值。方法回顾性分析2017年7月至2020年7月在青岛大学附属烟台毓璜顶医院(中心1)及2019年6月至2020年7月在复旦大学附属肿瘤医院(中心2)接受诊治的乳腺病变患者的临床及CESM图像资料。中心1纳入835例患者, 均为女性, 年龄17~80(49±12)岁, 以Python软件中的"traintestsplit"函数按8∶2的比例分为训练集(667例)和测试集(168例);中心2纳入49例患者作为外部验证集, 均为女性, 年龄34~70(51±8)岁。分别从所有患者CESM图像的病变内部区域(ITR)、病变周围5 mm及10 mm区域(PTR 5 mm、PTR 10 mm)及病变内部联合周围5 mm及10 mm区域(IPTR 5 mm、IPTR 10 mm)提取影像组学特征, 通过方差过滤、SelectKBest算法和最小绝对收缩和选择算子算法筛选后分别建立ITR标签、PTR 5 mm标签、PTR 10 mm标签、IPTR 5 mm标签、IPTR 10 mm标签。在训练集中采用单因素和多因素logistic回归筛选出对鉴别BI-RADS 4类乳腺病变良性与恶性有意义的影像组学标签及临床因素并构建列线图。采用受试者操作特征曲线及曲线下面积(AUC)评估列线图预测BI-RADS 4类乳腺病变良性与恶性的效能;采用决策曲线和校准曲线评估列线图的净获益及校准能力。结果基于ITR标签、PTR 5 mm标签、PTR 10 mm标签、IPTR 5 mm标签、年龄、BI-RADS 4类亚分类构建的列线图在训练集、测试集及外部验证集中鉴别BI-RADS 4类乳腺病变恶性与良性的AUC分别为0.94、0.92和0.95。校准曲线显示, 列线图在训练集、测试集及外部验证集中预测BI-RADS 4类乳腺恶性病变的概率和实际结果一致性较好。决策曲线表明, 列线图在训练集、测试集及外部验证集中净收益较好。结论基于CESM乳腺病变内部和周围区域的影像组学特征联合临�Objective To evaluate the value of radiomics based on contrast-enhanced spectral mammography(CESM)of internal and peripheral regions combined with clinical factors in predicting benign and malignant breast lesions of breast imaging reporting and data system category 4(BI-RADS 4).Methods A retrospective analysis was performed on the clinical and imaging data of patients with breast lesions who were treated in Yantai Yuhuangding Hospital(Center 1)Affiliated to Qingdao University from July 2017 to July 2020 and in Fudan University Cancer Hospital(Center 2)from June 2019 to July 2020.Center 1 included 835 patients,all female,aged 17-80(49±12)years,divided into training set(667 cases)and test set(168 cases)according to the"train-test-split"function in Python software at a ratio of 8∶2;and 49 patients were included from Center 2 as external validation set,all female,aged 34-70(51±8)years.The radiomics features were extracted from the intralesional region(ITR),the perilesional regions of 5,10 mm(PTR 5 mm,PTR10 mm)and the intra-and perilesional regions of 5,10 mm(IPTR 5 mm,IPTR 10 mm)and were selected by variance filtering,SelectKBest algorithm,and least absolute shrinkage and selection operator.Then five radiomics signatures were constructed including ITR signature,PTR 5 mm signature,PTR 10 mm signature,IPTR 5 mm signature,IPTR 10 mm signature.In the training set,univariable and multivariable logistic regressions were used to construct nomograms by selecting radiomics signatures and clinical factors with significant difference between benign and malignant BI-RADS type 4 breast lesions.The efficacy of nomogram in predicting benign and malignant BI-RADS 4 breast lesions was evaluated by the receiver operating characteristic curve and area under the curve(AUC).Decision curve and calibration curve were used to evaluate the net benefit and calibration capability of the nomogram.Results The nomogram included ITR signature,PTR 5 mm signature,PTR 10 mm signature,IPTR 5 mm signature,age,and BI-RADS category 4 subclassificati

关 键 词：乳腺肿瘤 影像组学 列线图 乳腺影像报告和数据系统 

分 类 号：R737.9[医药卫生—肿瘤]