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Non-Invasive Postoperative Recurrence Monitoring After Neoadjuvant Immunotherapy in Lung Cancer

NCT ID: NCT07291921

Last Updated: 2025-12-18

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

100 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-05-08

Study Completion Date

2027-10-31

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Lung Neoplasms Neoadjuvant Therapy Immunotherapy Minimal Residual Disease

Keywords

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NSCLC Neoadjuvant immunotherapy Perioperative monitoring Liquid biopsy MRD Minimal residual disease Multiple omics

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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High-risk group

High-risk recurrence groups identified by the multi-omics model

No interventions assigned to this group

Low-risk group

Low-risk recurrence groups identified by the multi-omics model

No interventions assigned to this group

Eligibility Criteria

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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).
Minimum Eligible Age

18 Years

Maximum Eligible Age

85 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Peking University People's Hospital

OTHER

Sponsor Role lead

Responsible Party

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Chen KeZhong

Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Peking University People's Hospital

Beijing, Beijing Municipality, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Kezhong Chen

Role: CONTACT

Phone: +86-010-88325983

Email: [email protected]

Yue He

Role: CONTACT

Phone: +86-010-88325983

Email: [email protected]

Facility Contacts

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Yue He

Role: primary

Other Identifiers

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BRWEP2024W034080204-1

Identifier Type: -

Identifier Source: org_study_id