对比增强CT影像组学在实性成分占比≥25%的肺腺癌中识别高级别模式及预后分层
Contrast-enhanced CT Radiomics for High-Grade Pattern Identification and Prognostic Stratification in Lung Adenocarcinoma with Consolidation-to-Tumor Ratio of 25% or More.
作者
作者单位
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer/Tianjin's Clinical Research Center for Cancer/Key Laboratory of Cancer Prevention and Therapy, Huanhuxi Rd, Tiyuanbei, Hexi District, Tianjin 300060, China.
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, HNHC Key Laboratory of Oncology Medical Imaging Response Assessment, Zhengzhou, China.
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
摘要
中文
背景 影像组学可能在本前识别肺腺癌（ADC）中的高级别模式（HGPs）并辅助临床决策。目的 开发并评估基于术前对比增强CT图像的机器学习模型以预测HGPs，并探索该模型的预后价值。材料与方法 回顾性纳入2017年1月至2025年5月在三家中心接受手术的临床I期浸润性ADC患者。基于HGP比例进行二元（低风险：HGP<20%；高风险：HGP≥20%）和三元（HGP0：HGP=0；HGP1：0<HGP<20%；HGP2：HGP≥20%）分类分析。使用多变量逻辑回归分析确定HGPs的独立预测因子。构建了基于XGBoost分类器的影像组学模型和组合模型（影像组学预测概率加临床变量加CT语义特征），并评估其判别能力、校准能力和临床实用性。进行Kaplan-Meier和Cox回归分析以确定总生存期（OS）和无复发生存期（RFS）的预后因素。结果 共1,181名患者（中位年龄61岁[IQR, 54-66岁]；694名女性）被分配到训练集（n=667）、内部测试集（n=279）和外部测试集（n=235）。组合模型在二元分类（训练集：受试者工作特征曲线下面积[AUC] 0.87 [95% CI: 0.84, 0.90]；内部测试：AUC 0.80 [95% CI: 0.74, 0.85]；外部测试：AUC 0.84 [95% CI: 0.78, 0.90]）和三元分类（训练集：微平均AUC 0.80 [95% CI: 0.78, 0.82]；内部测试：微平均AUC 0.74 [95% CI: 0.70, 0.77]；外部测试：微平均AUC 0.72 [95% CI: 0.68, 0.76]）中均达到最佳判别能力。模型预测的高风险组是OS（二元：风险比[HR]=1.98, P=.04；三元：HR=2.93, P=.03）和RFS（二元：HR=3.33, P<.001；三元：HR=5.10, P<.001）的独立预后因素，并在亚组分析中一致确认。结论 结合临床变量、CT语义特征和影像组学预测概率的组合模型有效预测了肺ADC中的高级别模式，并显示出强大的预后风险分层潜力。© RSNA, 2026 本文提供补充材料。另见本期Arita和Kocak的社论。
English
Background Radiomics may preoperatively identify high-grade patterns (HGPs) in lung adenocarcinoma (ADC) and assist in clinical decision-making. Purpose To develop and evaluate a machine learning model based on preoperative contrast-enhanced CT images to predict HGPs and explore the model's prognostic value. Materials and Methods Patients with clinical stage I invasive ADC who underwent surgery (January 2017 to May 2025) were retrospectively enrolled from three centers. Binary (low risk: HGPs < 20%; high risk: HGPs ≥ 20%) and ternary (HGP0: HGPs = 0; HGP1: 0 < HGPs < 20%; HGP2: HGPs ≥ 20%) classification analyses were performed based on the proportion of HGPs. Multivariable logistic regression analysis was used to determine independent predictors of HGPs. XGBoost classifier-based radiomic models and combined models (radiomics-predicted probabilities plus clinical variables plus CT semantic features) were constructed and evaluated for discriminability, calibration ability, and clinical utility. Kaplan-Meier and Cox regression analyses were conducted to identify prognostic factors for overall survival (OS) and recurrence-free survival (RFS). Results A total of 1181 patients (median age, 61 years [IQR, 54-66 years]; 694 female) were allocated to the training (n = 667), internal test (n = 279), and external test (n = 235) sets. The combined model achieved the best discrimination in both binary (training: area under the receiver operating characteristic curve [AUC], 0.87 [95% CI: 0.84, 0.90]; internal test: AUC, 0.80 [95% CI: 0.74, 0.85]; external test: AUC, 0.84 [95% CI: 0.78, 0.90]) and ternary (training: microaverage AUC, 0.80 [95% CI: 0.78, 0.82]; internal test: microaverage AUC, 0.74 [95% CI: 0.70, 0.77]; external test: microaverage AUC, 0.72 [95% CI: 0.68, 0.76]) classification analyses. Model-predicted high-risk group was an independent prognostic factor for both OS (binary: hazard ratio [HR] = 1.98, P =.04; ternary: HR = 2.93, P =.03) and RFS (binary: HR = 3.33, P < .001; ternary: HR = 5.10, P < .001) and was consistently confirmed across subgroup analyses. Conclusion The combined model, integrating clinical variables, CT semantic features, and radiomics-predicted probabilities, effectively predicted high-grade patterns in lung ADC and showed strong potential for prognostic risk stratification. © RSNA, 2026 Supplemental material is available for this article. See also the editorial by Arita and Kocak in this issue.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 17.6
- 新锐分区
- 1区