保留比肺量受损 (PRISm) 被认为是慢性阻塞性肺病的先兆。放射组学列线图可以有效地将 PRISm 受试者与非 COPD 受试者区分开来，特别是在大规模 CT 肺癌筛查期间。 总共包括 1481 名参与者（训练组、内部验证组和外部验证组分别为 864 名、370 名和 247 名）。使用全自动分割算法对薄层计算机断层扫描 (CT) 上的整个肺进行分割。采用 PyRadiomics 提取放射组学特征。还获得了临床特征。此外，采用Spearman相关分析、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）分类器来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包含临床特征和放射组学特征的列线图。最后，使用验证队列对校准、区分和临床有用性进行分析。放射组学特征包括 14 个稳定特征，与训练和验证队列的 PRISm 相关 (p<0.001)。与单独的训练队列临床模型或放射组学特征相比，结合独立预测因素（放射组学特征、年龄、BMI 和性别）的放射组学列线图可以很好地区分非 COPD 受试者的 PRISm（AUC 0.787 vs. 0.675 vs. 0.778），内部（ AUC 0.773 vs. 0.682 vs. 0.767）和外部验证队列（AUC 0.702 vs. 0.610 vs. 0.699）。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 以帮助临床决策。© 2024。作者。

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.© 2024. The Author(s).