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Predicting the pathological status of mammographic microcalcifications through a radiomics approach
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作者 Min Li Liyu Zhu +3 位作者 Guangquan Zhou Jianan He Yanni Jiang Yang Chen 《Intelligent Medicine》 2021年第3期95-103,共9页
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 4... 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. 展开更多
关键词 Nonpalpable microcalcifications Radiomics Machine learning
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X(2239) and η(2225) as hidden-strange molecular states from Λ Λ interaction
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作者 朱俊韬 刘奕 +2 位作者 陈殿勇 姜龙玉 何军 《Chinese Physics C》 SCIE CAS CSCD 2020年第12期29-37,共9页
In this work,we propose the possible assignment of the newly observed X(2239),as well asη(2225),as a molecular state from the interaction of a baryonΛand an antibaryonΛ¯.With the help of effective Lagrangians,... In this work,we propose the possible assignment of the newly observed X(2239),as well asη(2225),as a molecular state from the interaction of a baryonΛand an antibaryonΛ¯.With the help of effective Lagrangians,theΛΛ¯interaction is described within the one-boson-exchange model withη,η′,ω,ϕ,andσexchanges considered.After inserting the potential kernel into the quasipotential Bethe-Salpeter equation,the bound states from theΛΛ¯interaction can be studied by searching for the pole of the scattering amplitude.Two loosely bound states with spin parities IG(JPC)=0+(0−+)and 0−(1−−)appear near the threshold with almost the same parameter.The 0−(1−−)state can be assigned to X(2239)observed at BESⅢ,which is very close to theΛΛ¯threshold.The scalar mesonη(2225)can be interpreted as a 0+(0−+)state from theΛΛ¯interaction.The annihilation effect is also discussed through a coupled-channel calculation plus a phenomenological optical potential.It provides large widths to two bound states produced from theΛΛ¯interaction.The mass of the 1−state is slightly larger than the mass of the 0−state after including the annihilation effect,which is consistent with our assignment of these two states as X(2239)andη(2225),respectively.The results suggest that further investigation is required to understand the structures near theΛΛ¯threshold,such as X(2239),η(2225),and X(2175). 展开更多
关键词 hadronic molcular state X(2239) X(2225)
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