Artificial intelligence for predicting transformation of monoclonal gammopathy of undetermined significance to multiple myeloma: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objectives The purpose of this study was to assess the empirical evidence of the efficacy of AI in predicting the transformation of Monoclonal Gammopathy of Undetermined Significance (MGUS) to Multiple Myeloma (MM). Methods A comprehensive and systematic electronic database search was performed in Scopus, PubMed, Cochrane Library, ScienceDirect, and Google Scholar. Modified PICOS criteria were used to screen and select the eligible studies from the potential articles retrieved from the database search. Studies were considered if they included patients with MGUS whose progression was monitored using AI approaches. The selected studies were assessed for risk of bias using the Newcastle-Ottawa Scale (NOS). Data was then procedurally extracted and analyzed. Results The study selection process identified nine studies, including 42,853 patients. Ensemble methods (ElasticNet, GBM, Random Forest) consistently outperformed traditional risk stratification systems, with AI models achieving C-statistics of 0.692-0.879 compared to 0.533-0.670 for conventional IMWG/2-20-20 criteria. The meta-analysis demonstrated the favourable predictive performance of AI models for predicting MGUS to MM Transformation, with a pooled AUC of 0.824 (95% CI: 0.785-0.858, p< 0.001). The multi- modal integration of clinical parameters, genomic profiles, and cytogenetic markers enhanced the discriminative capacity. Conclusion AI models demonstrated high prediction accuracy for the transformation of MGUS to MM. In addition, various AI models integrate multimodal biological data, transforming complex genomic, cytogenetic, and clinical information into actionable risk assessments influencing surveillance intensity and intervention timing. KEYWORDS Artificial Intelligence; Monoclonal Gammopathy of Undetermined Significance; Multiple Myeloma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".