非小细胞肺癌EGFR突变状态预测影像结构化特征数据集

整合苏州大学附属第二医院肿瘤诊疗中心非小细胞肺癌患者的影像学结构化特征，通过数据预处理、构建端到端的深度学习模型，用以预测EGFR突变状态，为临床诊疗过程中优势人群筛选提供有力工具，最终实现医疗资源的合理分配，包括基本临床特征、影像一阶、二阶特征等。

This dataset integrates structured imaging features of patients with non-small cell lung cancer (NSCLC) from the Cancer Diagnosis and Treatment Center of the Second Affiliated Hospital of Soochow University. The dataset includes basic clinical characteristics, first-order and second-order imaging features, and other relevant data. By performing data preprocessing and constructing an end-to-end deep learning model, this resource is designed to predict EGFR mutation status, serving as a powerful tool for screening optimal patient subgroups in clinical diagnosis and treatment, and ultimately promoting the rational allocation of medical resources.

搜集汇总

数据集介绍

特点

该数据集整合了非小细胞肺癌患者的影像学结构化特征，通过深度学习模型预测EGFR突变状态，应用于临床决策、靶向治疗开发和优势人群筛选，旨在优化医疗资源的分配。