Objectives:To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma(GA).Methods:This retrospective study enrolled 592 patients with clinicopathologic...Objectives:To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma(GA).Methods:This retrospective study enrolled 592 patients with clinicopathologically confirmed GA(low-grade:n=154;high-grade:n=438)from January 2008 to March 2018 who were divided into training(n=450)and validation(n=142)sets according to the time of computed tomography(CT)examination.Radiomic features were extracted from the portal venous phase CT images.The Mann-Whitney U test and the least absolute shrinkage and selection operator(LASSO)regression model were used for feature selection,data dimension reduction and radiomics signature construction.Multivariable logistic regression analysis was applied to develop the prediction model.The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram.The performance of the nomogram was assessed with respect to its calibration and discrimination.Results:A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA(P<0.001 for both training and validation sets).A nomogram including the radiomics signature and tumor location as predictors was developed.The model showed both good calibration and good discrimination,in which C-index in the training set,0.752[95%confidence interval(95%CI):0.701-0.803];C-index in the validation set,0.793(95%CI:0.711-0.874).Conclusions:This study developed a radiomics nomogram that incorporates tumor location and radiomics signatures,which can be useful in facilitating preoperative individualized prediction of histologic grade of GA.展开更多
肌球蛋白重链3(myosin heavy chain 3,MYH3)基因编码胚胎型肌球蛋白重链蛋白,控制肌肉的牵引滑动。MYH3基因是肌肉分化的重要标志基因,能够调控肌肉发育及能量代谢,在动物整个肌肉发育过程中均发挥重要作用。MYH3基因在不同物种间高度保...肌球蛋白重链3(myosin heavy chain 3,MYH3)基因编码胚胎型肌球蛋白重链蛋白,控制肌肉的牵引滑动。MYH3基因是肌肉分化的重要标志基因,能够调控肌肉发育及能量代谢,在动物整个肌肉发育过程中均发挥重要作用。MYH3基因在不同物种间高度保守,且在动物体内多组织中均有表达,在胚胎期肌肉组织和肌肉再生过程中表达量较高。它受转录因子、microRNA、lncRNA及环境营养因子等多种因素影响,也可调控其他基因的功能。MYH3基因突变可以改变TGF-β信号通路和MAPK信号通路相关蛋白的磷酸化水平;影响ATP酶活性,使ATP水解时间延长,延长横桥周期;影响肌肉的能量代谢,最终引发肌肉能量代谢疾病。MYH3基因拷贝数变化、突变或表达量变化与动物的体尺、胴体重、屠宰重、生长性能具有显著的相关性。MYH3基因在大理石花纹高、肌内脂肪高的肌肉组织中表达量高,被认为是影响动物肌肉嫩度、剪切力和肉色红度的重要候选基因。MYH3基因的高表达与骨骼肌中氧化Ⅰ型肌纤维的含量、肌纤维直径和慢肌纤维含量有关。作者介绍了MYH3基因的基本结构特点,指出了其与肌肉组织发育及相关影响因子之间的调控作用,阐述了MYH3基因与动物肌肉能量代谢、生长性能和肉品质之间的关系,为进一步研究MYH3基因与肌肉发育调控和肉质性能改良提供参考。展开更多
Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ...Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification.Methods: We retrospectively identified 161 consecutive patients with stage Ⅰ-Ⅲ CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction.Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio(HR)=6.670;95% confidence interval(95% CI): 3.433-12.956;P<0.001), external validation cohort 1(HR=2.866;95% CI: 1.646-4.990;P<0.001) and external validation cohort 2(HR=3.342;95% CI: 1.289-8.663;P=0.002).Incorporating the EcoRad signature into the prediction model presented a higher prediction ability(P<0.001) with respect to the C-index(0.813, 95% CI: 0.804-0.822 in the training cohort;0.758, 95% CI: 0.751-0.765 in the external validation cohort 1;and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis(TNM) system, as well as a better calibration,improved reclassification and superior clinical usefulness.Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage Ⅰ-Ⅲ CRC patients.展开更多
基金supported by the National Key Research and Development Program of China(No.2017YFC 1309100)the National Science Fund for Distinguished Young Scholars(No.81925023)the National Natural Science Foundation of China(No.82071892,81771912,81901910)。
