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

Official title

Research on Precise Early Screening and Diagnosis of Pulmonary Nodules Based on a Novel Multidimensional Non-invasive Approach

Overview

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.

Detailed description

Lung cancer is one of the malignant tumours with a poor prognosis. Data from 61 countries globally show that the 5-year survival rate for lung cancer patients is only 10.0% to 20.0%. Since 2005, China has launched several major national public health service projects, including lung cancer screening initiatives such as the Rural Cancer Early Diagnosis and Treatment Program and the Urban Cancer Early Diagnosis and Treatment Program, gradually establishing a national network for lung cancer screening and early diagnosis/treatment. Although China has made significant progress in the diagnosis and treatment of lung cancer, the 5-year survival rate for the Chinese population was 19.7% between 2012 and 2015. The prognosis for lung cancer remains poor, making early diagnosis and treatment an essential guide for our research to improve patient survival.

CT scanning has become the recognised standard tool for lung cancer screening and early diagnosis. However, the false-positive rate for highly suspected nodules remains high, at 30% to 40%, and the accuracy of preoperative lesion assessment needs improvement. Therefore, establishing a highly sensitive, highly specific, minimally invasive, or even non-invasive, precise diagnostic method for early lung cancer, thereby avoiding unnecessary surgery, is a critical clinical problem urgently needing resolution, and is of great significance for advancing China's level of early lung cancer diagnosis.

This study will first systematically evaluate the efficacy and accessibility of \*\*exhaled breath metabolomics, plasma metabolomics, and radiomics in early lung cancer screening and diagnosis using a prospective database. Based on large-sample detection data, we will construct a novel multi-dimensional stereo lung cancer early screening system. This system uses exhaled breath metabolomics and plasma metabolomics for initial screening. Then it integrates multiple omics technologies, including radiomics, cfDNA methylation and fragmentomics, and the original metabolomics data, for differentiation and definitive diagnosis. Modelling algorithms will be optimised using methods such as cross-validation, internal validation, and stratified validation to form a stable lung cancer screening system.

Interventions

• Diagnostic test 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 rad

Primary outcome measures

• diagnostic sensitivity [Time frame: From enrollment to the end of treatment at 6-8 weeks]

Secondary outcome measures (1)

• Positive predictive value; negative predictive value and et al. [Time frame: 35 months]

Eligibility criteria

Inclusion criteria

• Age >18 years old.
• Availability of both exhaled breath and peripheral blood samples, and raw CT image data; the collection time is within one month before biopsy or surgical resection, and the subject has not received any treatment in between.
• Pulmonary nodular lesions identified by chest CT with a diameter < 3 cm.
• Pulmonary nodular lesions must be surgically resected and have complete, definitive pathological information regarding their benign or malignant nature.
• No prior history of malignant tumors.
• Has not received anti-tumor treatments such as radiotherapy, chemotherapy, or targeted therapy.
• Signed informed consent.

Exclusion criteria

• Missing clinical data or incomplete sample collection.
• Presence or suspicion of active infection or other severe co-morbidities.
• Abnormal liver or kidney function.
• Indefinite or inconclusive postoperative pathological results.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model

Cohort

Study locations

China · 1 center

• Peking University People's Hospital — Beijing

Identifiers

NCT: NCT07370077 · 2023XAGG0071

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