摘要
目的 旨在构建一个基于乳腺动态对比增强MRI(dynamic contrast-enhanced MRI,DCE-MRI)Kaiser评分的乳腺肿块诊断预测模型并进行外部验证,用于诊断预测乳腺MRI中肿块的恶性风险。材料与方法收集2020年5月至2021年3月于湖南省人民医院天心阁院区及2019年9月至2020年12月于湖南省人民医院马王堆院区行术前乳腺DCE-MRI检查并经手术或穿刺病理证实的病灶分别为199个(来自于199名患者)、86个(来自于81名患者,其中5名患者有2个病灶),以天心阁院区数据为训练集,马王堆院区数据为验证集。收集的影像参数包括:乳腺纤维腺体类型、背景实质强化(面积、对称性)、病灶大小、肿块特征(形状、边缘、内部强化特征)、DCE-MRI时间-信号曲线(time-signal intensity curve,TIC)、乳腺水肿情况、最大信号强度投影(maximum intensity projection,MIP)征、附属影像特征(包括:乳头回缩、乳头侵犯、皮肤回缩、皮肤增厚、皮肤侵犯、腋窝淋巴结增大、胸肌侵犯、胸壁侵犯、结构扭曲),并基于Kaiser评分流程图给出Kaiser评分;临床参数包括:年龄、性别、是否伴疼痛、是否可触及肿块、皮肤红肿情况、乳头溢液情况、是否伴橘皮样外观及酒窝征。采用最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)进行预测变量筛选,多因素logistic回归进行预测模型构建,并以列线图的形式呈现。受试者工作特征(receiver operating characteristic,ROC)曲线、De Long检验、净重新分类指数(net reclassification index,NRI)及综合判别改善指数(integrated discrimination improvement,IDI)用于比较基于Kaiser评分的乳腺肿块诊断预测模型(以下简称“乳腺肿块诊断模型”)和Kaiser评分的诊断性能;绘制校准曲线以评估乳腺诊断模型的校准度;决策曲线分析(decision curve analysis,DCA)用于评价二者的临床有效性。结果LASSO回归显示“年龄”“MIP征”及“附属影像特征”是Kaiser评分所用指标之外的有效预测因素;在训练集中,乳腺肿块诊断模型及Kaiser评分的AUC分别为0.944、0.890,差异有统计学意义(P<0.05),在验证集中,乳腺肿块诊断模型及Kaiser评分的AUC分别为0.941、0.874,差异有统计学意义(P<0.05)。De Long检验及NRI、IDI显示乳腺肿块诊断模型较Kaiser评分对乳腺肿块的诊断性能更好,差异有统计学意义(P<0.05),校准曲线显示乳腺肿块诊断模型的校准度良好;DCA表明乳腺肿块诊断模型具有较高的临床应用价值;结论基于Kaiser评分的乳腺肿块诊断模型可被用于乳腺肿块恶性概率的术前预测,并且其对乳腺肿块的诊断性能优于经典Kaiser评分。
Objective:To construct and externally validate a diagnostic prediction model for breast masses based on the Kaiser score of dynamic contrast-enhanced MRI(DCE-MRI)for diagnostic prediction of the risk of malignancy of masses on breast MRI.Materials and Methods:We collected 199 lesions(from 199 patients)and 86 lesions(from 81 patients,including 5 patients with 2 lesions)from the Tianxinge Branch of Hunan Provincial People's Hospital from May 2020 to March 2021 and from the Mawangdui Branch of Hunan Provincial People's Hospital from September 2019 to December 2020,who underwent preoperative breast DCE-MRI and were confirmed by surgical or puncture pathology.Using the data from Tianxinge Branch as the training set and the data from Mawangdui Branch as the validation set.Imaging parameters collected included:the amount of fibroglandular tissue(FGT),background parenchymal enhancement(BPE),lesion size,mass characteristics(shape,margins,internal enhancement features),time-signal intensity curve(TIC),breast edema status,maximum intensity projection(MIP)sign,associated features(including nipple retraction,nipple invasion,skin retraction,skin thickening,skin invasion,axillary lymph node enlargement,pectoral muscle invasion,chest wall invasion,structural distortion),and Kaiser score based on the Kaiser score flow chart.Clinical parameters included age,gender,presence of pain,palpable mass,skin erythema,nipple discharge,orange peel appearance,and dimple sign.The least absolute shrinkage and selection operator(LASSO)was used to select predictor variables.Multivariate logistic regression was used to construct the prediction model,which was presented as a nomogram.The receiver operating characteristic(ROC)curve,DeLong test,net reclassification index(NRI),and integrated discrimination improvement(IDI)were used to compare the diagnostic performance of the Kaiser score-based breast mass diagnostic prediction model(hereinafter referred to as"breast mass diagnostic model")and Kaiser score;calibration curves were plotted to assess the calibration of the breast mass diagnostic model;decision curve analysis(DCA)was used to evaluate the clinical validity of them.Results:LASSO regression showed that"age""MIP sign"and"associated features"were effective predictors in addition to those used in the Kaiser score;In the training set,the AUCs of the breast mass diagnostic model and Kaiser score were 0.944 and 0.890,with statistically significant differences(P<0.05),and in the validation set,the AUCs of the breast mass diagnostic model and Kaiser score were 0.941 and 0.874,with statistically significant differences(P<0.05).Furthermore,NRI and IDI showed that the breast mass diagnostic model had a better diagnostic performance for breast masses than the Kaiser score,and the difference was statistically significant(P<0.05);the calibration curve showed that the breast mass diagnostic model was well calibrated;DCA indicated that the breast mass diagnostic model had high clinical application value.Conclusions:The Kaiser score-based diagnostic model for breast masses can be used for preoperative prediction of the probability of malignancy of breast masses.Its diagnostic performance for breast masses is better than the classic Kaiser score.
作者
易熙
王月爱
刘芳
杨宇
陈晓琼
曾禹莉
YI Xi;WANG Yueai;LIU Fang;YANG Yu;CHEN Xiaoqiong;ZENG Yuli(Department of Ultrasound Imaging,the First Hospital of Hunan University of Chinese Medicine,Changsha 410007,China;Department of Radiology,Hunan Provincial People's Hospital(the First Affiliated Hospital of Hunan Normal University),Changsha 410016,China;Department of Radiology,the First Hospital of Hunan University of Chinese Medicine,Changsha 410007,China)
出处
《磁共振成像》
CAS
CSCD
北大核心
2023年第5期96-103,共8页
Chinese Journal of Magnetic Resonance Imaging
基金
湖南省教育厅科学研究项目(编号:21C0236)
湖南省自然科学基金委员会科卫联合基金资助项目(编号:2022JJ70114)。
