Dataset Information

Detection of circulating genetically abnormal cells in peripheral blood for early diagnosis of non-small cell lung cancer.

ABSTRACT:

Background

Circulating genetically abnormal cells (CACs) with specific chromosome variations have been confirmed to be present in non-small cell lung cancer (NSCLC). However, the diagnostic performance of CAC detection remains unclear. This study aimed to evaluate the potential clinical application of the CAC test for the early diagnosis of NSCLC.

Methods

In this prospective study, a total of 339 participants (261 lung cancer patients and 78 healthy volunteers) were enrolled. An antigen-independent fluorescence in situ hybridization was used to enumerate the number of CACs in peripheral blood.

Results

Patients with early-stage NSCLC were found to have a significantly higher number of CACs than those of healthy participants (1.34 vs. 0.19; P < 0.001). The CAC test displayed an area under the receiver operating characteristic (ROC) curve of 0.76139 for discriminating stage I NSCLC from healthy participants with 67.2% sensitivity and 80.8% specificity, respectively. Compared with serum tumor markers, the sensitivity of CAC assays for distinguishing early-stage NSCLC was higher (67.2% vs. 48.7%, P < 0.001), especially in NSCLC patients with small nodules (65.4% vs. 36.5%, P = 0.003) and ground-glass nodules (pure GGNs: 66.7% vs. 40.9%, P = 0.003; mixed GGNs: 73.0% vs. 43.2%, P < 0.001).

Conclusions

CAC detection in early stage NSCLC was feasible. Our study showed that CACs could be used as a promising noninvasive biomarker for the early diagnosis of NSCLC.

Key points

What this study adds: This study aimed to evaluate the potential clinical application of the CAC test for the early diagnosis of NSCLC. Significant findings of the study: CAC detection in early stage NSCLC was feasible. Our study showed that CACs could be used as a promising noninvasive biomarker for the early diagnosis of NSCLC.

SUBMITTER: Liu WR

PROVIDER: S-EPMC7606026 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Detection of circulating genetically abnormal cells in peripheral blood for early diagnosis of non-small cell lung cancer.

Liu Wei-Ran WR Zhang Bin B Chen Chen C Li Yue Y Ye Xin X Tang Dong-Jiang DJ Zhang Jun-Cheng JC Ma Jing J Zhou Yan-Ling YL Fan Xian-Jun XJ Yue Dong-Sheng DS Li Chen-Guang CG Zhang Hua H Ma Yu-Chen YC Huo Yan-Song YS Zhang Zhen-Fa ZF He Shu-Yu SY Wang Chang-Li CL

Thoracic cancer 20200928 11

<h4>Background</h4>Circulating genetically abnormal cells (CACs) with specific chromosome variations have been confirmed to be present in non-small cell lung cancer (NSCLC). However, the diagnostic performance of CAC detection remains unclear. This study aimed to evaluate the potential clinical application of the CAC test for the early diagnosis of NSCLC.<h4>Methods</h4>In this prospective study, a total of 339 participants (261 lung cancer patients and 78 healthy volunteers) were enrolled. An a ...[more]

PMID: 32989915

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Project description:BackgroundAn increase in the prevalence of lung cancer that is not smoking-related has been noticed in recent years. Unfortunately, these patients are not included in low dose computer tomography (LDCT) screening programs and are not actually considered in early diagnosis. Therefore, improved early diagnosis methods are urgently needed for non-smokers. It is necessary to establish a prediction model for non-smoking individuals at intermediate to high risk of developing lung cancer (LC) and develop a tool to address the significant gap in evaluating pulmonary nodules in non-smokers.MethodsWe retrospectively investigated 1121 patients with pulmonary nodules, who underwent LDCT examinations between September 2019 and March 2023. Five artificial intelligence (AI) algorithms were used to build two kinds of models and identify which one was better at diagnosing non-smoking pulmonary nodules patients. In the first model, we assigned 554 non-smoking individuals to a training cohort and 150 non-smoking patients to an independent validation cohort. The second model included 971 patients for the training set and 150 non-smoking patients for an independent validation set. All LDCT images of participants were obtained for AI analysis. AI of LDCT scans, liquid biopsy, and clinical characteristics were collected for model building.ResultsAmong LC patients, 58,4% were non-smokers. Non-smoking patients had a high incidence of LC (71.4%), and women showed a significant excess risk compared with non-smoking men in terms of LC risk. Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99).ConclusionsThese results could significantly improve early non-smoker LC diagnosis and treatment for non-smoker patients with malignant nodules. The established multi-omics model is a noninvasive prediction tool for non-smoking malignant pulmonary nodule diagnosis. Validation revealed that these models exhibited excellent discrimination and calibration capacities, especially the first model built using the RF method, suggesting their clinical utility in the early screening and diagnosis of non-smoking LC.

Dataset Information

Detection of circulating genetically abnormal cells in peripheral blood for early diagnosis of non-small cell lung cancer.

Background

Methods

Results

Conclusions

Key points

Publications

Detection of circulating genetically abnormal cells in peripheral blood for early diagnosis of non-small cell lung cancer.

Similar Datasets

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