Xue Li Min, Li Ying, Zhang Yu, Wang Shu Chao, Zhang Ran Ying, Ye Jian Ding, Yu Hong, Qiang Jin Wei
Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, China.
Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
Eur Radiol. 2022 Apr;32(4):2672-2682. doi: 10.1007/s00330-021-08343-5. Epub 2021 Oct 22.
Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. The optimal management of computed tomography (CT)-indeterminate pulmonary nodules is important. To optimize individualized follow-up strategies, we developed a radiomics nomogram for predicting 2-year growth in case of indeterminate small pulmonary nodules.
A total of 215 histopathology-confirmed small pulmonary nodules (21 benign and 194 malignant) in 205 patients with ultra-high-resolution CT (U-HRCT) were divided into growth and nongrowth nodules and were randomly allocated to the primary (n = 151) or validation (n = 64) group. The least absolute shrinkage and selection operator (LASSO) method was used for radiomics feature selection and radiomics signature determination. Multivariable logistic regression analysis was used to develop a radiomics nomogram that integrated the radiomics signature with significant clinical parameters (sex and nodule type). The area under the curve (AUC) was applied to assess the predictive performance of the radiomics nomogram. The net benefit of the radiomics nomogram was assessed using a clinical decision curve.
The radiomics signature and nomogram yielded AUCs of 0.892 (95% confidence interval [CI]: 0.843-0.940) and 0.911 (95% CI: 0.867-0.955), respectively, in the primary group and 0.826 (95% CI: 0.727-0.926) and 0.843 (95% CI: 0.749-0.937), respectively, in the validation group. The clinical usefulness of the nomogram was demonstrated by decision curve analysis.
A radiomics nomogram was developed by integrating the radiomics signature with clinical parameters and was easily used for the individualized prediction of two-year growth in case of CT-indeterminate small pulmonary nodules.
• A radiomics nomogram was developed for predicting the two-year growth of CT-indeterminate small pulmonary nodules. • The nomogram integrated a CT-based radiomics signature with clinical parameters and was valuable in developing an individualized follow-up strategy for patients with indeterminate small pulmonary nodules.
肺癌是全球最常见的癌症及癌症相关死亡的主要原因。计算机断层扫描（CT）检查结果不确定的肺结节的最佳管理至关重要。为优化个体化随访策略，我们开发了一种用于预测不确定的小肺结节2年生长情况的影像组学列线图。
对205例接受超高分辨率CT（U-HRCT）检查的患者中215个经组织病理学证实的小肺结节（21个良性和194个恶性）进行分析，将其分为生长结节和非生长结节，并随机分配至初级组（n = 151）或验证组（n = 64）。采用最小绝对收缩和选择算子（LASSO）方法进行影像组学特征选择和影像组学特征确定。使用多变量逻辑回归分析开发影像组学列线图，该列线图将影像组学特征与重要临床参数（性别和结节类型）相结合。应用曲线下面积（AUC）评估影像组学列线图的预测性能。使用临床决策曲线评估影像组学列线图的净效益。
在初级组中，影像组学特征和列线图的AUC分别为0.892（95%置信区间[CI]：0.843 - 0.940）和0.911（95%CI：0.867 - 0.955），在验证组中分别为0.826（95%CI：0.727 - 0.926）和0.843（95%CI：0.749 - 0.937）。决策曲线分析证明了列线图的临床实用性。
通过将影像组学特征与临床参数相结合开发了影像组学列线图，该列线图可轻松用于对CT检查结果不确定的小肺结节2年生长情况进行个体化预测。
•开发了一种用于预测CT检查结果不确定的小肺结节2年生长情况的影像组学列线图。•该列线图将基于CT的影像组学特征与临床参数相结合，对于为检查结果不确定的小肺结节患者制定个体化随访策略具有重要价值。
