A Machine Learning Approach to Connect Multiple Myeloma Complexity to Early Disease Recurrence

NCT ID: NCT06767254

Last Updated: 2025-01-09

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 200 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-10-30
• Study Completion Date: 2026-08-31

Brief Summary

This is a non-interventional, national, multicenter prospective non-profit observational study aiming at improving the accuracy of risk prediction in multiple myeloma (MM) by applying machine-learning tools for data processing to develop model(s) predicting response to therapy and the probability of early relapse for MM patients.

Detailed Description

Improvements in the therapy of MM have prompted the achievement of very deep clinical responses, with significantly improved outcomes and survival [2]. However, despite significant therapeutic progress, MM remains a challenge, due to the composite pathogenesis and intricate networks of different interacting factors. As a result, a relatively high proportion of NDMM patients across the different therapeutic strategies have a higher risk of disease progression and worse outcomes, independently of the anti-MM regimen received. These patients currently represent un unmet clinical need, with consequent challenges in the management of the disease and identify these patients upfront is an important goal in MM.

At present, risk stratification scores rely on a limited set of clinical and biological variables, not always sufficient to identify patients at a high risk of early disease progression or relapse (i.e., within 12 months from start of first-line therapy). Recently, AI tools have been explored to improve the accuracy of risk prediction, showing that high-risk diseases might be upfront recognized, based on tumor and immune biomarkers [3-4].

By applying Machine Learning (ML) tools for data processing, clinical, genomic, and imaging data from MM patients will be integrated and employed in models aimed at improving the accuracy of MM risk prediction. In this way, ML models will aggregate all tumor- and microenvironment-related information obtained by high-throughput technologies and omics approaches to identify and describe clusters of MM patients that best correlate with the achievement of early progression.

Overall, this study will identify new knowledge to support clinical research and decision-making in MM: precise up-front stratification of patients, based on the whole landscape of MM-related features, could improve understanding of MM individual risk. Results from this study will have an impact on the possibility to access personalized treatment, with predictable overall repercussion on the effective management of MM patients and savings for the National Health System.

Conditions

Multiple Myeloma (MM)

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• Age ≥ 18 years
• Signed Informed Consent form for study participation and personal data processing
• Diagnosis of active multiple myeloma

Exclusion Criteria

• None

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

IRCCS Azienda Ospedaliero-Universitaria di Bologna

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Elena Zamagni, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

IRCCS Azienda Ospedaliero-Universitaria di Bologna

Locations

Istituto Romagnolo per lo Studio dei Tumori "Dino Amadori" - IRST IRCCS

Meldola, Forlì-Cesena, Italy

Site Status: NOT_YET_RECRUITING

IRCCS Azienda Ospedaliero-Universitaria di Bologna

Bologna, , Italy

Site Status: RECRUITING

ARNAS "G. Brotzu" di Cagliari

Cagliari, , Italy

Site Status: NOT_YET_RECRUITING

Azienda Ospedaliera Universitaria Federico II

Napoli, , Italy

Site Status: NOT_YET_RECRUITING

Countries

Italy

Central Contacts

Elena Zamagni, MD, PhD

Role: CONTACT

e.zamagni@unibo.it

+39 051 214 3680

Facility Contacts

Martina Ghetti

Role: primary

martina.ghetti@irst.emr.it

+39 0543 739100

Elena Zamagni, MD, PhD

Role: primary

e.zamagni@unibo.it

+39 051 214 3680

Simona Barbato, Ph.D.

Role: backup

simona.barbato3@unibo.it

+39 051 214 3827

Daniele Derudas

Role: primary

daniele.derudas@aob.it

+39 070 52965520

Barbara Izzo

Role: primary

barbara.izzo@unina.it

+39 081 3737869

Other Identifiers

PNRR-TR1-2023-12378246

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

AIMMer

Identifier Type: -

Identifier Source: org_study_id