Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, China.
School of Medical Imaging, Shandong Second Medical University, Weifang, 261053, Shandong, China.
Respir Res. 2024 Sep 3;25(1):329. doi: 10.1186/s12931-024-02964-2.
Preserved Ratio Impaired Spirometry (PRISm) is considered to be a precursor of chronic obstructive pulmonary disease. Radiomics nomogram can effectively identify the PRISm subjects from non-COPD subjects, especially when during large-scale CT lung cancer screening.
Totally 1481 participants (864, 370 and 247 in training, internal validation, and external validation cohorts, respectively) were included. Whole lung on thin-section computed tomography (CT) was segmented with a fully automated segmentation algorithm. PyRadiomics was adopted for extracting radiomics features. Clinical features were also obtained. Moreover, Spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking and least absolute shrinkage and selection operator (LASSO) classifier were adopted to analyze whether radiomics features could be used to build radiomics signatures. A nomogram that incorporated clinical features and radiomics signature was constructed through multivariable logistic regression. Last, calibration, discrimination and clinical usefulness were analyzed using validation cohorts.
The radiomics signature, which included 14 stable features, was related to PRISm of training and validation cohorts (p < 0.001). The radiomics nomogram incorporating independent predicting factors (radiomics signature, age, BMI, and gender) well discriminated PRISm from non-COPD subjects compared with clinical model or radiomics signature alone for training cohort (AUC 0.787 vs. 0.675 vs. 0.778), internal (AUC 0.773 vs. 0.682 vs. 0.767) and external validation cohorts (AUC 0.702 vs. 0.610 vs. 0.699). Decision curve analysis suggested that our constructed radiomics nomogram outperformed clinical model.
The CT-based whole lung radiomics nomogram could identify PRISm to help decision-making in clinic.
保留比肺活量受损（PRISm）被认为是慢性阻塞性肺疾病的前兆。放射组学列线图可以有效地从非 COPD 患者中识别出 PRISm 患者，特别是在大规模 CT 肺癌筛查时。
共纳入 1481 名参与者（分别为训练组、内部验证组和外部验证组的 864、370 和 247 名）。采用全自动分割算法对全肺薄层 CT 进行分割。采用 PyRadiomics 提取放射组学特征。同时还获取了临床特征。此外，采用 Spearman 相关性分析、最小冗余最大相关性（mRMR）特征排序和最小绝对收缩和选择算子（LASSO）分类器分析放射组学特征是否可用于构建放射组学特征。通过多变量逻辑回归构建了纳入临床特征和放射组学特征的列线图。最后，通过验证队列分析校准、判别和临床实用性。
纳入 14 个稳定特征的放射组学特征与训练和验证队列的 PRISm 相关（p<0.001）。放射组学列线图纳入独立预测因子（放射组学特征、年龄、BMI 和性别），与临床模型或单独的放射组学特征相比，能更好地将 PRISm 与非 COPD 患者区分开来，对训练队列的区分能力（AUC 0.787 比 0.675 比 0.778）、内部验证队列（AUC 0.773 比 0.682 比 0.767）和外部验证队列（AUC 0.702 比 0.610 比 0.699）均有提高。决策曲线分析表明，我们构建的放射组学列线图优于临床模型。
基于 CT 的全肺放射组学列线图可以识别 PRISm，有助于临床决策。
