文献类型：期刊文献

英文题名：Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study

作者：Xu, Aqiao[1];Chu, Xiufeng[2];Zhang, Shengjian[3];Zheng, Jing[1];Shi, Dabao[1];Lv, Shasha[1];Li, Feng[4];Weng, Xiaobo[1]

机构：[1]Shaoxing Univ, Shaoxing Cent Hosp, Dept Radiol, Cent Hosp, Shaoxing, Peoples R China;[2]Shaoxing Univ, Shaoxing Cent Hosp, Dept Surg, Cent Hosp, Shaoxing, Peoples R China;[3]Fudan Univ, Dept Radiol, Shanghai Canc Ctr, Shanghai, Peoples R China;[4]Beijing Deepwise & League Doctor Philosophy PHD T, Res & Dev Ctr, Dept Res Collaborat, Beijing, Peoples R China

年份：2022

卷号：12

外文期刊名：FRONTIERS IN ONCOLOGY

收录：SCI-EXPANDED(收录号：WOS:000805622100001)、、Scopus(收录号：2-s2.0-85131678644)、WOS

基金：Funding This work was supported by the Medical and Health Research Project of Zhejiang Province (Grant No. 2021KY1161, 2022KY1316); Zhejiang Province Chinese Medicine Science Research Fund Project (Grant No. 2021ZA138); and institution from Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City.

语种：英文

外文关键词：breast cancer; radiomics; omics analysis; LASSO regression algorithm; infiltration

外文摘要：ObjectiveTo investigate the feasibility of radiomics in predicting molecular subtype of breast invasive ductal carcinoma (IDC) based on dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). MethodsA total of 303 cases with pathologically confirmed IDC from January 2018 to March 2021 were enrolled in this study, including 223 cases from Fudan University Shanghai Cancer Center (training/test set) and 80 cases from Shaoxing Central Hospital (validation set). All the cases were classified as HR+/Luminal, HER2-enriched, and TNBC according to immunohistochemistry. DCE-MRI original images were treated by semi-automated segmentation to initially extract original and wavelet-transformed radiomic features. The extended logistic regression with least absolute shrinkage and selection operator (LASSO) penalty was applied to identify the optimal radiomic features, which were then used to establish predictive models combined with significant clinical risk factors. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis were adopted to evaluate the effectiveness and clinical benefit of the models established. ResultsOf the 223 cases from Fudan University Shanghai Cancer Center, HR+/Luminal cancers were diagnosed in 116 cases (52.02%), HER2-enriched in 71 cases (31.84%), and TNBC in 36 cases (16.14%). Based on the training set, 788 radiomic features were extracted in total and 8 optimal features were further identified, including 2 first-order features, 1 gray-level run length matrix (GLRLM), 4 gray-level co-occurrence matrices (GLCM), and 1 3D shape feature. Three multi-class classification models were constructed by extended logistic regression: clinical model (age, menopause, tumor location, Ki-67, histological grade, and lymph node metastasis), radiomic model, and combined model. The macro-average areas under the ROC curve (macro-AUC) for the three models were 0.71, 0.81, and 0.84 in the training set, 0.73, 0.81, and 0.84 in the test set, and 0.76, 0.82, and 0.83 in the validation set, respectively. ConclusionThe DCE-MRI-based radiomic features are significant biomarkers for distinguishing molecular subtypes of breast cancer noninvasively. Notably, the classification performance could be improved with the fusion analysis of multi-modal features.