Research on Early Screening and Diagnosis of Pulmonary Nodules Based on Novel Non-invasive Technologies.

Condition(s) studied

Investigational drug(s) / intervention(s)

Employing multi-omics diagnostic approaches to enhance diagnostic efficacy: The study will first systematically evaluate the efficacy and accessibility of metabolomics and radiomics in the early screening and diagnosis of lung cancer through retrospective data analysis of prospective databases and prospective cohort validation. Based on large-scale detection data, a novel multidimensional early-stage lung cancer screening system will be established. This system will employ metabolomics as the initial screening method, supplemented by multi-omics approaches including radiomics, cfDNA methylation fragment detection, TCR detection, and metabolomics for differential diagnosis and confirmation.

Study summary

This is a prospective observational study designed to address the clinical challenge posed by the high false-positive rate associated with CT imaging in early lung cancer screening.

The primary objective is to develop a multi-omics technology for early lung cancer screening, leveraging \*\*exhaled breath metabolomics, plasma metabolomics, radiomics, and liquid biopsy. Based on large-sample detection data, the study aims to construct a \*\*multi-dimensional, sequential decision-making system\*\*. This system utilises the high accessibility of metabolomics for primary screening, combined with radiomics and ctDNA technologies for subsequent \*\*differentiation and definitive diagnosis.

The research plans to prospectively enrol 300 patients with non-small cell lung cancer, along with corresponding subjects with benign nodules and healthy controls. By optimising the model using machine learning and deep learning algorithms (such as SVM, HRNet, and PAResNet), the ultimate goal is to establish a novel lung cancer early screening system characterised by \*\*high sensitivity, high accuracy, and high accessibility\*\*, enabling the precise differentiation and screening of healthy individuals, benign pulmonary nodules, and early-stage lung cancer.

Eligibility

Primary outcome measure(s)

• diagnostic sensitivity — From enrollment to the end of treatment at 6-8 weeks
  The primary research indicators in this study focus on evaluating the diagnostic efficacy of multi-omics models for early-stage lung cancer. Firstly, diagnostic sensitivity serves as the core metric to assess the model's ability to correctly identify lung cancer patients, with a target value set at no less than 85%. Diagnostic specificity measures the model's capacity to correctly exclude non-lung cancer individuals, with a target value set at no less than 90%. The area under the receiver operating characteristic curve serves as a comprehensive indicator of the model's discriminative capability, with a target value exceeding 0.90 to ensure robust overall diagnostic performance. Regarding early detection capability, the detection rate for stage I lung cancer represents a key primary indicator in this study, specifically encompassing the detection of stage IA and IB lung cancer. This is because patients at this stage typically present with the optimal surgical resection opportunities an

Trial sites (1)

Other trials for the same condition