Routine blood biomarkers for the detection of multiple myeloma using machine learning

Abstract

Introduction: Primary laboratory tests performed in the diagnosis of multiple myeloma (MM) include bone marrow examination and free light chain assay; however, these may only be ordered after clinical suspicion of disease. In contrast, routine blood test results are readily available. Methods: Machine learning algorithms (ML) combined with routine blood tests were used to detect MM. Feature selection was performed to achieve improved classification performance. The robustness of the classification models was assessed in an internal and external validation data set. To minimize the divergence, the training and validation data sets were combined and used to assess the performance of the ML algorithms. Results:The AdaBoost-DecisionTable produced the best performance (accuracy =94.75%, sensitivity =87.70%, positive predictive value (PPV) =92.50%, F-measure =90.00%, and areas under the receiver operating characteristic curves (AUC) =97.50%) in the training data set using a 10-fold cross-validation. Performance in the validation data sets was affected by the divergence of the data sets, with accuracy greater than 85% and AUC greater than 90% in the validation data sets. The ML algorithm achieved a high accuracy of 92.61%, high AUC (96.80%), a sensitivity value of 85.20%, a PPV value of 88.50%, and an F-measure of 86.80% in a test set that was randomly selected from the combined data set. Conclusions:Combining ML and routine serum biomarkers hold a potential benefit in MM diagnosis.

The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis

“…The type of study was mainly retrospective. There were 8 multicenter studies ( 16 , 23 , 25 , 27 , 34 , 39 , 40 , 42 ) and 4 studies from registered database sources ( 24 , 32 , 33 , 46 ). Disease studies included pan-studies on malignant bone tumors and studies on different specific malignancies, among which 16 articles were on malignant bone tumors (including 2 articles on malignant bone tumors of the spine only), 6 articles on multiple myeloma, 5 articles on osteosarcoma, and 4 articles on chondrosarcoma.…”

“…Six papers ( 16 , 20 , 23 , 38 , 40 , 47 ) involved selective inclusion of cases, three papers ( 20 , 21 , 38 ) were case-control studies, and two papers ( 28 , 43 ) did not specify the type of study, which could lead to potential case selection bias. Three papers ( 25 , 32 , 43 ) were unable to derive a diagnostic fourfold table due to missing data. The risk of bias and clinical applicability evaluation for the rest of the literature were considered low risk.…”