AI-Based Fine Morphological Subtyping of Myeloma Single Cells for Predicting FISH Abnormalities

NCT07410403 | Enrolling By Invitation DMC

• 1 other identifier: 441435122
• Study Type: observational
• Target: 10
• Locations: 1 country
• Sites: 1

Brief Summary

This study developed an artificial intelligence (AI)-based methodology for the quantitative analysis of single-cell morphological data in multiple myeloma (MM). The approach achieves high-precision AI-driven identification and segmentation of myeloma cells, nuclei, cytoplasm, and nucleoli, overcoming the inherent limitations of subjective traditional morphological analysis. Furthermore, integrating this morphological quantification with cytogenetic abnormality analysis of myeloma cells provides an efficient predictive tool for identifying high-risk cytogenetic abnormalities. Leveraging AI-guided selection of genetic testing targets, the research applied a rapid genetic abnormality detection technique utilizing first-drop bone marrow aspirate smears. This methodology achieves orders of magnitude improvements in testing cost, sample preprocessing time and detection sensitivity.

Conditions

Multiple Myeloma

Keywords

artificial intelligence; myeloma; genetic

Outcome Measures

Primary Outcomes (1)

• Cytogenetic result of multiple myeloma

  To predict cytogenetic result of multiple myeloma, using high-precision AI-driven identification and segmentation of myeloma cells, nuclei, cytoplasm, and nucleoli.

  2 days

Study Arms (1)

myeloma

high-precision AI-driven identification and segmentation of myeloma cells, nuclei, cytoplasm, and nucleoli, overcoming the inherent limitations of subjective traditional morphological analysis

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

according to the criteria outlined in the Chinese guidelines for MM diagnosis and management (2024 edition)

You may qualify if:

• according to the criteria outlined in the Chinese guidelines for MM diagnosis and management (2024 edition)

You may not qualify if:

• non-MM

Sponsors & Collaborators

• Fuling Zhou: lead
• Wuhan Central Hospital: collaborator
• The Affiliated Hospital Of Southwest Medical University: collaborator
• Linyi People's Hospital: collaborator

Study Sites (1)

Zhongnan Hospital of Wuhan University

Wuhan, Hubei, 410071, China

Location

Biospecimen

Retention: SAMPLES WITH DNA

bone marrow smears

MeSH Terms

Conditions

Multiple Myeloma; Neoplasms, Plasma Cell

Condition Hierarchy (Ancestors)

Neoplasms by Histologic Type; Neoplasms; Hemostatic Disorders; Vascular Diseases; Cardiovascular Diseases; Paraproteinemias; Blood Protein Disorders; Hematologic Diseases; Hemic and Lymphatic Diseases; Hemorrhagic Disorders; Lymphoproliferative Disorders; Immunoproliferative Disorders; Immune System Diseases

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: June 16, 2025
• First Posted: February 13, 2026
• Study Start: August 1, 2018
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: February 13, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)