This project aims to innovatively integrate multi-omics data, including plasma metabolomics, radiomics, and cfDNA multi-level information, combined with survival data (e.g., RFS), to establish a novel multidimensional approach for noninvasive postoperative recurrence monitoring in lung cancer using artificial intelligence algorithms. The goal is to develop a new noninvasive recurrence monitoring system for lung cancer.
Signed written informed consent.
Male or female, aged ≥ 18 and < 85 years.
Radical resection performed, pathologic stage IB-IIIA (8th TNM) non-small-cell lung cancer.
Tumor tissue and blood samples obtainable at all protocol-specified time-points.
Postoperative pathology shows other than NSCLC, including but not limited to benign lesions, small-cell carcinoma, metastasis, or indeterminate/inadequate histology.
Insufficient or poor-quality blood or tissue samples.
Pure ground-glass nodule on imaging.
History of any malignancy within the past 5 years.
Patients with pulmonary nodules who underwent radical treatment at our center between May 2025 and October 2027, met all eligibility criteria, and provided informed consent.
Cohort
Prospective
Not listed
Two-year recurrence-free survival rate
Overall survival
Timely diagnosis rate by the novel MRD monitoring technique
The proportion of patients with recurrence signals detected by non-invasive methods prior to clinical confirmation of recurrence/metastasis, and quantify the mean lead time.
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