■为了解决低剂量计算机断层扫描(LDCT)的低依从性和不满意的特异性,在肺癌筛查和治疗中,需要有效和非侵入性的方法来补充其局限性.ASCEND-LUNG研究是一项前瞻性的两阶段病例对照研究,旨在评估基于液体活检的全面肺癌筛查和筛查后肺结节管理系统的性能。
■我们旨在开发一种名为北京大学肺癌筛查和管理系统(PKU-LCSMS)的综合肺癌系统,该系统包括用于识别需要LDCT的特定人群的肺癌筛查模型和用于对LDCT后的肺结节进行分类的人工智能辅助(AI辅助)肺结节诊断模型。465名参与者的数据集(216名癌症,47良性,202个非癌症对照)用于两个模型的发展阶段。对于肺癌筛查模型的开发,癌症参与者以1:1的比例随机分为训练和验证队列,然后非癌症对照与癌症病例的年龄匹配为1:1。同样,对于AI辅助的肺结节模型,癌症和良性参与者也以2:1的比例随机分为训练和验证队列.随后,在模型验证阶段,使用由291名参与者组成的独立验证队列(140名癌症,25良性,126非癌症对照)。前瞻性收集的血液样本进行了多组学分析,包括无细胞DNA(cfDNA)甲基化,突变,和血清蛋白。还获得了计算机断层扫描(CT)图像数据。另外分析配对组织样本的DNA甲基化,DNA突变,和信使RNA(mRNA)的表达,进一步探讨其潜在的生物学机制。这项研究在ClinicalTrials.gov注册,NCT04817046。
对整个筛选和诊断过程中的基线血液样品进行评价。基于cfDNA甲基化的肺癌筛查模型显示出最高的曲线下面积(AUC)为0.910(95%CI,0.869-0.950),其次是蛋白质模型(0.891[95%CI,0.845-0.938]),最后是突变模型(0.577[95%CI,0.482-0.672])。Further,最终的筛选模型,结合了cfDNA甲基化和蛋白质特征,AUC为0.963(95%CI,0.942-0.984)。在独立验证队列中,多组学筛查模型的敏感性为99.2%(95%CI,0.957-1.000),特异性为56.3%(95%CI,0.472-0.652).对于AI辅助的肺结节诊断模型,结合了cfDNA甲基化和CT图像特征,它的灵敏度为81.1%(95%CI,0.732-0.875),独立验证队列的特异性为76.0%(95%CI,0.549~0.906).此外,肺癌筛查模型和AI辅助肺结节诊断模型共有4个差异甲基化区域(DMRs).
■我们开发并验证了一种基于液体活检的综合肺癌筛查和管理系统,称为PKU-LCSMS,该系统结合了基于血液组学的结合cfDNA甲基化和蛋白质特征的基于血液组学的肺癌筛查模型和AI辅助的肺结节诊断模型,该模型将CT图像和cfDNA甲基化特征依次整合在一起,以简化肺癌筛查和筛查后肺结节管理的整个过程。它可能为肺癌筛查和管理提供一个有前途的适用解决方案。
■这项工作得到了科学支持,科学,雄安新区科技创新工程,北京市自然科学基金,CAMS医学科学创新基金(CIFMS),北京大学临床医学加X青年学者项目,中央大学基础研究基金,早期非小细胞肺癌智能诊断与治疗研究单位,中国医学科学院,国家自然科学基金,北京大学人民医院科研发展基金,国家重点研究发展计划,中央高校基础研究经费。
UNASSIGNED: In order to address the low compliance and dissatisfied specificity of low-dose computed tomography (LDCT), efficient and non-invasive approaches are needed to complement its limitations for lung cancer screening and management. The ASCEND-LUNG
study is a prospective two-stage case-control
study designed to evaluate the performance of a liquid biopsy-based comprehensive lung cancer screening and post-screening pulmonary nodules management system.
UNASSIGNED: We aimed to develop a comprehensive lung cancer system called Peking University Lung Cancer Screening and Management System (PKU-LCSMS) which comprises a lung cancer screening model to identify specific populations requiring LDCT and an artificial intelligence-aided (AI-aided) pulmonary nodules diagnostic model to classify pulmonary nodules following LDCT. A dataset of 465 participants (216 cancer, 47 benign, 202 non-cancer control) were used for the two models\' development phase. For the lung cancer screening model development, cancer participants were randomly split at a ratio of 1:1 into the train and validation cohorts, and then non-cancer controls were age-matched to the cancer cases in a 1:1 ratio. Similarly, for the AI-aided pulmonary nodules model, cancer and benign participants were also randomly divided at a ratio of 2:1 into the train and validation cohorts. Subsequently, during the model validation phase, sensitivity and specificity were validated using an independent validation cohort consisting of 291 participants (140 cancer, 25 benign, 126 non-cancer control). Prospectively collected blood samples were analyzed for multi-omics including cell-free DNA (cfDNA) methylation, mutation, and serum protein. Computerized tomography (CT) images data was also obtained. Paired tissue samples were additionally analyzed for DNA methylation, DNA mutation, and messenger RNA (mRNA) expression to further explore the potential biological mechanisms. This
study is registered with ClinicalTrials.gov, NCT04817046.
UNASSIGNED: Baseline blood samples were evaluated for the whole screening and diagnostic process. The cfDNA methylation-based lung cancer screening model exhibited the highest area under the curve (AUC) of 0.910 (95% CI, 0.869-0.950), followed by the protein model (0.891 [95% CI, 0.845-0.938]) and lastly the mutation model (0.577 [95% CI, 0.482-0.672]). Further, the final screening model, which incorporated cfDNA methylation and protein features, achieved an AUC of 0.963 (95% CI, 0.942-0.984). In the independent validation cohort, the multi-omics screening model showed a sensitivity of 99.2% (95% CI, 0.957-1.000) at a specificity of 56.3% (95% CI, 0.472-0.652). For the AI-aided pulmonary nodules diagnostic model, which incorporated cfDNA methylation and CT images features, it yielded a sensitivity of 81.1% (95% CI, 0.732-0.875), a specificity of 76.0% (95% CI, 0.549-0.906) in the independent validation cohort. Furthermore, four differentially methylated regions (DMRs) were shared in the lung cancer screening model and the AI-aided pulmonary nodules diagnostic model.
UNASSIGNED: We developed and validated a liquid biopsy-based comprehensive lung cancer screening and management system called PKU-LCSMS which combined a blood multi-omics based lung cancer screening model incorporating cfDNA methylation and protein features and an AI-aided pulmonary nodules diagnostic model integrating CT images and cfDNA methylation features in sequence to streamline the entire process of lung cancer screening and post-screening pulmonary nodules management. It might provide a promising applicable solution for lung cancer screening and management.
UNASSIGNED: This work was supported by Science, Science, Technology & Innovation Project of Xiongan New Area, Beijing Natural Science Foundation, CAMS Innovation Fund for Medical Sciences (CIFMS), Clinical Medicine Plus X-Young Scholars Project of Peking University, the Fundamental Research Funds for the Central Universities, Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Peking University People\'s Hospital Research and Development Funds, National Key Research and Development Program of China, and the fundamental research funds for the central universities.