Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules

Abstract

Background The accuracy of CT and tumour markers in screening lung cancer needs to be improved. Computer-aided diagnosis has been reported to effectively improve the diagnostic accuracy of imaging data, and recent studies have shown that circulating genetically abnormal cell (CAC) has the potential to become a novel marker of lung cancer. The purpose of this research is explore new ways of lung cancer screening. Methods From May 2020 to April 2021, patients with pulmonary nodules who had received CAC examination within one week before surgery or biopsy at First Affiliated Hospital of Zhengzhou University were enrolled. CAC counts, CT scan images, serum tumour marker (CEA, CYFRA21–1, NSE) levels and demographic characteristics of the patients were collected for analysis. CT were uploaded to the Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) to assess the malignancy probability of nodules. We compared diagnosis based on PNAIDS, CAC, Mayo Clinic Model, tumour markers alone and their combination. The combination models were built through logistic regression, and was compared through the area under (AUC) the ROC curve. Results A total of 93 of 111 patients were included. The AUC of PNAIDS was 0.696, which increased to 0.847 when combined with CAC. The sensitivity (SE), specificity (SP), and positive (PPV) and negative (NPV) predictive values of the combined model were 61.0%, 94.1%, 94.7% and 58.2%, respectively. In addition, we evaluated the diagnostic value of CAC, which showed an AUC of 0.779, an SE of 76.3%, an SP of 64.7%, a PPV of 78.9%, and an NPV of 61.1%, higher than those of any single serum tumour marker and Mayo Clinic Model. The combination of PNAIDS and CAC exhibited significantly higher AUC values than the PNAIDS (P = 0.009) or the CAC (P = 0.047) indicator alone. However, including additional tumour markers did not significantly alter the performance of CAC and PNAIDS. Conclusions CAC had a higher diagnostic value than traditional tumour markers in early-stage lung cancer and a supportive value for PNAIDS in the diagnosis of cancer based on lung nodules. The results of this study offer a new mode of screening for early-stage lung cancer using lung nodules.

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

“…In the test phase, the implementation of Artificial Neural Networks (ANNs) alongside serum protein panels (β2-microglobulin, CEA, gastrin, CA125, NSE, sIL-6R, and three metal ions: Cu 2+ /Zn 2+ , Ca 2+ , and Mg 2+ ) demonstrated a commendable prediction rate of 85%. Additionally, incorporating clinical parameters (such as symptoms, risk factors, smoking, and kitchen environment) resulted in an increased prediction rate of 87.3% [43]. On combining the Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS), which analyzes CT images, along with tumor markers (TM), the predictive models had the highest specificity (96.1%), whereas integration with circulating abnormal cells has shown to have a specificity of 94.1% [44].…”