目的探讨1.5 T磁共振(MR)高扩散敏感系数(b值)弥散成像(DWI)对早期前列腺癌的诊断效能,为临床提供参考。方法选取2019年1月至2022年1月琼海市人民医院收治的182例疑似早期前列腺癌患者为研究对象进行回顾性分析,均行1.5 T MR多b值DWI检...目的探讨1.5 T磁共振(MR)高扩散敏感系数(b值)弥散成像(DWI)对早期前列腺癌的诊断效能,为临床提供参考。方法选取2019年1月至2022年1月琼海市人民医院收治的182例疑似早期前列腺癌患者为研究对象进行回顾性分析,均行1.5 T MR多b值DWI检查、手术或穿刺活检病理检查,以病理结果为金标准,观察早期前列腺癌的MR表现。比较在不同b值下早期前列腺癌与前列腺增生、前列腺炎、前列腺癌周围正常组织的弥散系数(ADC);观察不同b值时DWI对早期前列腺癌诊断的敏感度、特异度、准确度。结果早期前列腺癌的T2加权成像(T2WI)及ADC图主要表现为低或稍低信号,DWI主要表现为局灶性或弥漫性高信号。早期前列腺癌组织在b值取800 s/mm^(2)、1000 s/mm^(2)、1200 s/mm^(2)、1500 s/mm^(2)下的ADC值均低于前列腺增生、前列腺炎、前列腺癌周围正常组织(P<0.05)。b值=1500 s/mm^(2)时敏感度、特异度、准确度为89.47%、83.20%、85.16%,高于其他b值时的敏感度、特异度、准确度(P<0.05)。结论早期前列腺癌的不同b值下ADC值均低于前列腺增生、前列腺炎、前列腺癌周围正常组织,1.5 T MR高b值(1500 s/mm^(2))DWI诊断早期前列腺癌的效能较高。展开更多
Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were hi...Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were high-throughput re-evaluated from composition variations and nanoindentation data of diffusion couples.Then,the Ti-(22±0.5)at.%Nb-(30±0.5)at.%Zr-(4±0.5)at.%Cr(TNZC) alloy with a single body-centered cubic(BCC) phase was screened in an interactive loop.The experimental results exhibited a relatively low Young's modulus of(58±4) GPa,high nanohardness of(3.4±0.2) GPa,high microhardness of HV(520±5),high compressive yield strength of(1220±18) MPa,large plastic strain greater than 30%,and superior dry-and wet-wear resistance.This work demonstrates that ML combined with high-throughput analytic approaches can offer a powerful tool to accelerate the design of multicomponent Ti alloys with desired properties.Moreover,it is indicated that TNZC alloy is an attractive candidate for biomedical applications.展开更多
基金the financial supports from the National Key Research and Development Program of China (No. 2022YFB3707501)the National Natural Science Foundation of China (No. 51701083)+1 种基金the GDAS Project of Science and Technology Development, China (No. 2022GDASZH2022010107)the Guangzhou Basic and Applied Basic Research Foundation, China (No. 202201010686)。
文摘Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were high-throughput re-evaluated from composition variations and nanoindentation data of diffusion couples.Then,the Ti-(22±0.5)at.%Nb-(30±0.5)at.%Zr-(4±0.5)at.%Cr(TNZC) alloy with a single body-centered cubic(BCC) phase was screened in an interactive loop.The experimental results exhibited a relatively low Young's modulus of(58±4) GPa,high nanohardness of(3.4±0.2) GPa,high microhardness of HV(520±5),high compressive yield strength of(1220±18) MPa,large plastic strain greater than 30%,and superior dry-and wet-wear resistance.This work demonstrates that ML combined with high-throughput analytic approaches can offer a powerful tool to accelerate the design of multicomponent Ti alloys with desired properties.Moreover,it is indicated that TNZC alloy is an attractive candidate for biomedical applications.
