Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, South Korea.
Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 388-1 Pungnap-dong, Songpa-gu, Seoul, 138-736, South Korea.
Eur Radiol. 2021 Oct;31(10):7316-7324. doi: 10.1007/s00330-021-07747-7. Epub 2021 Apr 13.
To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS).
This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis.
The five features remaining after the LASSO analysis were %LAA, AWT_Pi10_6, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA. The RS demonstrated a C-index of 0.774 in the discovery group and 0.805 in the validation group. Patients with a RS greater than 1.053 were classified into the high-risk group and demonstrated worse overall survival than those in the low-risk group in both the discovery (log-rank test, < 0.001; hazard ratio [HR], 5.265) and validation groups (log-rank test, < 0.001; HR, 5.223). For both groups, RS was significantly associated with overall survival after adjustments for patient age and body mass index.
A radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality.
• A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA, AWT_Pi10_6, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA were selected to generate a radiomics model. • A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group. • Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.
应用放射组学分析对慢性阻塞性肺疾病（COPD）患者的总生存进行预测，并评估放射组学特征（RS）的性能。
本研究纳入了来自韩国阻塞性肺疾病（KOLD）队列的 344 例患者。对 112 例患者的队列进行了外部验证。总共从 525 个基于胸部 CT 的放射组学特征中进行了半自动提取。通过最小绝对收缩和选择操作（LASSO）Cox 回归分析选择了 5 个用于生存预测的最有用特征，并用于生成 RS。通过 Kaplan-Meier 生存分析和 Cox 比例风险回归分析评估 RS 对 COPD 患者进行高或低死亡率风险分组的能力。
经过 LASSO 分析后保留的 5 个特征为 %LAA、AWT_Pi10_6、AWT_Pi10_异质性、%WA_异质性和 VA。在发现组中，RS 的 C 指数为 0.774，在验证组中为 0.805。在发现组中（对数秩检验，<0.001；风险比[HR]，5.265）和验证组中（对数秩检验，<0.001；HR，5.223），RS 大于 1.053 的患者被分为高危组，其总生存率明显低于低危组。对于两组，在调整患者年龄和体重指数后，RS 与总生存率显著相关。
用于 COPD 患者生存预测和风险分层的放射组学方法是可行的，构建的放射组学模型具有良好的性能。从 COPD 患者的胸部 CT 数据中得出的 RS 能够有效地识别出死亡率增加的患者。
共提取了 525 个基于胸部 CT 的放射组学特征，选择了 5 个放射组学特征，即 %LAA、AWT_Pi10_6、AWT_Pi10_异质性、%WA_异质性和 VA，生成放射组学模型。
在发现组中，放射组学模型预测 COPD 患者生存率的 C 指数为 0.774，在验证组中为 0.805，具有可靠的性能。
放射组学方法能够有效地识别出死亡率增加的 COPD 患者，高风险组患者在发现组和验证组中的总生存率均较差。
