Background: Surgical resection remains the standard treatment for early-stage lung cancer. However, the recurrence rate after surgery is unacceptably high, ranging from 30% to 50%. Despite extensive efforts, accurately predicting the likelihood and timing of recurrence remains a significant challenge. This study aims to predict postoperative recurrence by identifying novel image biomarkers from preoperative chest CT scans. Methods: A cohort of 309 patients was selected from 512 non-small-cell lung cancer patients who underwent lung resection. Cox proportional hazards regression analysis was employed to identify risk factors associated with recurrence and was compared with machine learning (ML) methods for predictive performance. The goal is to improve the ability to predict the risk and time of recurrence in seemingly “cured” patients, enabling personalized surveillance strategies to minimize lung cancer recurrence. Results: The Cox hazards analyses identified surgical procedure, TNM staging, lymph node involvement, body composition, and tumor characteristics as significant determinants of recurrence risk, both for local/regional and distant recurrence, as well as recurrence-free survival (RFS) and overall survival (OS) (p< 0.05). ML models and Cox models exhibited comparable predictive performance, with an area under the receiver operative characteristic (ROC) curve (AUC) ranging from 0.75 to 0.77. Conclusions: These promising findings demonstrate the feasibility of predicting postoperative lung cancer recurrence and survival time using preoperative chest CT scans. However, further validation using larger, multisite cohort is necessary to ensure robustness and facilitate integration into clinical practice for improved cancer management.
背景：手术切除仍是早期肺癌的标准治疗方法。然而，术后复发率高达30%至50%，这一现状令人难以接受。尽管已付出大量努力，准确预测复发可能性及时间点仍面临重大挑战。本研究旨在通过术前胸部CT扫描识别新型影像生物标志物，以预测术后复发风险。 方法：从512例接受肺切除术的非小细胞肺癌患者中筛选出309例作为研究队列。采用Cox比例风险回归分析识别与复发相关的风险因素，并与机器学习方法的预测性能进行比较。研究目标在于提升对表面“治愈”患者复发风险及时间的预测能力，从而制定个体化监测策略以降低肺癌复发率。 结果：Cox风险分析显示，手术方式、TNM分期、淋巴结转移、身体成分及肿瘤特征均是局部/区域复发、远处转移、无复发生存期及总生存期的显著影响因素（p<0.05）。机器学习模型与Cox模型展现出相当的预测效能，受试者工作特征曲线下面积达0.75-0.77。 结论：这些具有前景的研究结果证实了利用术前胸部CT扫描预测肺癌术后复发及生存时间的可行性。但需通过更大规模、多中心队列研究进一步验证，以确保其稳健性，推动该技术融入临床实践，从而优化癌症管理策略。
肿瘤(癌症)患者之家