文摘Objectives:To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma(GA).Methods:This retrospective study enrolled 592 patients with clinicopathologically confirmed GA(low-grade:n=154;high-grade:n=438)from January 2008 to March 2018 who were divided into training(n=450)and validation(n=142)sets according to the time of computed tomography(CT)examination.Radiomic features were extracted from the portal venous phase CT images.The Mann-Whitney U test and the least absolute shrinkage and selection operator(LASSO)regression model were used for feature selection,data dimension reduction and radiomics signature construction.Multivariable logistic regression analysis was applied to develop the prediction model.The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram.The performance of the nomogram was assessed with respect to its calibration and discrimination.Results:A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA(P<0.001 for both training and validation sets).A nomogram including the radiomics signature and tumor location as predictors was developed.The model showed both good calibration and good discrimination,in which C-index in the training set,0.752[95%confidence interval(95%CI):0.701-0.803];C-index in the validation set,0.793(95%CI:0.711-0.874).Conclusions:This study developed a radiomics nomogram that incorporates tumor location and radiomics signatures,which can be useful in facilitating preoperative individualized prediction of histologic grade of GA.
文摘肌球蛋白重链3(myosin heavy chain 3,MYH3)基因编码胚胎型肌球蛋白重链蛋白,控制肌肉的牵引滑动。MYH3基因是肌肉分化的重要标志基因,能够调控肌肉发育及能量代谢,在动物整个肌肉发育过程中均发挥重要作用。MYH3基因在不同物种间高度保守,且在动物体内多组织中均有表达,在胚胎期肌肉组织和肌肉再生过程中表达量较高。它受转录因子、microRNA、lncRNA及环境营养因子等多种因素影响,也可调控其他基因的功能。MYH3基因突变可以改变TGF-β信号通路和MAPK信号通路相关蛋白的磷酸化水平;影响ATP酶活性,使ATP水解时间延长,延长横桥周期;影响肌肉的能量代谢,最终引发肌肉能量代谢疾病。MYH3基因拷贝数变化、突变或表达量变化与动物的体尺、胴体重、屠宰重、生长性能具有显著的相关性。MYH3基因在大理石花纹高、肌内脂肪高的肌肉组织中表达量高,被认为是影响动物肌肉嫩度、剪切力和肉色红度的重要候选基因。MYH3基因的高表达与骨骼肌中氧化Ⅰ型肌纤维的含量、肌纤维直径和慢肌纤维含量有关。作者介绍了MYH3基因的基本结构特点,指出了其与肌肉组织发育及相关影响因子之间的调控作用,阐述了MYH3基因与动物肌肉能量代谢、生长性能和肉品质之间的关系,为进一步研究MYH3基因与肌肉发育调控和肉质性能改良提供参考。
基金supported by the National Key R&D Program of China (No. 2021YFF1201003)the Key R&D Program of Guangdong Province, China (No. 2021B0101420006)+2 种基金the National Science Fund for Distinguished Young Scholars (No. 81925023 and 82071892)the National Natural Science Foundation of China (No. 81771912 and 82071892)the National Natural Science Foundation for Young Scientists of China (No. 81701782 and 81901910).
文摘Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification.Methods: We retrospectively identified 161 consecutive patients with stage Ⅰ-Ⅲ CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction.Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio(HR)=6.670;95% confidence interval(95% CI): 3.433-12.956;P<0.001), external validation cohort 1(HR=2.866;95% CI: 1.646-4.990;P<0.001) and external validation cohort 2(HR=3.342;95% CI: 1.289-8.663;P=0.002).Incorporating the EcoRad signature into the prediction model presented a higher prediction ability(P<0.001) with respect to the C-index(0.813, 95% CI: 0.804-0.822 in the training cohort;0.758, 95% CI: 0.751-0.765 in the external validation cohort 1;and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis(TNM) system, as well as a better calibration,improved reclassification and superior clinical usefulness.Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage Ⅰ-Ⅲ CRC patients.
