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El Alaoui Y, Elomri A, Qaraqe M, Padmanabhan R, Yasin Taha R, El Omri H, El Omri A, Aboumarzouk O. A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects. J Med Internet Res 2022; 24:e36490. [PMID: 35819826 PMCID: PMC9328784 DOI: 10.2196/36490] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 05/14/2022] [Accepted: 05/29/2022] [Indexed: 12/23/2022] Open
Abstract
Background Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management. Objective This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer. Methods We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model. Results Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review. Conclusions The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.
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Affiliation(s)
- Yousra El Alaoui
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Adel Elomri
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Marwa Qaraqe
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Regina Padmanabhan
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Ruba Yasin Taha
- National Center for Cancer Care and Research, Hamad Medical Corporation, Doha, Qatar
| | - Halima El Omri
- National Center for Cancer Care and Research, Hamad Medical Corporation, Doha, Qatar
| | - Abdelfatteh El Omri
- Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha, Qatar
| | - Omar Aboumarzouk
- Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha, Qatar.,College of Medicine, Qatar University, Doha, Qatar.,College of Medicine, University of Glasgow, Glasgow, United Kingdom
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Mengxuan S, Fen Z, Runming J. Novel Treatments for Pediatric Relapsed or Refractory Acute B-Cell Lineage Lymphoblastic Leukemia: Precision Medicine Era. Front Pediatr 2022; 10:923419. [PMID: 35813376 PMCID: PMC9259965 DOI: 10.3389/fped.2022.923419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/02/2022] [Indexed: 12/05/2022] Open
Abstract
With the markedly increased cure rate for children with newly diagnosed pediatric B-cell acute lymphoblastic leukemia (B-ALL), relapse and refractory B-ALL (R/R B-ALL) remain the primary cause of death worldwide due to the limitations of multidrug chemotherapy. As we now have a more profound understanding of R/R ALL, including the mechanism of recurrence and drug resistance, prognostic indicators, genotypic changes and so on, we can use newly emerging technologies to identify operational molecular targets and find sensitive drugs for individualized treatment. In addition, more promising and innovative immunotherapies and molecular targeted drugs that are expected to kill leukemic cells more effectively while maintaining low toxicity to achieve minimal residual disease (MRD) negativity and better bridge hematopoietic stem cell transplantation (HSCT) have also been widely developed. To date, the prognosis of pediatric patients with R/R B-ALL has been enhanced markedly thanks to the development of novel drugs. This article reviews the new advancements of several promising strategies for pediatric R/R B-ALL.
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Affiliation(s)
- Shang Mengxuan
- Department of Pediatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhou Fen
- Department of Pediatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jin Runming
- Department of Pediatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Karami K, Akbari M, Moradi MT, Soleymani B, Fallahi H. Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques. PLoS One 2021; 16:e0254976. [PMID: 34288963 PMCID: PMC8294525 DOI: 10.1371/journal.pone.0254976] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 07/07/2021] [Indexed: 12/26/2022] Open
Abstract
This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify the most suitable factors in assessing the survival of AML patients. Here, six data mining algorithms including Decision Tree, Random Forrest, Logistic Regression, Naive Bayes, W-Bayes Net, and Gradient Boosted Tree (GBT) are employed for the detection model and implemented using the common data mining tool RapidMiner and open-source R package. To improve the predictive ability of our model, a set of features were selected by employing multiple feature selection methods. The accuracy of classification was obtained using 10-fold cross-validation for the various combinations of the feature selection methods and machine learning algorithms. The performance of the models was assessed by various measurement indexes including accuracy, kappa, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC). Our results showed that GBT with an accuracy of 85.17%, AUC of 0.930, and the feature selection via the Relief algorithm has the best performance in predicting the survival rate of AML patients.
