PDF(Pubmed)
Abstract：
UNASSIGNED: This study aimed to develop quantitative feature-based models from histopathological images to assess aurora kinase A (AURKA) expression and predict the prognosis of patients with lung adenocarcinoma (LUAD).
UNASSIGNED: A dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival.
UNASSIGNED: High AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257-2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071-2.438, P = 0.022).
UNASSIGNED: Pathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.
UNASSIGNED: A dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival.
UNASSIGNED: High AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257-2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071-2.438, P = 0.022).
UNASSIGNED: Pathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.
摘要：
■本研究旨在从组织病理学图像中开发基于定量特征的模型，以评估极光激酶A（AURKA）的表达并预测肺腺癌（LUAD）患者的预后。
■LUAD患者的数据集来自癌症基因组图谱（TCGA），其中包含有关临床特征的信息，RNA测序和组织病理学图像。TCGA-LUAD队列随机分为训练组(n=229)和测试组(n=98)。我们使用计算方法从LUAD患者的组织病理学切片中提取定量图像特征，在训练集中构建了AURKA表达的预测模型，并估计它们在测试集中的预测性能。使用Cox比例风险模型来评估由该模型产生的病理评分(PS)是否独立地预测LUAD存活。
■高AURKA表达是LUAD患者总生存期（OS）的独立危险因素（风险比=1.816，95%置信区间=1.257-2.623，P=0.001）。基于组织病理学图像特征的模型对AURKA表达具有显著的预测价值:训练集和验证集中受试者工作特征曲线的曲线下面积分别为0.809和0.739。决策曲线分析表明该模型具有临床实用性。高PS和低PS患者的生存率不同(P=0.019)。多因素分析显示PS是LUAD的独立预后因素(风险比=1.615,95%置信区间=1.071-2.438,P=0.022)。
■基于机器学习的Pathomics模型可以准确预测AURKA表达，并且该模型生成的PS可以预测LUAD预后。
■LUAD患者的数据集来自癌症基因组图谱（TCGA），其中包含有关临床特征的信息，RNA测序和组织病理学图像。TCGA-LUAD队列随机分为训练组(n=229)和测试组(n=98)。我们使用计算方法从LUAD患者的组织病理学切片中提取定量图像特征，在训练集中构建了AURKA表达的预测模型，并估计它们在测试集中的预测性能。使用Cox比例风险模型来评估由该模型产生的病理评分(PS)是否独立地预测LUAD存活。
■高AURKA表达是LUAD患者总生存期（OS）的独立危险因素（风险比=1.816，95%置信区间=1.257-2.623，P=0.001）。基于组织病理学图像特征的模型对AURKA表达具有显著的预测价值:训练集和验证集中受试者工作特征曲线的曲线下面积分别为0.809和0.739。决策曲线分析表明该模型具有临床实用性。高PS和低PS患者的生存率不同(P=0.019)。多因素分析显示PS是LUAD的独立预后因素(风险比=1.615,95%置信区间=1.071-2.438,P=0.022)。
■基于机器学习的Pathomics模型可以准确预测AURKA表达，并且该模型生成的PS可以预测LUAD预后。
