首页|多模态MRI影像组学对非小细胞肺癌纵隔淋巴结转移的预测价值

多模态MRI影像组学对非小细胞肺癌纵隔淋巴结转移的预测价值

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目的 建立基于常规MRI序列的影像组学模型,比较不同模型预测非小细胞肺癌(non-small cell lung cancer,NSCLC)纵隔淋巴结转移的效能.材料与方法 回顾性分析2012年10月至2022年5月南通市第一人民医院90例NSCLC患者的术前MRI数据,根据手术病理结果分为淋巴结转移阳性组(52例)和阴性组(38例),采用完全随机法按照7∶3比例将患者分为训练集和测试集,盐城第一人民医院的31例患者数据作为外部验证(阳性9例,阴性22例),放射科医师半自动逐层勾画原发病灶,提取基于T1WI、T2WI、高b值弥散加权成像(diffusion weighted imaging,DWI)、表观扩散系数(apparent diffusion coefficient,ADC)图像的组学特征,由超参数搜索在单因素方差分析、L1正则化、树模型等特征筛选法中选择最佳方法用于降维,分别建立逻辑斯特回归(logistic regression,LR)、高斯朴素贝叶斯(Gaussian naive Bayes,Gaussian NB)、随机森林(random forest,RF)、支持向量机(support vector machine,SVM)、决策树(decision tree,DT)等11种模型,通过受试者工作特征(receiver operating characteristic,ROC)曲线来评估模型的性能.结果 在不同序列中DT、LR、SVM模型的预测性能都表现良好,其中基于T2WI图像构建的SVM模型效能最佳,训练集、测试集及外部验证集曲线下面积(area under the curve,AUC)分别达0.98、0.98、0.72,准确度分别为96%、67%、61%、敏感度分别为88%、67%、55%、特异度分别为100%、67%、78%.结论 MRI影像组学可帮助识别NSCLC患者纵隔淋巴结是否转移,以基于T2WI的SVM模型表现最优.
Predictive value of multimodal MRI histology for mediastinal lymph node metastasis in non-small cell lung cancer
Objective:To construct radiomic models utilizing conventional MRI sequences to assess and compare their effectiveness in predicting mediastinal lymph node metastasis in non-small cell lung cancer(NSCLC).Materials and Methods:Preoperative MRI data from 90 patients diagnosed with NSCLC at the First People's Hospital of Nantong between October 2012 and May 2022 were retrospectively collected.Based on the surgical pathology results,these patients were categorized into two groups:lymph node metastasis-positive(52 cases)and negative(38 cases).The patients were allocated into a training set and a test set using a complete randomization method,with a ratio of 7∶3.Additionally,data from 31 patients at the First People's Hospital of Yancheng were used for external validation,consisting of 9 positive cases and 22 negative cases.Radiologists used layer-by-layer semi-automated delineation of the primary lesions,radiomics features were extracted from axial T1WI,T2WI,high b-value diffusion weighted imaging(DWI),and apparent diffusion coefficient(ADC)images,and selected the best method for dimensionality reduction among the feature screening methods,such as ANOVA F-value,Linear models penalized with the L1 norm,Tree-Based,etc.,by the hyperparameter search.Eleven models were established,including logistic regression(LR),Gaussian naive Bayes(Gaussian NB),random forest(RF),and Support vector machine(SVM),decision tree(DT),etc.The receiver operating characteristic(ROC)curves were calculated for each model.Results:DT,LR,and SVM models all performed well in different sequences.The SVM model based on T2WI images had the best performance,with area under the curve(AUC)of 0.98,0.98,and 0.72 for the training,test,and external validation sets,respectively,and with accuracies of 96%,67%,and 61%,sensitivities of 88%,67%,and 55%,and specificities of 100%,67%,and 78%,respectively.Conclusions:MRI-based radiomics is valuable in identifying mediastinal lymph node metastasis in NSCLC,with the SVM model based on T2WI images showing the best performance.

non-small cell lung cancerlymph node metastasisprediction modelradiomicmachine learningmagnetic resonance imaging

曹瑕尹、李蕊、王婉琼、薛颖、江建芹、崔磊

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南通大学第二附属医院放射科,南通 226006

南通市中医院放射科,南通 226007

盐城市第一人民医院放射科,盐城 224006

非小细胞肺癌 淋巴结转移 预测模型 影像组学 机器学习 磁共振成像

2022年南通市科技局指导性项目-社会民生科技计划项目2022年南通市卫生健康委员会科研课题面上项目

MSZ2022046MS2022034

2024

磁共振成像
中国医院协会 首都医科大学附属北京天坛医院

磁共振成像

CSTPCD北大核心
影响因子:1.38
ISSN:1674-8034
年,卷(期):2024.15(4)
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