Abstract 844P
Background
The objective of this study is to apply machine learning (ML) models to multiple myeloma(MM) patient-level data to evaluate if ML models can identify high-risk features linked to faster disease progression.
Methods
Data for 15,931 MM patients in France, Spain, UK, Germany, and Italy were extracted from Oncology Dynamics (OD), a large cross-sectional survey that collects drug-treated patient-level data through a panel of cancer specialists. The analyses include 6 years (2017-2022) of records. Progression-free survival (PFS) was measured as the time from start of therapy to date of progression. The underlying data included treatment regime, line of therapy (LOT), ECOG status, stem cell transplant (SCT) eligibility, Durie-Salmon stage, cytogenetic risk, and age and gender. Machine Learning-based survival models (Gradient Boosted Survival Trees, Random Survival Forest) were applied to predict outcomes. Additionally, Shapley values were calculated to assess the model’s decision making and identify key risk drivers.
Results
Data was split into Training (80%) and Test Data (20%). The output of the model was the ML derived risk score: higher risk score was associated with shorter PFS. Model performance was good (Harrell’s C-Index = 0.741). Data shows that higher LOTs and not being eligible for SCT are associated with increased risk of disease progression. Inversely, being in the 1st LOT, ECOG asymptomatic and certain drug regimens (e.g., Lenalidomide or Dexamethasone/Lenalidomide) are associated with longer PFS.
Conclusions
This is a first successful step in leveraging OD data for the development of a prognostic score for MM patients using machine learning algorithms. ML models applied to secondary market research can support optimal disease management and planning of clinical trials. Also, given the heterogeneity of treatment plans across Europe, further investigation into country-specific differences in PFS may be studied.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
IQVIA.
Funding
Has not received any funding.
Disclosure
M.L. Pleguezuelo Witte, M. Muller, J. Hartmann, C. Ricarte, T. Moehler, M. Lopez, K. Jindal: Financial Interests, Personal, Full or part-time Employment: IQVIA.
Resources from the same session
957P - Interim report of Notable-HCC: A phase Ib study of neoadjuvant PD-1 with stereotactic body radiotherapy in patients with resectable hepatocellular carcinoma (HCC)
Presenter: Mingming Li
Session: Poster session 18
959P - Combination therapy of envafolimab and suvemcitug in patients with hepatocellular carcinoma (HCC): Results from a phase II clinical trial
Presenter: Lixia Ma
Session: Poster session 18
960P - Personalized circulating tumor DNA (ctDNA) monitoring for recurrence detection and treatment response assessment in hepatocellular carcinoma (HCC)
Presenter: Maen Abdelrahim
Session: Poster session 18
961P - Blood circulating Galectin-3 is a prognostic biomarker in hepatocellular carcinoma
Presenter: Shadi Chamseddine
Session: Poster session 18
962P - SBRT improves the efficacy of immuno-checkpoint inhibitors for hepatocellular carcinoma through the activation of IL-6/JAK1-STAT3/PD-L1 axis mediated by MBD3 degradation
Presenter: Weiwei Yan
Session: Poster session 18
963P - Discovery and validation of cfDNA methylation, AFP and ctDNA mutation for the early detection of hepatocellular carcinoma: A multicenter prospective study (ASCEND-Hep)
Presenter: Mingxin Pan
Session: Poster session 18
966P - Potential role of neuropilin-1 in the prognosis, development and risk of invasion in hepatocellular carcinoma patients
Presenter: Tania Payo-Serafín
Session: Poster session 18
967P - The effect of prognosis value of EZH2 (Enhancer Of Zeste Homologue) staining in hepatocellular cancer
Presenter: Mehmet Kidi
Session: Poster session 18