ArticleBlood cancer journal2022
Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group.
Article in Blood cancer journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Effect of daratumumab on patients with high-risk cytogenetic multiple myeloma: a systematic review and meta-analysis of randomized controlled trials.Frontiers in immunology · 2026Pooled it
- The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis.Frontiers in oncology · 2023Pooled it
- Automatic phenotyping of emergency department patients with incidental hepatic steatosis: A machine learning clustering analysis.The American journal of emergency medicine · 2026Article
- Development and temporal validation of a machine learning-based model for predicting renal impairment in multiple myeloma: a single-center retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Review
- Observational
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- Shaping the future of multiple myeloma with artificial intelligence and digital twins: from concept to clinic.Frontiers in digital health · 2026Review
- Omics and artificial intelligence integration for stratifying blast crisis CML using COSMIC signatures and pan-cancer precision drug repurposing.World journal of clinical oncology · 2025Article
- Machine learning risk stratification strategy for multiple myeloma: Insights from the EMN-HARMONY Alliance platform.HemaSphere · 2025Article
- A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data.Scientific reports · 2025Article
- Review
- Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma.NPJ digital medicine · 2024Article
- Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis.HemaSphere · 2023Article
- Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model.HemaSphere · 2022Article
- Research on predicting the progression of multiple myeloma treated with bortezomib based on multimodal ensemble learning.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors at 17 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The International Staging System (ISS) and the Revised International Staging System (R-ISS) are commonly used prognostic scores in multiple myeloma (MM). These methods have significant gaps, particularly among intermediate-risk groups. The aim of this study was to improve risk stratification in newly diagnosed MM patients using data from three different trials developed by the Spanish Myeloma Group. For this, we applied an unsupervised machine learning clusterization technique on a set of clinical, biochemical and cytogenetic variables, and we identified two novel clusters of patients with significantly different survival. The prognostic precision of this clusterization was superior to those of ISS and R-ISS scores, and appeared to be particularly useful to improve risk stratification among R-ISS 2 patients. Additionally, patients assigned to the low-risk cluster in the GEM05 over 65 years trial had a significant survival benefit when treated with VMP as compared with VTD. In conclusion, we describe a simple prognostic model for newly diagnosed MM whose predictions are independent of the ISS and R-ISS scores. Notably, the model is particularly useful in order to re-classify R-ISS score 2 patients in 2 different prognostic subgroups. The combination of ISS, R-ISS and unsupervised machine learning clusterization brings a promising approximation to improve MM risk stratification.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.