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NCT Number: NCT07291921

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)

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.

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Key information

Age range

18 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Peking University People's Hospital

Beijing, Beijing Municipality, 100044, China

Location status: Recruiting

Location contact

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.
  • No pure ground-glass nodule on imaging.
  • Completed standard neoadjuvant immunotherapy combined with platinum-based chemotherapy.

Exclusion criteria

  • 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.
  • Contraindication to surgery preventing radical resection.
  • Non-radical (R2) resection.
  • Pathologic stage IIIB-N3, IIIC, or IV on paraffin sections.
  • Refusal or withdrawal of informed consent.
  • Any condition deemed unsuitable by the investigator (e.g., perioperative blood transfusion, severe psychiatric disorder precluding follow-up).

Treatment and study plan

Primary outcomes

  1. 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

  1. Overall survival

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

  2. Timely diagnosis rate by the novel MRD monitoring technique

    Time frame: two years

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Sponsors and collaborators

Lead sponsor

Peking University People's Hospital

Other

Registry information

Important dates

Study start
2025
Primary completion
2027
Study completion
2027
First posted
Dec 18, 2025
Registry last updated
Mar 3, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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