Yan Haoji, Niimi Takahiro, Matsunaga Takeshi, Fukui Mariko, Hattori Aritoshi, Takamochi Kazuya, Suzuki Kenji
Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan.
Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan.
J Thorac Cardiovasc Surg. 2025 Jan;169(1):254-266.e9. doi: 10.1016/j.jtcvs.2024.05.010. Epub 2024 May 22.
Clinical stage IA non-small cell lung cancer (NSCLC) showing a pure-solid appearance on computed tomography is associated with a worse prognosis. This study aimed to develop and validate machine-learning models using preoperative clinical and radiomic features to predict overall survival (OS) in clinical stage IA pure-solid NSCLC.
Patients who underwent lung resection for NSCLC between January 2012 and December 2020 were reviewed. The radiomic features were extracted from the intratumoral and peritumoral regions on computed tomography. The machine-learning models were developed using random survival forest and eXtreme Gradient Boosting (XGBoost) algorithms, whereas the Cox regression model was set as a benchmark. Model performance was assessed using the integrated time-dependent area under the curve (iAUC) and validated by 5-fold cross-validation.
In total, 642 patients with clinical stage IA pure-solid NSCLC were included. Among 3748 radiomic and 34 preoperative clinical features, 42 features were selected. Both machine-learning models outperformed the Cox regression model (iAUC, 0.753; 95% confidence interval [CI], 0.629-0.829). The XGBoost model showed a better performance (iAUC, 0.832; 95% CI, 0.779-0.880) than the random survival forest model (iAUC, 0.795; 95% CI, 0.734-0.856). The XGBoost model showed an excellent survival stratification performance with a significant OS difference among the low-risk (5-year OS, 100.0%), moderate low-risk (5-year OS, 88.5%), moderate high-risk (5-year OS, 75.6%), and high-risk (5-year OS, 41.7%) groups (P < .0001).
A radiomics-based machine-learning model can preoperatively and accurately predict OS and improve survival stratification in clinical stage IA pure-solid NSCLC.
计算机断层扫描显示为纯实性表现的临床IA期非小细胞肺癌（NSCLC）预后较差。本研究旨在开发并验证使用术前临床和影像组学特征的机器学习模型，以预测临床IA期纯实性NSCLC的总生存期（OS）。
回顾了2012年1月至2020年12月期间因NSCLC接受肺切除术的患者。从计算机断层扫描的肿瘤内和肿瘤周围区域提取影像组学特征。使用随机生存森林和极端梯度提升（XGBoost）算法开发机器学习模型，而将Cox回归模型作为基准。使用综合时间依赖性曲线下面积（iAUC）评估模型性能，并通过五折交叉验证进行验证。
总共纳入了642例临床IA期纯实性NSCLC患者。在3748个影像组学特征和34个术前临床特征中，选择了42个特征。两个机器学习模型均优于Cox回归模型（iAUC，0.753；95%置信区间[CI]，0.629 - 0.829）。XGBoost模型表现优于随机生存森林模型（iAUC，0.795；95%CI，0.734 - 0.856）（iAUC，0.832；95%CI，0.779 - 0.880）。XGBoost模型显示出出色的生存分层性能，低风险（5年OS，100.0%）、中低风险（5年OS，88.5%）、中高风险（5年OS，75.6%）和高风险（5年OS，41.7%）组之间的OS存在显著差异（P <.0001）。
基于影像组学的机器学习模型可以在术前准确预测临床IA期纯实性NSCLC的OS并改善生存分层。
