To Conduct Multi-omics Integrated Studies in Peripheral Blood, Such as Fragment Omics, Metabolomics and Epigenetics, and Establish Non-invasive Dynamic Follow-up Monitoring Programs During Perioperative and Postoperative Periods (Observational Study)

N/ARecruitingNCT07291921

Peking University People's Hospital100 enrolled

Overview

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.

This project is a prospective observational study designed to comprehensively integrate plasma metabolomic, radiomic, and epigenomic data to develop a predictive model for postoperative recurrence risk in lung cancer. The study will retrospectively enroll 200 patients who underwent radical surgery after neoadjuvant therapy, and prospectively enroll 100 additional post-radical-surgery lung cancer patients who received neoadjuvant treatment as a validation cohort. Peripheral blood samples will be collected at multiple timepoints for metabolomic profiling. Unsupervised clustering, random forest algorithms, and Wilcoxon tests will be applied to identify recurrence-related features and construct a recurrence prediction model.Additionally, using preoperative and first postoperative follow-up CT imaging data, a deep learning-based 3D ResNet will be employed to generate radiomic recurrence risk scores for each patient. Plasma cfDNA will undergo low-pass whole-genome sequencing and methylation analysis to extract multi-dimensional recurrence-associated features. Finally, the study will innovatively utilize the DeepProg deep learning framework to integrate radiomic, cfDNA, and plasma metabolomic data into a non-invasive multi-omics model. Combined with survival data, this model will predict recurrence risk, ultimately achieving high-accuracy stratification of patients' postoperative recurrence probability.

Study Type

OBSERVATIONAL

Enrollment

100

Conditions

Lung Neoplasms Neoadjuvant Therapy

Eligibility

Sex: ALLMin age: 18 YearsMax age: 85 Years

Medical Language ↔ Plain English

Inclusion Criteria: 1. Signed written informed consent. 2. Male or female, aged ≥ 18 and \< 85 years. 3. Radical resection performed, pathologic stage IB-IIIA (8th TNM) non-small-cell lung cancer. 4. Tumor tissue and blood samples obtainable at all protocol-specified time-points. 5. No pure ground-glass nodule on imaging. 6. Completed standard neoadjuvant immunotherapy combined with platinum-based chemotherapy. Exclusion Criteria: 1. Postoperative pathology shows other than NSCLC, including but not limited to benign lesions, small-cell carcinoma, metastasis, or indeterminate/inadequate histology. 2. Insufficient or poor-quality blood or tissue samples. 3. Pure ground-glass nodule on imaging. 4. History of any malignancy within the past 5 years. 5. Contraindication to surgery preventing radical resection. 6. Non-radical (R2) resection. 7. Pathologic stage IIIB-N3, IIIC, or IV on paraffin sections. 8. Refusal or withdrawal of informed consent. 9. Any condition deemed unsuitable by the investigator (e.g., perioperative blood transfusion, severe psychiatric disorder precluding follow-up).

Outcomes

Primary Outcomes

Two-year recurrence-free survival rate

Time frame: Time from curative surgery to confirmation of clinical progression (recurrence or metastasis) within two years

Secondary Outcomes

Overall survival

Time frame: Time from curative surgery to confirmation of death (any cause)，assessed up to 60 months.

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.

Time frame: two years