Qi Hongzhuo, Xuan Qifan, Liu Pingping, An Yunfei, Huang Wenjuan, Miao Shidi, Wang Qiujun, Liu Zengyao, Wang Ruitao
School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.
Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin 150081, China.
Biomedicines. 2024 Aug 15;12(8):1865. doi: 10.3390/biomedicines12081865.
This study investigated the relationship between mediastinal fat and pulmonary nodule status, aiming to develop a deep learning-based radiomics model for diagnosing benign and malignant pulmonary nodules. We proposed a combined model using CT images of both pulmonary nodules and the fat around the chest (mediastinal fat). Patients from three centers were divided into training, validation, internal testing, and external testing sets. Quantitative radiomics and deep learning features from CT images served as predictive factors. A logistic regression model was used to combine data from both pulmonary nodules and mediastinal adipose regions, and personalized nomograms were created to evaluate the predictive performance. The model incorporating mediastinal fat outperformed the nodule-only model, with C-indexes of 0.917 (training), 0.903 (internal testing), 0.942 (external testing set 1), and 0.880 (external testing set 2). The inclusion of mediastinal fat significantly improved predictive performance (NRI = 0.243, < 0.05). A decision curve analysis indicated that incorporating mediastinal fat features provided greater patient benefits. Mediastinal fat offered complementary information for distinguishing benign from malignant nodules, enhancing the diagnostic capability of this deep learning-based radiomics model. This model demonstrated strong diagnostic ability for benign and malignant pulmonary nodules, providing a more accurate and beneficial approach for patient care.
本研究调查了纵隔脂肪与肺结节状态之间的关系，旨在开发一种基于深度学习的放射组学模型，用于诊断良性和恶性肺结节。我们提出了一种结合肺结节和胸部周围脂肪（纵隔脂肪）CT图像的联合模型。来自三个中心的患者被分为训练集、验证集、内部测试集和外部测试集。CT图像的定量放射组学和深度学习特征作为预测因素。使用逻辑回归模型结合来自肺结节和纵隔脂肪区域的数据，并创建个性化列线图以评估预测性能。纳入纵隔脂肪的模型优于仅基于结节的模型，其C指数在训练集中为0.917，在内部测试集中为0.903，在外部测试集1中为0.942，在外部测试集2中为0.880。纳入纵隔脂肪显著提高了预测性能（NRI = 0.243，<0.05）。决策曲线分析表明，纳入纵隔脂肪特征对患者更有益。纵隔脂肪为区分良性和恶性结节提供了补充信息，增强了这种基于深度学习的放射组学模型的诊断能力。该模型对良性和恶性肺结节具有很强的诊断能力，为患者护理提供了一种更准确、更有益的方法。
