Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection
Abstract
:Simple Summary
Abstract
1. Introduction
General Considerations on Machine Learning and Deep Learning
2. Machine Learning and Multiple Myeloma Diagnosis
2.1. Machine Learning and Bone Lesions Identification in Multiple Myeloma Patients
2.2. Machine Learning and Multiple Myeloma Prognosis
2.3. Machine Learning and Prediction of Clinical Drug Response
2.4. Machine Learning and Apoptosis in Multiple Myeloma
3. Conclusions, Challenges, and Perspectives
Author Contributions
Funding
Conflicts of Interest
References
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Diagnosis | |||
---|---|---|---|
Parameters | AI Tools | Ref. | Key Findings |
Blood and biochemical exams | Gradient boosting decisional tree | [24] | A ML approach on standard laboratory findings enhances the percentage of early detection |
Differential cell counts of bone marrow aspirate | VGG16 convolutional network | [25] | Bone marrow aspirate differential counts employing ML techniques |
Cytofluorimetric analysis of bone marrow aspirate | FlowCAP | [26] | Computerized methods for cytofluorimetric analysis |
Gradient boosting machine technique | [27] | Classification of plasma cell dyscrasias by combining AI and flow cytometry | |
Laser-induced breakdown spectroscopy analysis | Quadratic discriminant analysis, k-Nearest Neighbour | [28] | Diagnosis of malignancies using serum-based laser induced breakdown spectroscopy and chemometric methods |
K-Nearest Neighbour, Support Vector Machine, Artificial Neural Networks | [29] | Diagnosis of malignancies using serum-based laser induced breakdown spectroscopy in combination with ML methods can serve as fast technique for MM diagnosis and staging | |
Bone Lesions Identification | |||
Techniques | AI tools | Ref. | Key findings |
PET and CT | Convolutional neural network (v-Net, w-Net) | [30] | 68Ga-Pentixaflor PET/CT and DL techniques to detect MM whole-body bone lesions |
PET and CT | Random Forest | [31] | Radiomics analysis of 18-FDG PET/CT image with ML overcame the limitations of visual analysis |
MRI | Naïve Bayes, Support Vector Machine, k-Nearest Neighbour, Random Forest, Artificial Neural Networks | [32] | ML radiomics is able to differentiate between MM and metastasis subtypes of lumbar vertebra lesions |
SELDI-TOF-MS (mass peaks with mass-to-charge ratios) | Random Forest, Partial least squares discriminant analysis | [33] | SELDI-TOF-MS and ML tools discriminate MM patients with and without skeletal involvement |
Prognosis | ||||
---|---|---|---|---|
Parameters | AI Tools | Ref. | Key Findings | |
Laboratory parameters | k-adaptive partitioning | [54] | AI-supported modified risk staging for multiple myeloma | |
Beta2microglobulin | Infinicyt software | [55] | Next-Generation Flow and ML for highly sensitive detection of minimal residual disease | |
Gene expression profile, ISS stage, first line therapy | Random Forest | [56] | Survival prediction and treatment optimization using ML models based on clinical and gene expression data | |
mRNA expression-based steamness index | One-class logistic regression | [57] | Analysis of gene expression via one-class logistic regression ML identifies stemness features in MM | |
Prediction of Response to Treatment | ||||
Drugs | Parameters | AI tools | Ref. | |
Bortezomib, carfilzomib, ixazomib, oprozomib | Gene expression profile | Random Forest | [58] | A gene expression signature distinguishes resistance to proteasome inhibitors |
Proteasome inhibitors | Gene complex | Simulated Treatment learning signature | [59] | Gene networks constructed using simulated treatment learning can predict proteasome inhibitor benefit |
PAD, VCD | Gene evaluation | Random Forest, Support Vector Machine, Ridge Regression, Binomial Naïve Bayes, Multi-layer perception | [60] | ML applicability for classification of chemotherapy response using 53 MM RNA-sequencing profiles |
Five first-line treatments (Bor-Cyc-Dex, Bor-Dex, Bor-Len-Dex, Len-Dex, Non-treatment) | Clinical markers, gene evaluation | Multi Learning Training approach | [61] | ML predicts treatment sensitivity in MM based on molecular and clinical information coupled with drug response |
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Share and Cite
Allegra, A.; Tonacci, A.; Sciaccotta, R.; Genovese, S.; Musolino, C.; Pioggia, G.; Gangemi, S. Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection. Cancers 2022, 14, 606. https://doi.org/10.3390/cancers14030606
Allegra A, Tonacci A, Sciaccotta R, Genovese S, Musolino C, Pioggia G, Gangemi S. Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection. Cancers. 2022; 14(3):606. https://doi.org/10.3390/cancers14030606
Chicago/Turabian StyleAllegra, Alessandro, Alessandro Tonacci, Raffaele Sciaccotta, Sara Genovese, Caterina Musolino, Giovanni Pioggia, and Sebastiano Gangemi. 2022. "Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection" Cancers 14, no. 3: 606. https://doi.org/10.3390/cancers14030606