ArticleNPJ precision oncology2023
ML-based sequential analysis to assist selection between VMP and RD for newly diagnosed multiple myeloma.
Article in NPJ precision oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- Review
- Development and multinational validation of a multiple myeloma-specific comorbidity index using real-world cohorts: CAREMM-2108.Blood cancer journal · 2026Article
- Shaping the future of multiple myeloma with artificial intelligence and digital twins: from concept to clinic.Frontiers in digital health · 2026Review
- Machine learning-based prediction of response to Janus kinase inhibitors in patients with rheumatoid arthritis using clinical data.Frontiers in immunology · 2025Article
- Machine learning-based approach to guide the choice between baricitinib and tocilizumab in critical COVID-19 pneumonia treatment: a retrospective cohort study.Frontiers in medicine · 2025Article
- Recent advances in and applications of ex vivo drug sensitivity analysis for blood cancers.Blood research · 2024Review
- Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma.NPJ digital medicine · 2024Article
- Clinical decision support for chemotherapy-induced neutropenia using a hybrid pharmacodynamic/machine learning model.CPT: pharmacometrics & systems pharmacology · 2023Article
- Using Proteomics Data to Identify Personalized Treatments in Multiple Myeloma: A Machine Learning Approach.International journal of molecular sciences · 2023Article
- Machine Learning Predicts 30-Day Outcome among Acute Myeloid Leukemia Patients: A Single-Center, Retrospective, Cohort Study.Journal of clinical medicine · 2023Article
- Research on predicting the progression of multiple myeloma treated with bortezomib based on multimodal ensemble learning.Digital healthArticle
Corrections and comments
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Authors and funding
12 authors at 8 institutions in 3 countries.
Funding
Abstract
Optimal first-line treatment that enables deeper and longer remission is crucially important for newly diagnosed multiple myeloma (NDMM). In this study, we developed the machine learning (ML) models predicting overall survival (OS) or response of the transplant-ineligible NDMM patients when treated by one of the two regimens-bortezomib plus melphalan plus prednisone (VMP) or lenalidomide plus dexamethasone (RD). Demographic and clinical characteristics obtained during diagnosis were used to train the ML models, which enabled treatment-specific risk stratification. Survival was superior when the patients were treated with the regimen to which they were low risk. The largest difference in OS was observed in the VMP-low risk & RD-high risk group, who recorded a hazard ratio of 0.15 (95% CI: 0.04-0.55) when treated with VMP vs. RD regimen. Retrospective analysis showed that the use of the ML models might have helped to improve the survival and/or response of up to 202 (39%) patients among the entire cohort (N = 514). In this manner, we believe that the ML models trained on clinical data available at diagnosis can assist the individualized selection of optimal first-line treatment for transplant-ineligible NDMM patients.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.