磁共振成像2024，Vol.15Issue(4)：78-87,10.DOI:10.12015/issn.1674-8034.2024.04.013
多模态影像组学列线图术前预测乳腺浸润性导管癌腋窝淋巴结转移的价值
Value of multimodal radiomics nomogram in predicting axillary lymph node metastasis in invasive ductal carcinoma of the breast before surgery
摘要
Abstract
Objective:To investigate the value of multimodal radiomics nomogram in predicting axillary lymph node(ALN)metastasis in invasive ductal carcinoma of the breast before surgery.Materials and Methods:The clinical and imaging data of 224 patients with invasive ductal carcinoma of the breast confirmed by surgical pathology in our hospital from January 2019 to June 2023 were retrospectively collected.Firstly,the maximum level of the lesion of the T2WI image and the second phase of dynamic contrast-enhanced MRI(DCE-MRI)and the mammography(MG)of the same lesion were selected to delineate the region of interest,and the characteristics of the lesion area of interest were extracted.According to the ratio of 7∶3,the samples were randomly divided into 156 cases in the training set and 68 cases in the test set,and the feature dimensionality reduction screening was carried out by least absolute shrinkage and selection operator(LASSO)regression,5 kinds of machine learning classifiers[support vector machine(SVM)、K nearest neighbors(KNN)、extreme gradient boosting(XGBoost)、logistic regression(LR)、randomforest(RF)]were selected to build a multimodal radiomics model,and the classifier with the best prediction performance was selected to establish MRI and mammography models.Univariate logistic regression was used to screen clinical high-risk factors and construct a clinical model.Finally,Radiomics score combined with clinical high-risk factors was selected to construct an electromics nomogram model.The receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to evaluate the efficacy of the model in predicting the ALN status of breast cancer patients,and the clinical practicability of the prediction model was evaluated by using the fitting ability of the calibration curve to evaluate the decision curve.Results:Finally,14 optimal radiomics features were obtained.The AUC value of the five machine learning classifiers in the test set ranged from 0.764-0.864,and the AUC value of SVM was the highest(0.864).Lymph node palpation(P<0.001)and MRI_ALN(P=0.005)were independent risk factors for ALN metastasis.The AUC,sensitivity,specificity and accuracy of the nomogram model training set were 0.941,90.7%,88.9%and 88.5%,respectively.The test sets were 0.926,84.4%,86.1%,and 85.3%,respectively.Conclusions:The nomogram model has important value in predicting ALN status before surgery,and can assist in the formulation of scientific and effective clinical diagnosis and treatment plans.关键词
乳腺癌/浸润性导管癌/腋窝淋巴结/影像组学/列线图/钼靶检查/磁共振成像Key words
breast cancer/invasive ductal carcinoma/axillary lymph nodes/radiomics/nomograms/mammography/magnetic resonance imaging分类
临床医学引用本文复制引用
张舒妮,赵楠楠,李阳,朱芸,杨静茹,张澳琪,顾一泓,谢宗玉..多模态影像组学列线图术前预测乳腺浸润性导管癌腋窝淋巴结转移的价值[J].磁共振成像,2024,15(4):78-87,10.基金项目
Natural Science Foundation of Anhui Provincial Department of Education(No.2022AH051473) （No.2022AH051473）
Bengbu Medical College Level Project(No.Byycx23096). 安徽省教育厅自然科学基金重点项目(编号:2022AH051473) （No.Byycx23096）
蚌埠医学院校级课题项目(编号:Byycx23096) （编号:Byycx23096）
