Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes
Myelodysplastic syndromes (MDS) are characterized by variable clinical manifestations and outcomes. Several prognostic systems relying on clinical factors and cytogenetic abnormalities have been developed to help stratify MDS patients into different risk categories of distinct prognoses and therapeu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/23/5/2802 |
| _version_ | 1797474828801802240 |
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| author | Hussein Awada Carmelo Gurnari Arda Durmaz Hassan Awada Simona Pagliuca Valeria Visconte |
| author_facet | Hussein Awada Carmelo Gurnari Arda Durmaz Hassan Awada Simona Pagliuca Valeria Visconte |
| author_sort | Hussein Awada |
| collection | DOAJ |
| description | Myelodysplastic syndromes (MDS) are characterized by variable clinical manifestations and outcomes. Several prognostic systems relying on clinical factors and cytogenetic abnormalities have been developed to help stratify MDS patients into different risk categories of distinct prognoses and therapeutic implications. The current abundance of molecular information poses the challenges of precisely defining patients’ molecular profiles and their incorporation in clinically established diagnostic and prognostic schemes. Perhaps the prognostic power of the current systems can be boosted by incorporating molecular features. Machine learning (ML) algorithms can be helpful in developing more precise prognostication models that integrate complex genomic interactions at a higher dimensional level. These techniques can potentially generate automated diagnostic and prognostic models and assist in advancing personalized therapies. This review highlights the current prognostication models used in MDS while shedding light on the latest achievements in ML-based research. |
| first_indexed | 2024-03-09T20:36:38Z |
| format | Article |
| id | doaj.art-21a3a9a96b774a8db0c669eb171fd567 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T20:36:38Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-21a3a9a96b774a8db0c669eb171fd5672023-11-23T23:09:51ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-03-01235280210.3390/ijms23052802Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic SyndromesHussein Awada0Carmelo Gurnari1Arda Durmaz2Hassan Awada3Simona Pagliuca4Valeria Visconte5Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USADepartment of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USADepartment of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USARoswell Park Comprehensive Cancer Center, Buffalo, NY 14203, USADepartment of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USADepartment of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USAMyelodysplastic syndromes (MDS) are characterized by variable clinical manifestations and outcomes. Several prognostic systems relying on clinical factors and cytogenetic abnormalities have been developed to help stratify MDS patients into different risk categories of distinct prognoses and therapeutic implications. The current abundance of molecular information poses the challenges of precisely defining patients’ molecular profiles and their incorporation in clinically established diagnostic and prognostic schemes. Perhaps the prognostic power of the current systems can be boosted by incorporating molecular features. Machine learning (ML) algorithms can be helpful in developing more precise prognostication models that integrate complex genomic interactions at a higher dimensional level. These techniques can potentially generate automated diagnostic and prognostic models and assist in advancing personalized therapies. This review highlights the current prognostication models used in MDS while shedding light on the latest achievements in ML-based research.https://www.mdpi.com/1422-0067/23/5/2802prognostic scoring systemsmutationsmyeloid neoplasia |
| spellingShingle | Hussein Awada Carmelo Gurnari Arda Durmaz Hassan Awada Simona Pagliuca Valeria Visconte Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes International Journal of Molecular Sciences prognostic scoring systems mutations myeloid neoplasia |
| title | Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes |
| title_full | Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes |
| title_fullStr | Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes |
| title_full_unstemmed | Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes |
| title_short | Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes |
| title_sort | personalized risk schemes and machine learning to empower genomic prognostication models in myelodysplastic syndromes |
| topic | prognostic scoring systems mutations myeloid neoplasia |
| url | https://www.mdpi.com/1422-0067/23/5/2802 |
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