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Affiliation(s)
- Keyvan Karami
- Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
- Department of Animal Science, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboubeh Akbari
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mohammad-Taher Moradi
- Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Bijan Soleymani
- Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
- * E-mail: , (HF); (BS)
| | - Hossein Fallahi
- Department of Biology, School of Sciences, Razi University, Kermanshah, Iran
- * E-mail: , (HF); (BS)
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Nguyen NHK, Wu H, Tan H, Peng J, Rubnitz JE, Cao X, Pounds S, Lamba JK. Global Proteomic Profiling of Pediatric AML: A Pilot Study. Cancers (Basel) 2021; 13:cancers13133161. [PMID: 34202615 PMCID: PMC8268478 DOI: 10.3390/cancers13133161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/14/2021] [Accepted: 06/19/2021] [Indexed: 12/17/2022] Open
Abstract
Acute Myeloid Leukemia (AML) is a heterogeneous disease with several recurrent cytogenetic abnormalities. Despite genomics and transcriptomics profiling efforts to understand AML's heterogeneity, studies focused on the proteomic profiles associated with pediatric AML cytogenetic features remain limited. Furthermore, the majority of biological functions within cells are operated by proteins (i.e., enzymes) and most drugs target the proteome rather than the genome or transcriptome, thus, highlighting the significance of studying proteomics. Here, we present our results from a pilot study investigating global proteomic profiles of leukemic cells obtained at diagnosis from 16 pediatric AML patients using a robust TMT-LC/LC-MS/MS platform. The proteome profiles were compared among patients with or without core binding factor (CBF) translocation indicated by a t(8;21) or inv(16) cytogenetic abnormality, minimal residual disease status at the end of the first cycle of chemotherapy (MRD1), and in vitro chemosensitivity of leukemic cells to cytarabine (Ara-C LC50). Our results established proteomic differences between CBF and non-CBF AML subtypes, providing insights to AML subtypes physiology, and identified potential druggable proteome targets such as THY1 (CD90), NEBL, CTSF, COL2A1, CAT, MGLL (MAGL), MACROH2A2, CLIP2 (isoform 1 and 2), ANPEP (CD13), MMP14, and AK5.
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Affiliation(s)
- Nam H. K. Nguyen
- Department of Pharmacotherapy and Translational Research, Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA;
| | - Huiyun Wu
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (H.W.); (S.P.)
| | - Haiyan Tan
- Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (H.T.); (J.P.)
| | - Junmin Peng
- Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (H.T.); (J.P.)
- Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Jeffrey E. Rubnitz
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| | - Xueyuan Cao
- College of Nursing, University of Tennessee Health Science Center, Memphis, TN 38163, USA;
| | - Stanley Pounds
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (H.W.); (S.P.)
| | - Jatinder K. Lamba
- Department of Pharmacotherapy and Translational Research, Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA;
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
- Correspondence: ; Tel.: +1-352-273-6425
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RSMOTE: improving classification performance over imbalanced medical datasets. Health Inf Sci Syst 2020; 8:22. [PMID: 32549976 DOI: 10.1007/s13755-020-00112-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 05/20/2020] [Indexed: 10/24/2022] Open
Abstract
Introduction Medical diagnosis is a crucial step for patient treatment. However, diagnosis is prone to bias due to imbalanced datasets. To overcome the imbalanced dataset problem, simple minority oversampling technique (SMOTE) was proposed that can generate new synthetic samples at data level to create the balance between minority and majority classes. However, the synthetic samples are generated on a random basis which causes class mixture problem; thus, resulting in deteriorating the classification performance and biased diagnosis. Purpose In order to overcome the SMOTE shortcomings, some modified methods were proposed that try to generate synthetic samples along the line segment of selected minority samples. Most of these methods adopt one of the two policies for selecting minority samples to generate synthetic samples: borderline region sampling or safe region sampling. However, they both suffer from over-generalisation problem. We propose a modified SMOTE-based resampling method called RSMOTE to alleviate the medical imbalanced dataset problem. We provide an in-depth analysis and verify the performance of RSMOTE over imbalanced medical datasets. Methods In this paper, the proposed RSMOTE divides the minority sample domain into four regions (normal, semi-normal, semi-critical, and critical) based on the minority sample density analysis. RSMOTE discovers the minority sample region globally and applies the resampling near a specific group of samples. Results Our analysis and experiments verify that if synthetic samples are generated in the regions with high minority sample density, classification performance will be improved due to low risk of class mixture. Unlike some safe region methods, RSMOTE decides the region of minority samples on a global basis, thus removing the over-generalisation problem. Classic and additional evaluation metrics are considered to measure the effectiveness of the modified method: Recall, FP Rate, Precision, F-Measure, ROC area, and Average Aggregated Metric. We carried out experiments over various imbalanced medical datasets. Conclusion Based on the minority sample density analysis, we propose RSMOTE method that divides the minority sample domain into four regions. The proposed RSMOTE includes four re-sampling methods that each of them carries out resampling on a specific region. According to the experimental results, resampling on the regions with high minority sample density obtained better results while those with lower minority sample density got the inferior results. Thus, we conclude that the RSMOTE is a more flexible resampling method for the imbalanced medical datasets that is capable of generating samples with various minority sample densities.
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