Lin Song, Niu Yanli, Song Lina, Ye Yingjian, Yang Jinfang, Liu Junjie, Zhou Xin, An Peng
Department of Radiology and Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China.
Department of Medical Cosmetology, Anesthesiology, Oncology, and Epidemiology, Xiangyang Key Laboratory of Maternal-fetal Medicine on Fetal Congenital Heart Disease, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Hubei, People's Republic of China (P.R.C), Xiangyang, Hubei, China.
Front Oncol. 2025 May 23;15:1601674. doi: 10.3389/fonc.2025.1601674. eCollection 2025.
PURPOSE: This study aims to develop an interpretable predictive model combining contrast-enhanced CT (CECT) radiomics features with clinicopathological parameters to assess 3-year recurrence risk after surgery for lung adenocarcinoma (LA). METHODS: A retrospective cohort of 350 LA patients (126 recurrence, 224 non-recurrence) from Xiangyang NO.1 People's Hospital (2016-2023) was included. Radiomics features were extracted from arterial and venous phase CECT images using 3D Slicer's Radiomics plugin. Features with intraclass correlation coefficient (ICC > 0.75) were selected, followed by LASSO regression with cross-validation to generate radiomics scores (Radscore3 for intratumoral and Radscore4 for peritumoral regions). Clinical variables (sex, heterogeneous enhancement, pleural invasion, Ki67) were integrated via chi-square/t-test analysis. Ten machine learning algorithms (e.g., XGBoost, CatBoost, Random Forest) were trained on a stratified 7:3 split (training: n=245; testing: n=105) with five-fold cross-validation. Model performance was evaluated using ROC curves (AUC), calibration curves, decision curve analysis (DCA), and a nomogram. RESULTS: Univariate analysis identified sex (OR=1.66, p=0.02), heterogeneous enhancement (OR=4.32, p<0.05), visceral pleural invasion (OR=4.75, p<0.05), Radscore3 (OR=356.17, p<0.05), Radscore4 (OR=1529.16, p<0.05), and Ki67 (OR=1.09, p=0.01) as significant predictors. Among machine learning models, CatBoost achieved superior performance (AUC=0.883, 95% CI:0.811-0.955) compared to logistic regression (AUC=0.877, 95% CI:0.804-0.949) in test set. Calibration curves demonstrated high consistency between predicted and observed recurrence risks, while DCA indicated clinical utility at threshold probabilities >0.17. SHAP analysis highlighted heterogeneous enhancement, visceral pleural invasion, Radscore3/4, and Ki67 as key contributors. The nomogram integrated these factors, enhancing model interpretability and clinical applicability. CONCLUSION: The CatBoost model integrating CECT environmental radiomics and clinicopathological parameters effectively predicts postoperative LA recurrence, supporting personalized adjuvant therapy decisions. Its interpretable framework emphasizes tumor heterogeneity (Radscore3/4) as a critical prognostic biomarker, providing mechanistic insights into LA recurrence.
目的：本研究旨在开发一种可解释的预测模型，该模型将对比增强CT（CECT）影像组学特征与临床病理参数相结合，以评估肺腺癌（LA）手术后3年的复发风险。 方法：纳入了襄阳市第一人民医院（2016 - 2023年）的350例LA患者的回顾性队列（126例复发，224例未复发）。使用3D Slicer的影像组学插件从动脉期和静脉期CECT图像中提取影像组学特征。选择组内相关系数（ICC > 0.75）的特征，随后进行带交叉验证的LASSO回归以生成影像组学评分（瘤内为Radscore3，瘤周区域为Radscore4）。通过卡方检验/t检验分析整合临床变量（性别、不均匀强化、胸膜侵犯、Ki67）。在分层的7:3分割（训练：n = 245；测试：n = 105）上使用五折交叉验证训练十种机器学习算法（例如，XGBoost、CatBoost、随机森林）。使用ROC曲线（AUC）、校准曲线、决策曲线分析（DCA）和列线图评估模型性能。 结果：单因素分析确定性别（OR = 1.66，p = 0.02）、不均匀强化（OR = 4.32，p < 0.05）﹑脏层胸膜侵犯（OR = 4.75，p < 0.05）、Radscore3（OR = 356.17，p < 0.05）、Radscore4（OR = 1529.16，p < 0.05）和Ki67（OR = 1.09，p = 0.01）为显著预测因素。在机器学习模型中，与逻辑回归（AUC = 0.877，95% CI：0.804 - 0.949）相比，CatBoost在测试集中表现出更优的性能（AUC = 0.883，95% CI：0.811 - 0.955）。校准曲线显示预测的和观察到的复发风险之间具有高度一致性，而DCA表明在阈值概率>0.17时具有临床实用性。SHAP分析突出了不均匀强化、脏层胸膜侵犯、Radscore3/4和Ki67是关键因素。列线图整合了这些因素，增强了模型的可解释性和临床适用性。 结论：整合CECT环境影像组学和临床病理参数的CatBoost模型有效地预测了LA术后复发，支持个性化辅助治疗决策。其可解释框架强调肿瘤异质性（Radscore3/4）作为关键的预后生物标志物，为LA复发提供了机制性见解。
Cancer Immunol Immunother. 2024-6-4
Radiol Artif Intell. 2024-3
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