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作者

李云

作者单位

山东第一医科大学附属肿瘤医院

会议名称

山东中医药学会医学影像专业委员会成立大会暨第一次学术会议

会议录

山东中医药学会医学影像专业委员会成立大会暨第一次学术会议论文集

召开年

2022-10-29

关键词

mammography image feature deep learning clinical prediction radiomics

摘要

Background:Breast cancer is one of the leading causes of death in female cancer patients.The disease can be detected early using Mammography,an effective X-ray imaging technology.The most important step in mammography is the classification of mammogram patches as benign or malignant.Classically,benign or malignant breast tumors are diagnosed by ra-diologists' interpretation of mammograms based on clinical parameters.However,because masses are heterogeneous,clinical pa-rameters supply limited inf...更多

Background:Breast cancer is one of the leading causes of death in female cancer patients.The disease can be detected early using Mammography,an effective X-ray imaging technology.The most important step in mammography is the classification of mammogram patches as benign or malignant.Classically,benign or malignant breast tumors are diagnosed by ra-diologists' interpretation of mammograms based on clinical parameters.However,because masses are heterogeneous,clinical pa-rameters supply limited information on mammography mass.Therefore,this study aimed to predict benign or malignant breast masses using a combination of image biomarkers and clinical parameters.Methods:We trained a deep learningfusion network of VGG16 and Inception-V3 network in 5996 mammography im-ages from the training cohort;DL features were extracted from the second fully connected layer of the DL fusion network.We then developed a combined model incorporating DL features,hand-crafted features,and clinical parameters to predict benign or malignant breast masses.The prediction performance was compared between clinical parameters and the combination of the above features.The strengths of the clinical model and the combined model were subsequently validated in a test cohortand an external validation cohort,respectively.Results:Extracted features comprised 30 hand-crafted features,27 DL features,and 5 clinical features.The model combining the three feature types yielded the best performance in predicting be-nign or malignant massesin the test cohort.A significant difference in the predictive performance between the combined model and the clinical model was observed in an independent external validation cohort.Conclusion:The prediction of benign or malignant breast masses improves when image biomarkers and clinical parameters are combined;the combined model was more robust than clinical parameters alone.收起

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会议论文