Study on Early Screening of Myeloma Based on Liquid Biopsy cfDNA Fragmentomics Technology. This study aims to construct and validate a set of machine learning models for the early screening of myeloma tailored to the Chinese population, based on plasma cfDNA fragmentomics features, to effectively distinguish myeloma patients from healthy individuals.
Age over 40 years.
Diagnosed with MM, SMM, or MGUS through standard diagnostic procedures.
Voluntarily participating and signed written informed consent.
Pregnant or lactating women.
Prior cancer diagnosis within the past 5 years, or history of any form of systemic cancer therapy (including chemotherapy, targeted therapy, immunotherapy, etc.).
History of organ transplantation, or allogeneic bone marrow or stem cell transplantation.
History of blood transfusion within 7 days prior to blood collection.
Study Population Description Target Population: Chinese adults aged 40 years, including patients with plasma cell dyscrasias and healthy controls. Patient Group: Individuals with a laboratory- and clinically-confirmed diagnosis of MM, SMM, or MGUS following standard diagnostic guidelines. Healthy Controls: Age-matched healthy individuals with no active malignancies or major systemic diseases. General Eligibility Summary: All participants must voluntarily sign a written informed consent form. Individuals with recent blood transfusions (within 7 days), prior histories of other cancers or systemic oncology therapies within 5 years, or histories of organ/allogeneic transplants will be excluded, alongside any candidates deemed ineligible by the investigator.
Cohort
Prospective
Not listed
Sensitivity and Specificity
The early screening model will be developed using the training set; its sensitivity and specificity will then be evaluated in the validation set based on the consistency between the model's classification and clinical diagnosis.
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The First Affiliated Hospital with Nanjing Medical University
Lead sponsor
Nanjing Geneseeq Technology Inc.
Collaborator
This trial is not recruiting at the moment. You can still explore other options:
