提高肺部小结节的鉴别诊断能力：综合血浆甲基化、蛋白生物标志物和 LDCT 成像特征的模型。

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features.

机构信息

Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, People's Republic of China.

Department of Thoracic Surgery, Center of Respiratory Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, People's Republic of China.

出版信息

J Transl Med. 2024 Oct 31;22(1):984. doi: 10.1186/s12967-024-05723-5.

DOI:10.1186/s12967-024-05723-5

PMID:39482707

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11526513/

Abstract

BACKGROUND

Accurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5-10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.

METHODS

Blood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.

RESULTS

Our results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5-10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.

CONCLUSIONS

Our study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.

TRIAL REGISTRATION

This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.

CLINICALTRIALS

gov/ct2/show/NCT05432128 .

摘要

背景

准确区分恶性和良性肺结节，尤其是直径为 5-10 毫米的肺结节，仍然是一项具有挑战性的诊断难题。本研究通过整合循环游离 DNA（cfDNA）甲基化模式、蛋白质谱分析和计算机断层扫描（CT）成像特征，引入了一种新的精确方法，以增强肺结节的分类。

方法

在对 5-30 毫米大小的肺结节进行任何改变疾病的治疗或手术干预之前，收集了 419 名被诊断为患有肺结节的参与者的血液样本。利用高通量亚硫酸氢盐测序进行 DNA 甲基化谱分析，同时利用 Olink 邻近延伸测定法进行蛋白质谱分析。数据集分为训练集和独立测试集。训练集包括 162 例良性和恶性结节的匹配病例，在性别和年龄上是平衡的。相比之下，测试集包括 46 例良性和 49 例恶性结节。通过有效整合分子（DNA 甲基化和蛋白质谱分析）和 CT 成像参数，开发了一种复杂的基于深度学习的分类器，以准确区分良性和恶性肺结节。

结果

我们的结果表明，该综合模型在区分良性和恶性肺结节方面具有准确性和稳健性。它在测试集的分类中实现了 AUC 评分为 0.925（敏感性=83.7%，特异性=82.6%）。综合模型的性能明显高于单独的甲基化（AUC=0.799，P=0.004）、蛋白质（AUC=0.846，P=0.009）和成像模型（AUC=0.866，P=0.01）。重要的是，综合模型在 5-10 毫米的小结节中实现了更高的 AUC 值为 0.951（敏感性=83.9%，特异性=89.7%）。这些结果共同证实了我们的模型在检测良性结节中的恶性结节的准确性和稳健性。