Saha Pratim, Bodduluri Sandeep, Nakhmani Arie, Chaudhary Muhammad F A, Amudala Puchakalaya Praneeth R, Sthanam Venkata, San Jose Estepar Raul, Reinhardt Joseph M, Zhang Chengzui, Bhatt Surya P
The University of Alabama at Birmingham, Computer Science, Birmingham, Alabama, United States.
University of Alabama at Birmingham, Pulmonary, Allergy and Critical Care Medicine, Birmingham, Alabama, United States.
Ann Am Thorac Soc. 2024 Oct 15;22(1):83-92. doi: 10.1513/AnnalsATS.202401-009OC.
Rationale Emphysema progression is heterogeneous. Predicting temporal changes in lung density and detecting rapid progressors may facilitate selection of individuals for targeted therapies. Objective To test whether computed tomography (CT) radiomics can be used to predict changes in lung density and detect rapid progressors. Methods We extracted radiomics features from inspiratory chest CT in 4,575 subjects with and without airflow obstruction at enrollment, who completed a follow-up visit at approximately 5 years. We quantified emphysema using adjusted lung density (ALD) and estimated emphysema progression as the annualized change in ALD (∆ALD/year) between visits. We categorized participants into rapid progressors (>1% ∆ALD/year) and stable disease (≤1% ∆ALD/year). A gradient boosting model was used (1) to predict ALD at 5-years and (2) to identify rapid progressors. Four models using demographics (base clinical model); CT density; radiomics; and combined features (clinical, radiomics, and CT density) were evaluated and tested. Results There were 1,773 (38.8%) rapid progressors. For predicting ALD at 5-years in the 20% held-out data, the base model explained 31% of the variance (adjusted R2 = 0.31) whereas R2 was 0.74 for the CT density model, 0.66 for the radiomics-only model, and 0.77 for the combined features model. For detecting rapid progressors, the base model (AUC = 0.57, 95%CI 0.53-0.61) was outperformed by the radiomics-only model (AUC = 0.73, 95%CI 0.69-0.76, ∆ =0.0003, p < 0.001) and the combined model (AUC = 0.74, 95%CI 0.71-0.77, ∆ = 0.0003, p < 0.001). Conclusions Parenchymal and airway radiomics features derived from inspiratory scans can be used to predict temporal changes in lung density and help identify rapid progressors.
原理 肺气肿进展具有异质性。预测肺密度的时间变化并检测快速进展者可能有助于选择接受靶向治疗的个体。目的 测试计算机断层扫描（CT）影像组学是否可用于预测肺密度变化并检测快速进展者。方法 我们从4575名在入组时有无气流受限的受试者的吸气胸部CT中提取影像组学特征，这些受试者在大约5年后完成了随访。我们使用调整后的肺密度（ALD）对肺气肿进行量化，并将肺气肿进展估计为两次随访之间ALD的年化变化（∆ALD/年）。我们将参与者分为快速进展者（>1%∆ALD/年）和疾病稳定者（≤1%∆ALD/年）。使用梯度提升模型（1）预测5年时的ALD，（2）识别快速进展者。评估并测试了四个模型，分别使用人口统计学数据（基础临床模型）；CT密度；影像组学；以及联合特征（临床、影像组学和CT密度）。结果 有1773名（38.8%）快速进展者。对于在20%的留出数据中预测5年时的ALD，基础模型解释了31%的方差（调整后R2 = 0.31），而CT密度模型的R2为0.74，仅影像组学模型的R2为0.66，联合特征模型的R2为0.77。对于检测快速进展者，基础模型（AUC = 0.57，95%CI 0.53 - 0.61）的表现不如仅影像组学模型（AUC = 0.73，95%CI 0.69 - 0.76，∆ = 0.0003，p < 0.001）和联合模型（AUC = 0.74，95%CI 0.71 - 0.77，∆ = 0.0003，p < 0.001）。结论 从吸气扫描中获得的实质和气道影像组学特征可用于预测肺密度的时间变化并有助于识别快速进展者。
