Zhang Hao, Li Yuan, Wu Sikai, Peng Yue, Liu Yang, Gao Shugeng
Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Cardiothoracic Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Transl Lung Cancer Res. 2024 Dec 31;13(12):3579-3589. doi: 10.21037/tlcr-24-668. Epub 2024 Dec 27.
Preoperative assessment of lymph node status is critical in managing lung cancer, as it directly impacts the surgical approach and treatment planning. However, in clinical stage I lung adenocarcinoma (LUAD), determining lymph node metastasis (LNM) is often challenging due to the limited sensitivity of conventional imaging modalities, such as computed tomography (CT) and positron emission tomography/CT (PET/CT). This study aimed to establish an effective radiomics prediction model using multicenter data for early assessment of LNM risk in patients with clinical stage I LUAD. The goal is to provide a basis for formulating lymph node dissection strategies before surgery in early-stage lung cancer patients.
A total of 578 patients with LUAD from three medical centers [Cancer Hospital, Chinese Academy of Medical Sciences (CCAM), the First Affiliated Hospital of Chongqing Medical University (1CMU), and Beijing Chao-Yang Hospital (BCYH)] who underwent preoperative chest CT were divided into three groups, the training group (n=336), the testing group (n=167), and the independent validation group (n=75). The records of 1,316 radiomics features of each primary tumor were extracted. The least absolute shrinkage and selection operator (LASSO) analysis and multivariable logistic regression were used to reduce the data dimensionality, select features, and construct the prediction models.
In the training group, the area under the curve (AUC) for the clinical model, radiomics model, and composite model were 0.820, 0.871, and 0.883, respectively. In the testing group, the AUC for the clinical model, radiomics model, and composite model were 0.897, 0.915, and 0.934, respectively. In the validation set, the AUC of the radiomics model was the highest at 0.870, while the composite model and clinical model had AUCs of 0.841 and 0.710, respectively. The results of the Delong test showed that the AUCs of the radiomics model and composite model were significantly higher than those of the clinical model in both the training and validation groups. The decision curve analysis showed that the radiomics nomogram was clinically useful.
This study developed and validated a radiomics prediction model, which enables easy LNM prediction in stage I LUAD patients. This model provides a basis for formulating lymph node dissection strategies before surgery and helps to better determine the tumor node metastasis stage of the early-stage LUAD.
术前评估淋巴结状态对于肺癌治疗至关重要，因为它直接影响手术方式和治疗方案的制定。然而，在临床I期肺腺癌（LUAD）中，由于传统成像方式（如计算机断层扫描（CT）和正电子发射断层扫描/CT（PET/CT））的敏感性有限，确定淋巴结转移（LNM）往往具有挑战性。本研究旨在利用多中心数据建立一种有效的影像组学预测模型，用于早期评估临床I期LUAD患者的LNM风险。目的是为早期肺癌患者术前制定淋巴结清扫策略提供依据。
来自三个医疗中心[中国医学科学院肿瘤医院（CCAM）、重庆医科大学附属第一医院（1CMU）和北京朝阳医院（BCYH）]的578例接受术前胸部CT检查的LUAD患者被分为三组，即训练组（n = 336）、测试组（n = 167）和独立验证组（n = 75）。提取每个原发性肿瘤的1316个影像组学特征记录。采用最小绝对收缩和选择算子（LASSO）分析和多变量逻辑回归进行数据降维、特征选择并构建预测模型。
在训练组中，临床模型、影像组学模型和复合模型的曲线下面积（AUC）分别为0.820、0.871和0.883。在测试组中，临床模型、影像组学模型和复合模型的AUC分别为0.897、0.915和0.934。在验证集中，影像组学模型的AUC最高为0.870，而复合模型和临床模型的AUC分别为0.841和0.710。德龙检验结果显示，在训练组和验证组中，影像组学模型和复合模型的AUC均显著高于临床模型。决策曲线分析表明影像组学列线图具有临床实用性。
本研究开发并验证了一种影像组学预测模型，该模型能够轻松预测I期LUAD患者的LNM。该模型为术前制定淋巴结清扫策略提供了依据，并有助于更好地确定早期LUAD的肿瘤淋巴结转移分期。
