摘要
目的:本研究基于脑部T_(1)C和T_(2)W MRI建立人工智能模型,预测肺癌脑转移患者在靶向治疗中的耐药性T790M突变。方法:本研究收集80例肺癌脑转移患者(2017年6月—2019年12月)的T_(1)C和T_(2)W MRI影像和临床数据进行回顾性分析(患者按照2∶1的比例分成训练集和测试集)。采用无监督k-means算法将肿瘤区域划分为高亮度区域和低亮度区域,提取不同区域的影像组学图像特征构建模型,评估每个模型的诊断效果。绘制受试者工作特征(Receiver operating characteristic,ROC)曲线,计算ROC曲线下面积(Area under curve,AUC)、特异性和敏感性作为模型评价指标,分析模型的潜在临床应用价值。结果:对T_(1)C和T_(2)W MRI和临床特征融合的统计计算表明,本研究建立的模型对T790M突变具有良好的预测能力,在训练集和测试集上的AUC分别为0.899和0.818。结论:本研究建立的计算机模型可以有效预测肺癌脑转移患者T790M突变,具有潜在的临床辅助诊断价值。
Objective:In this study,an artificial intelligence model was established based on contrast-enhanced T_(1)-weighted(T_(1)C)and T_(2)-weighted(T_(2)W)sequences of brain MRI to predict drug-resistant T790M mutations in lung cancer brain metastasis patients undergoing targeted therapy.Methods:In this study,T_(1)C and T_(2)W MRI imaging data and clinical data of 80 lung cancer brain metastasis patients(from June 2017 to December 2019)were collected for retrospective analysis(the data was divided into training and validation cohorts in a ratio of 2∶1).The unsupervised k-means algorithm was used to segment the tumor region into high-brightness and low-brightness subregions,and the radiomics features of every subregion were extracted to establish a model to evaluate the diagnostic performance of every model.Receiver operating characteristic(ROC)curves were plotted,and the area under the curve(AUC),specificity and sensitivity were used as evaluation metrics to analyze the potential clinical application value of the model.Results:Statistical calculations combining T_(1)C and T_(2)W MRI and clinical features showed that the model established in this study had good predictive ability for T790M mutation,with AUCs of 0.899 and 0.818 in the training and testing sets,respectively.Conclusion:The computer model established in this study can effectively predict the T790M mutation in lung cancer brain metastasis patients and has potential clinical auxiliary diagnostic value.
作者
崔婀娜
杨春娜
王晓煜
沙宪政
赵鹏
孙艺瑶
CUI E-nuo;YANG Chun-na;WANG Xiao-yu;SHA Xian-zheng;ZHAO Peng;SUN Yi-yao(School of Intelligent Science and Engineering,Shenyang University,Shenyang 110044,China;School of Intelligent Medicine,China Medical University,Shenyang 110122,China;Department of Medical Imaging,Liaoning Cancer Hospital,Shenyang 110801,China)
出处
《中国临床医学影像杂志》
CAS
CSCD
北大核心
2024年第3期153-159,共7页
Journal of China Clinic Medical Imaging
基金
国家重点研发项目BTIT(2022YFF1202803)
辽宁省教育厅面上项目(JYTMS20230132)。
关键词
癌
非小细胞肺
脑肿瘤
肿瘤转移
磁共振成像
Carcinoma,Non-Small-Cell Lung
Brain Neoplasms
Neoplasm Metastasis
Magnetic Resonance Imaging
