摘要
目的探讨基于^(18)F-FDG PET/CT的影像组学预测乳腺癌分子分型和细胞增殖核抗原Ki-67表达水平的价值。方法回顾性分析2016年4月至2023年5月于苏州大学附属第一医院行^(18)F-FDG PET/CT检查并经病理学检查证实的134例乳腺癌患者[均为女性,年龄(55.4±13.3)岁]。利用LIFEx软件提取影像组学特征,采用最小绝对收缩和选择算子(LASSO)算法和两独立样本t检验来筛选特征,并计算影像组学得分,得到影像组学模型;利用有监督的logistic回归筛选并得到临床模型;结合影像组学和临床特征,采用logistic回归分析建立复合预测模型。绘制ROC曲线,并采用Delong检验比较不同模型AUC的差异。结果134例患者中,三阴性乳腺癌(TNBC)22例,人表皮生长因子受体2(HER2)过表达型47例,Luminal A型和B型分别37例和28例。其中,Ki-67高表达型85例,低表达型49例。复合模型预测TNBC、HER2过表达型,Luminal A型和Ki-67表达的AUC及95%CI分别为:0.843(0.770~0.900)、0.808(0.723~0.876)、0.825(0.711~0.908)和0.836(0.762~0.894),高于单独临床模型(z值:1.97~3.06,均P<0.05)。结论基于^(18)F-FDG PET/CT的影像组学模型结合临床因素可以很好地预测乳腺癌的分子分型和Ki-67表达水平。
Objective To investigate the value of radiomics signatures based on ^(18)F-FDG PET/CT for predicting molecular classification and Ki-67 expression of breast cancer.Methods A total of 134 female patients((55.4±13.3)years)who underwent ^(18)F-FDG PET/CT examination and were diagnosed with breast cancer by pathology in the First Affiliated Hospital of Soochow University from April 2016 to May 2023 were retrospectively enrolled.LIFEx software was used to extract radiomics features and the least absolute shrinkage and selection operator(LASSO)algorithm and independent-sample t test were used to screen potentially meaningful features and calculate the radiomics score,which were considered as radiomics models.Clinical characteristics were selected by supervised logistic regression and clinical models were established.Radiomics features and clinical characteristics were incorporated to logistic regression analysis to establish combined models.ROC curves were drawn and the differences among AUCs were analyzed by Delong test.Results Among 134 patients,22 were with triple negative breast cancer(TNBC),47 were human epidermal growth factor receptor 2(HER2)over-expression type,37 were Luminal A type and the rest 28 were Luminal B type.The expression of Ki-67 was high in 85 patients,and was low in the rest 49 patients.The AUCs(95%CI)of the combined models for predicting TNBC,HER2 overexpression type,Luminal A type and Ki-67 expression were 0.843(0.770-0.900),0.808(0.723-0.876),0.825(0.711-0.908)and 0.836(0.762-0.894),respectively,which were higher than those of clinical models(z values:1.97-3.06,all P<0.05).Conclusion The predictive model combining radiomics signatures based on ^(18)F-FDG PET/CT and clinical characteristics can well predict the molecular classification and Ki-67 expression level of breast cancer.
作者
贾童童
史津宇
李继会
章斌
桑士标
张晓懿
邓胜明
Jia Tongtong;Shi Jinyu;Li Jihui;Zhang Bin;Sang Shibiao;Zhang Xiaoyi;Deng Shengming(Department of Nuclear Medicine,the First Affiliated Hospital of Soochow University,Suzhou 215006,China;Department of Nuclear Medicine,Changshu No.2 People's Hospital,Changshu 215501,China)
出处
《中华核医学与分子影像杂志》
CAS
CSCD
北大核心
2024年第2期86-91,共6页
Chinese Journal of Nuclear Medicine and Molecular Imaging
基金
国家自然科学基金(81601522)
姑苏卫生青年拔尖人才资助项目(GSWS2020013)。
