Huang Xiaoxin, Huang Xiaoxiao, Wang Kui, Bai Haosheng, Lu Xiuxian, Jin Guanqiao
Medical Imaging Center, Guangxi Medical University Cancer Hospital, Liangyu Avenue, Wuxiang New District, Nanning City, Guangxi Zhuang Autonomous Region, 530021, China.
Department of Radiology, Jiangbin Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
BMC Med Imaging. 2025 Jul 1;25(1):225. doi: 10.1186/s12880-025-01759-1.
Occult lymph node metastasis (OLNM) refers to lymph node involvement that remains undetectable by conventional imaging techniques, posing a significant challenge in the accurate staging of lung adenocarcinoma. This study aims to investigate the potential of combining 2.5D deep learning radiomics with clinical data to predict OLNM in lung adenocarcinoma.
Retrospective contrast-enhanced CT images were collected from 1,099 patients diagnosed with lung adenocarcinoma across two centers. Multivariable analysis was performed to identify independent clinical risk factors for constructing clinical signatures. Radiomics features were extracted from the enhanced CT images to develop radiomics signatures. A 2.5D deep learning approach was used to extract deep learning features from the images, which were then aggregated using multi-instance learning (MIL) to construct MIL signatures. Deep learning radiomics (DLRad) signatures were developed by integrating the deep learning features with radiomic features. These were subsequently combined with clinical features to form the combined signatures. The performance of the resulting signatures was evaluated using the area under the curve (AUC).
The clinical model achieved AUCs of 0.903, 0.866, and 0.785 in the training, validation, and external test cohorts The radiomics model yielded AUCs of 0.865, 0.892, and 0.796 in the training, validation, and external test cohorts. The MIL model demonstrated AUCs of 0.903, 0.900, and 0.852 in the training, validation, and external test cohorts, respectively. The DLRad model showed AUCs of 0.910, 0.908, and 0.875 in the training, validation, and external test cohorts. Notably, the combined model consistently outperformed all other models, achieving AUCs of 0.940, 0.923, and 0.898 in the training, validation, and external test cohorts.
The integration of 2.5D deep learning radiomics with clinical data demonstrates strong capability for OLNM in lung adenocarcinoma, potentially aiding clinicians in developing more personalized treatment strategies.
隐匿性淋巴结转移（OLNM）是指常规成像技术无法检测到的淋巴结受累情况，这给肺腺癌的准确分期带来了重大挑战。本研究旨在探讨将2.5D深度学习放射组学与临床数据相结合预测肺腺癌OLNM的潜力。
回顾性收集了两个中心1099例诊断为肺腺癌患者的增强CT图像。进行多变量分析以确定构建临床特征的独立临床危险因素。从增强CT图像中提取放射组学特征以建立放射组学特征。采用2.5D深度学习方法从图像中提取深度学习特征，然后使用多实例学习（MIL）进行汇总以构建MIL特征。通过将深度学习特征与放射组学特征整合来开发深度学习放射组学（DLRad）特征。随后将这些特征与临床特征相结合以形成联合特征。使用曲线下面积（AUC）评估所得特征的性能。
临床模型在训练、验证和外部测试队列中的AUC分别为0.903、0.866和0.785。放射组学模型在训练、验证和外部测试队列中的AUC分别为0.865、0.892和0.796。MIL模型在训练、验证和外部测试队列中的AUC分别为0.903、0.900和0.852。DLRad模型在训练、验证和外部测试队列中的AUC分别为0.910、0.908和0.875。值得注意的是，联合模型始终优于所有其他模型，在训练、验证和外部测试队列中的AUC分别为0.940、0.923和0.898。
2.5D深度学习放射组学与临床数据的整合显示出在肺腺癌OLNM预测方面的强大能力，可能有助于临床医生制定更个性化的治疗策略。
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