ArticleFrontiers in oncology2021
Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Review
- Multi-center validation of a machine learning model for early detection of monoclonal immunoglobulin-related disorders using routine laboratory data.Frontiers in immunology · 2026Article
- Risk factors for severe COVID-19 and development of a predictive model.BMC pulmonary medicine · 2025Article
- A differential diagnostic model based on immunological evaluation and routine laboratory tests: distinguishing multiple myeloma from other disorders with aberrant immunoglobulin elevation.Discover oncology · 2025Article
- A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data.Scientific reports · 2025Article
- The mean corpuscular volume (MCV) is a hematological biomarker associated with COVID-19 mortality risk.Biomarkers in medicine · 2025Article
- Assessing serum thrombopoietin for enhanced diagnosis of ITP, AA, and MDS using machine learning: A retrospective cohort study.Annals of hematology · 2025Article
- Detection of early relapse in multiple myeloma patients.Cell division · 2025Article
- IL-38 as a Novel Biomarker in Multiple Myeloma Patients: A Prospective Clinical Evaluation.Cancer management and research · 2025Article
- Advancements in Hematologic Malignancy Detection: A Comprehensive Survey of Methodologies and Emerging Trends.TheScientificWorldJournal · 2025Review
- A multicenter study on developing a prognostic model for severe fever with thrombocytopenia syndrome using machine learning.Frontiers in microbiology · 2025Article
- Single Cell Transcriptomics Genomics Based on Machine Learning Algorithm: Constructing and Validating Neutrophil Extracellular Trap Gene Model in COPD.International journal of general medicine · 2025Article
- Review
- Article
- Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests.Frontiers in oncology · 2024Article
- Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.American journal of nuclear medicine and molecular imaging · 2024Review
- Integrating artificial intelligence into haematology training and practice: Opportunities, threats and proposed solutions.British journal of haematology · 2022Article
- Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection.Cancers · 2022Review
- Metabolic Volume Measurements in Multiple Myeloma.Metabolites · 2021Review
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveIn order to enhance the detection rate of multiple myeloma and execute an early and more precise disease management, an artificial intelligence assistant diagnosis system is developed.
methods4,187 routine blood and biochemical examination records were collected from Shengjing Hospital affiliated to China Medical University from January 2010 to January 2020, which include 1,741 records of multiple myeloma (MM) and 2,446 records of non-myeloma (infectious diseases, rheumatic immune system diseases, hepatic diseases and renal diseases). The data set was split into training and test subsets with the ratio of 4:1 while connecting hemoglobin, serum creatinine, serum calcium, immunoglobulin (A, G and M), albumin, total protein, and the ratio of albumin to globulin data. An early assistant diagnostic model of MM was established by Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Deep Neural Networks (DNN), and Random Forest (RF). Out team calculated the precision and recall of the system. The performance of the diagnostic model was evaluated by using the receiver operating characteristic (ROC) curve.
resultsBy designing the features properly, the typical machine learning algorithms SVM, DNN, RF and GBDT all performed well. GBDT had the highest precision (92.9%), recall (90.0%) and F1 score (0.915) for the myeloma group. The maximized area under the ROC (AUROC) was calculated, and the results of GBDT (AUC: 0.975; 95% confidence interval (CI): 0.963-0.986) outperformed that of SVM, DNN and RF.
conclusionThe model established by artificial intelligence derived from routine laboratory results can accurately diagnose MM, which can boost the rate of early diagnosis.
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