Peking University People's Hospital
Beijing, Beijing Municipality, 100044, China
Location status: Recruiting
NCT Number: NCT07291921
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.
Interested in participating?
Request Info18 year–85 year
All sexes
Observational
Beijing, Beijing Municipality, 100044, China
Location status: Recruiting
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: Time from curative surgery to confirmation of clinical progression (recurrence or metastasis) within two years
Time frame: Time from curative surgery to confirmation of death (any cause),assessed up to 60 months.
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.
Contact information is provided by the study sponsor or research team.
Kezhong Chen
CONTACT
Yue He
CONTACT
Peking University People's Hospital
Other
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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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