Early Screening for Multiple Myeloma Based on Liquid Biopsy cfDNA Fragmentomics

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07684274

Enrollment

290

Registered

2026-07-06

Start date

2026-06-13

Completion date

2027-12-31

Last updated

2026-07-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

MGUS, Multiple Myeloma (MM), Smoldering Multiple Myeloma (SMM)

Brief summary

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

40 Years to No maximum

Healthy volunteers

Yes

Inclusion criteria

* Age over 40 years. * Diagnosed with MM, SMM, or MGUS through standard diagnostic procedures. * Voluntarily participating and signed written informed consent.

Exclusion criteria

* 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. * Poor compliance or potential interference with signing informed consent as judged by the investigator, or any other conditions rendering the subject unsuitable for the study.

Design outcomes

Primary

Measure	Time frame	Description
Sensitivity and Specificity	Baseline	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.

Outcome results