Ye Guanchao, Zhang Chi, Zhuang Yuzhou, Liu Hong, Song Enmin, Li Kuo, Liao Yongde
Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Transl Oncol. 2024 Jun;44:101922. doi: 10.1016/j.tranon.2024.101922. Epub 2024 Mar 29.
To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing the occult lymph node metastasis (OLNM) status in clinical stage IA lung adenocarcinoma.
A cohort of 473 cases of lung adenocarcinomas from two hospitals was included, with 404 cases allocated to the training cohort and 69 cases to the testing cohort. Clinical characteristics and semantic features were collected, and radiomics features were extracted from the computed tomography (CT) images. Additionally, deep transfer learning (DTL) features were generated using RseNet50. Predictive models were developed using the logistic regression (LR) machine learning algorithm. Moreover, gene analysis was conducted on RNA sequencing data from 14 patients to explore the underlying biological basis of deep learning radiomics scores.
The training and testing cohorts achieved AUC values of 0.826 and 0.775 for the clinical model, 0.865 and 0.801 for the radiomics model, 0.927 and 0.885 for the DTL-radiomics model, and 0.928 and 0.898 for the nomogram model. The nomogram model demonstrated superiority over the clinical model. The decision curve analysis (DCA) revealed a net benefit in predicting OLNM for all models. The investigation into the biological basis of deep learning radiomics scores identified an association between high scores and pathways related to tumor proliferation and immune cell infiltration in the microenvironment.
The nomogram model, incorporating clinical-semantic features, radiomics, and DTL features, exhibited promising performance in predicting OLNM. It has the potential to provide valuable information for non-invasive lymph node staging and individualized therapeutic approaches.
评估深度学习影像组学列线图在鉴别临床ⅠA期肺腺癌隐匿性淋巴结转移（OLNM）状态中的有效性。
纳入来自两家医院的473例肺腺癌病例，其中404例分配至训练队列，69例分配至测试队列。收集临床特征和语义特征，并从计算机断层扫描（CT）图像中提取影像组学特征。此外，使用RseNet50生成深度迁移学习（DTL）特征。采用逻辑回归（LR）机器学习算法建立预测模型。此外，对14例患者的RNA测序数据进行基因分析，以探索深度学习影像组学评分的潜在生物学基础。
临床模型在训练队列和测试队列中的AUC值分别为0.826和0.775，影像组学模型分别为0.865和0.801，DTL-影像组学模型分别为0.927和0.885，列线图模型分别为0.928和0.898。列线图模型显示出优于临床模型的性能。决策曲线分析（DCA）显示所有模型在预测OLNM方面均有净获益。对深度学习影像组学评分生物学基础的研究确定了高分与肿瘤增殖和微环境中免疫细胞浸润相关通路之间的关联。
结合临床语义特征、影像组学和DTL特征的列线图模型在预测OLNM方面表现出良好的性能。它有可能为非侵入性淋巴结分期和个体化治疗方法提供有价值的信息。
