【摘要】 为了克服横断面型数据的不完整性，提出了一种基于合成分析的早期肺癌风险预测模型。收集247组来自医院的患者数据进行实验，使用合成分析法结合肺结节良恶性判断结果、吸烟史、家族病史等肺癌危险因素建立风险评估模型。实验表明，未纳入影像学良恶性分类结果的合成分析模型的准确率为83.20%，而纳入影像学良恶性分类结果的合成分析模型的准确率达到87.40%。结合基于CT影像的肺结节良恶性判断结果能有效提高早期肺癌风险评估模型的准确性。更多还原

【Abstract】 The incidence and mortality of lung cancer have always been high. The establishment of an effective method to predict the risk of early lung cancer is of great significance for improving the survival rate of lung cancer patients. To overcome the incompleteness of cross-sectional data, an early lung cancer risk prediction model was proposed based on synthetic analysis. 247 groups of cases from hospitals were collected for experiments, and a risk assessment model was established using synthetic analysis method combined with lung nodule benign and malignant judgment results, smoking history,family history and other lung cancer risk factors. The accuracy rate of the synthetic analysis model that did not include the imaging benign and malignant classification results was 83.20 %, and the accuracy of the synthetic analysis model that included the imaging benign and malignant classification results reached 87.40 %. Combining the results of benign and malignant lung nodules based on CT images can effectively improve the accuracy of early lung cancer risk assessment models.更多还原