Predicting the pathological status of mammographic microcalcifications through a radiomics approach  

作　　者：Min Li Liyu Zhu Guangquan Zhou Jianan He Yanni Jiang Yang Chen 

机构地区：[1]School of Biological Science and Medical Engineering,Southeast University,Nanjing,Jiangsu 210096,China [2]Department of Radiology,the First Affiliated Hospital of Nanjing Medical University,Nanjing,Jiangsu 210029,China [3]Department of Radiology,the Second Affiliated Hospital of Nantong University,Nantong,Jiangsu 226001,China [4]The State Key Laboratory of Bioelectronics,Southeast University,Nanjing,Jiangsu 210096,China [5]Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing,School of Computer Science and Engineering,Southeast University,Nanjing,Jiangsu 210096,China [6]The Laboratory of Image Science and Technology,Key Laboratory of Ministry of Education,School of Computer Science and Engineering,Southeast University,Nanjing,Jiangsu 210096,China

出　　处：《Intelligent Medicine》2021年第3期95-103,共9页智慧医学（英文）

基　　金：supported in part by the State’s Key Project of Research and Development Plan(Grant Nos.2017YFC0109202 and 2017YFA0104302);in part by the National Natural Science Foundation(Grant No.61871117);in part by Science and Technology Program of Guangdong(Grant No.2018B030333001).

摘　　要：Objective The study aimed to develop a machine learning(ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications(MCs).Methods We enrolled 463 digital mammographical view images from 260 consecutive patients detected with non-palpable MCs and BI-RADS scored at 4(training cohort,n=428;independent testing cohort,n=35)in the First Affiliated Hospital of Nanjing Medical University between September 2010 and January 2019.Subsequently,837 textures and 9 shape features were subsequently extracted from each view and finally selected by an XGBoostembedded recursive feature elimination technique(RFE),followed by four machine learning-based classifiers to build the radiomics signature.Results Ten radiomic features constituted a malignancy-related signature for breast MCs as logistic regression(LR)and support vector machine(SVM)yielded better positive predictive value(PPV)/sensitivity(SE),0.904(95%CI,0.865–0.949)/0.946(95%CI,0.929–0.977)and 0.891(95%CI,0.822–0.939)/0.939(95%CI,0.907–0.973)respectively,outperforming their negative predictive value(NPV)/specificity(SP)from 10-fold crossvalidation(10FCV)of the training cohort.The optimal prognostic model was obtained by SVM with an area under the curve(AUC)of 0.906(95%CI,0.834–0.969)and accuracy(ACC)0.787(95%CI,0.680–0.855)from 10FCV against AUC 0.810(95%CI,0.760–0.960)and ACC 0.800 from the testing cohort.Conclusion The proposed radiomics signature dependens on a set of ML-based advanced computational algorithms and is expected to identify pathologically cancerous cases from mammographically undecipherable MCs and thus offer prospective clinical diagnostic guidance.

关 键 词：Nonpalpable microcalcifications Radiomics Machine learning 

分 类 号：R735.9[医药卫生—肿瘤]