Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, Jiangsu, China; Clinical Research Institute of Traditional Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, Jiangsu, China.
J Thorac Cardiovasc Surg. 2024 Nov;168(5):e140-e175. doi: 10.1016/j.jtcvs.2024.04.026. Epub 2024 Apr 24.
Although low-dose computed tomography has been proven effective to reduce lung cancer-specific mortality, a considerable proportion of surgically resected high-risk lung nodules were still confirmed pathologically benign. There is an unmet need of a novel method for malignancy classification in lung nodules.
We recruited 307 patients with high-risk lung nodules who underwent curative surgery, and 247 and 60 cases were pathologically confirmed malignant and benign lung lesions, respectively. Plasma samples from each patient were collected before surgery and performed low-depth (5×) whole-genome sequencing. We extracted cell-free DNA characteristics and determined radiomic features. We built models to classify the malignancy using our data and further validated models with 2 independent lung nodule cohorts.
Our models using one type of profile were able to distinguish lung cancer and benign lung nodules at an area under the curve metrics of 0.69 to 0.91 in the study cohort. Integrating all the 5 base models using cell-free DNA profiles, the cell-free DNA-based ensemble model achieved an area under the curve of 0.95 (95% CI, 0.92-0.97) in the study cohort and 0.98 (95% CI, 0.96-1.00) in the validation cohort. At a specificity of 95.0%, the sensitivity reached 80.0% in the study cohort. With the same threshold, the specificity and sensitivity had similar performances in both validation cohorts. Furthermore, the performance of area under the curve reached 0.97 in both the study and validation cohorts when considering the radiomic profile.
The cell-free DNA profiles-based method is an efficient noninvasive tool to distinguish malignancies and high-risk but pathologically benign lung nodules.
虽然低剂量计算机断层扫描已被证明能有效降低肺癌特异性死亡率,但相当一部分手术切除的高危肺结节仍被病理证实为良性。因此,需要一种新的方法来对肺结节进行恶性肿瘤分类。
我们招募了 307 名接受根治性手术的高危肺结节患者,其中 247 例和 60 例分别经病理证实为恶性和良性肺病变。每位患者在术前采集血浆样本,并进行低深度(5×)全基因组测序。我们提取了游离 DNA 特征和确定了放射组学特征。我们使用这些数据构建了分类恶性肿瘤的模型,并使用另外 2 个独立的肺结节队列对模型进行了验证。
我们使用单一类型的模型,在研究队列中,曲线下面积指标(AUC)为 0.69 至 0.91,能够区分肺癌和良性肺结节。整合所有 5 种基于游离 DNA 图谱的基础模型,基于游离 DNA 的集成模型在研究队列中的 AUC 为 0.95(95%置信区间,0.92-0.97),在验证队列中的 AUC 为 0.98(95%置信区间,0.96-1.00)。在特异性为 95.0%的情况下,研究队列中的敏感性达到 80.0%。在相同的阈值下,两个验证队列的特异性和敏感性表现相似。此外,当考虑放射组学特征时,研究和验证队列的 AUC 性能均达到 0.97。
基于游离 DNA 图谱的方法是一种有效的非侵入性工具,可用于区分恶性肿瘤和高危但病理良性的肺结节。