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

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

Inclusion Criteria

1. Age \>18 years old.
2. 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.
3. Pulmonary nodular lesions identified by chest CT with a diameter \< 3 cm.
4. Pulmonary nodular lesions must be surgically resected and have complete, definitive pathological information regarding their benign or malignant nature.
5. No prior history of malignant tumors.
6. Has not received anti-tumor treatments such as radiotherapy, chemotherapy, or targeted therapy.
7. Signed informed consent.

Exclusion Criteria

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

Conditions2

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