本研究旨在构建基于放射组学和深度学习的渐进二元分类模型，以预测表皮生长因子受体 (EGFR) 和 TP53 突变的存在，并评估模型识别适合 TKI 靶向治疗和治疗的患者的能力。回顾性纳入我院收治的267例接受基因检测和胸部平扫的肺腺癌患者。收集临床信息和成像特征，并对所有定义的感兴趣区域 (ROI) 进行高通量特征采集。我们选择特征并构建临床模型、放射组学模型、深度学习模型和集成模型来分别预测所有患者的 EGFR 状态和 EGFR 阳性患者的 TP53 状态。每种模型的有效性和可靠性用曲线下面积（AUC）、敏感性、特异性、准确性、精密度和F1评分表示。我们针对2种不同的二分法构建了7种模型，即临床模型、放射组学模型模型、DL 模型、rad-clin 模型、DL-clin 模型、DL-rad 模型和 DL-rad-clin 模型。对于EGFR-和EGFR，DL-rad-clin模型的AUC值最高，为0.783（95% CI：0.677-0.889），其次是rad-clin模型、DL-clin模型和DL-rad模型。在EGFR突变组中，对于TP53-和TP53，rad-clin模型的AUC值最高，为0.811（95% CI：0.651-0.972），其次是DL-rad-clin模型和DL-rad我们基于放射组学和深度学习的渐进二元分类模型可以为 TKI 应答者和预后不良者的临床识别提供良好的参考和补充。版权所有 © 2024 作者。由 Wolters Kluwer Health, Inc. 出版

This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor (EGFR) and TP53 mutations and to assess the models' capacities to identify patients who are suitable for TKI-targeted therapy and those with poor prognoses.A total of 267 patients with lung adenocarcinomas who underwent genetic testing and noncontrast chest computed tomography from our hospital were retrospectively included. Clinical information and imaging characteristics were gathered, and high-throughput feature acquisition on all defined regions of interest (ROIs) was carried out. We selected features and constructed clinical models, radiomics models, deep learning models, and ensemble models to predict EGFR status with all patients and TP53 status with EGFR-positive patients, respectively. The validity and reliability of each model were expressed as the area under the curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score.We constructed 7 kinds of models for 2 different dichotomies, namely, the clinical model, the radiomics model, the DL model, the rad-clin model, the DL-clin model, the DL-rad model, and the DL-rad-clin model. For EGFR- and EGFR+, the DL-rad-clin model got the highest AUC value of 0.783 (95% CI: 0.677-0.889), followed by the rad-clin model, the DL-clin model, and the DL-rad model. In the group with an EGFR mutation, for TP53- and TP53+, the rad-clin model got the highest AUC value of 0.811 (95% CI: 0.651-0.972), followed by the DL-rad-clin model and the DL-rad model.Our progressive binary classification models based on radiomics and deep learning may provide a good reference and complement for the clinical identification of TKI responders and those with poor prognoses.Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.