Precision patient selection for improved detection of circulating genetically abnormal cells in pulmonary nodules

Abstract

Circulating genetically abnormal cells (CACs) have emerged as a promising biomarker for the early diagnosis of lung cancer, particularly in patients with pulmonary nodules. However, their performance may be suboptimal in certain patient populations. This study aimed to refine patient selection to improve the detection of CACs in pulmonary nodules. A retrospective analysis was conducted on 241 patients with pulmonary nodules who had undergone pathological diagnosis through surgical tissue specimens. Utilizing consensus clustering analysis, the patients were categorized into three distinct clusters. Cluster 1 was characterized by older age, larger nodule size, and a higher prevalence of hypertension and diabetes. Notably, the diagnostic efficacy of CACs in Cluster 1 surpassed that of the overall patient population (AUC: 0.855 vs. 0.689, P = 0.044). Moreover, for Cluster 1, an integrated diagnostic model was developed, incorporating CACs, sex, maximum nodule type, and maximum nodule size, resulting in a further improved AUC of 0.925 (95% CI 0.846-1.000). In conclusion, our study demonstrates that CACs detection shows better diagnostic performance in aiding the differentiation between benign and malignant nodules in older patients with larger pulmonary nodules and comorbidities such as diabetes and hypertension. Further research and validation are needed to explore how to better integrate CACs detection into clinical practice.

Keywords: Circulating genetically abnormal cells; Early diagnosis; Lung cancer; Precision patient selection; Pulmonary nodule.

Fig. 1

Flowchart of patients enrolled.

Fig. 2

Consensus matrix heat maps for different k values. (A) k = 2. (B) k = 3. (C) k = 4. (D) k = 5. (E) k = 6. (F) k = 7. (G) k = 8. (H) k = 9. (I) k = 10. The color changes from dark blue to white, indicating a gradual decrease in consistency. The deepest blue color signifies a state of absolute consensus, where two individuals consistently cluster together. Conversely, the presence of white color indicates a perfect consensus in which the two individuals invariably group separately. k is the number of clusters. When k = 3, clearly defined boundaries in the heat map indicate high clustering stability.

Fig. 3

The relative change in area under the conditional density function (CDF) curve of consensus cluster analysis when cluster number changes from k to k + 1. When k = 3, a significant decrease in the curve suggests optimal k = 3.

Fig. 4

Receiver operating characteristic curve (ROC) of circulating genetically abnormal cells (CACs). (A) ROC of CACs in overall patients. (B) ROC of CACs in Cluster (1) (C) ROC of CACs in Cluster (2) (D) ROC of CACs in Cluster 3.

Fig. 5

ROC of the joint diagnostic model (including CACs, sex, maximum nodule type, and maximum nodule size).