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An ZY, Zhang XH. Embracing the age of artificial intelligence: paradigm shifts, opportunities, and challenges in the treatment of acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation. SCIENCE CHINA. LIFE SCIENCES 2024; 67:1302-1304. [PMID: 38811443 DOI: 10.1007/s11427-023-2511-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/14/2023] [Indexed: 05/31/2024]
Affiliation(s)
- Zhuo-Yu An
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, 100044, China
- Collaborative Innovation Center of Hematology, Peking University, Beijing, 100044, China
- Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, 100044, China
- National Clinical Research Center for Hematologic Disease, Beijing, 100044, China
| | - Xiao-Hui Zhang
- Peking University People's Hospital, Peking University Institute of Hematology, Beijing, 100044, China.
- Collaborative Innovation Center of Hematology, Peking University, Beijing, 100044, China.
- Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, 100044, China.
- National Clinical Research Center for Hematologic Disease, Beijing, 100044, China.
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Chandra G, Wang J, Siirtola P, Röning J. Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia. BMC Med Res Methodol 2024; 24:112. [PMID: 38734644 PMCID: PMC11088760 DOI: 10.1186/s12874-024-02237-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 05/02/2024] [Indexed: 05/13/2024] Open
Abstract
Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to limited data availability and complexities in effective care. This study delves into harnessing the potential of machine learning to enhance care strategies for orphan diseases, specifically focusing on allogeneic hematopoietic cell transplantation (allo-HCT) in T-cell prolymphocytic leukemia. The investigation evaluates how varying numbers of variables impact model performance, considering the rarity of the disease. Utilizing data from the Center for International Blood and Marrow Transplant Research, the study scrutinizes outcomes following allo-HCT for T-cell prolymphocytic leukemia. Diverse machine learning models were developed to forecast acute graft-versus-host disease (aGvHD) occurrence and its distinct grades post-allo-HCT. Assessment of model performance relied on balanced accuracy, F1 score, and ROC AUC metrics. The findings highlight the Linear Discriminant Analysis (LDA) classifier achieving the highest testing balanced accuracy of 0.58 in predicting aGvHD. However, challenges arose in its performance during multi-class classification tasks. While affirming the potential of machine learning in enhancing care for orphan diseases, the study underscores the impact of limited data and disease rarity on model performance.
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Affiliation(s)
- Gunjan Chandra
- Biomimetics and Intelligent Systems Group, University of Oulu, Pentti Kaiteran katu 1, 90570, Oulu, Finland.
| | - Junfeng Wang
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
| | - Pekka Siirtola
- Biomimetics and Intelligent Systems Group, University of Oulu, Pentti Kaiteran katu 1, 90570, Oulu, Finland
| | - Juha Röning
- Biomimetics and Intelligent Systems Group, University of Oulu, Pentti Kaiteran katu 1, 90570, Oulu, Finland
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Zhou Y, Smith J, Keerthi D, Li C, Sun Y, Mothi SS, Shyr DC, Spitzer B, Harris A, Chatterjee A, Chatterjee S, Shouval R, Naik S, Bertaina A, Boelens JJ, Triplett BM, Tang L, Sharma A. Longitudinal clinical data improve survival prediction after hematopoietic cell transplantation using machine learning. Blood Adv 2024; 8:686-698. [PMID: 37991991 PMCID: PMC10844815 DOI: 10.1182/bloodadvances.2023011752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/20/2023] [Accepted: 10/31/2023] [Indexed: 11/24/2023] Open
Abstract
ABSTRACT Serial prognostic evaluation after allogeneic hematopoietic cell transplantation (allo-HCT) might help identify patients at high risk of lethal organ dysfunction. Current prediction algorithms based on models that do not incorporate changes to patients' clinical condition after allo-HCT have limited predictive ability. We developed and validated a robust risk-prediction algorithm to predict short- and long-term survival after allo-HCT in pediatric patients that includes baseline biological variables and changes in the patients' clinical status after allo-HCT. The model was developed using clinical data from children and young adults treated at a single academic quaternary-care referral center. The model was created using a randomly split training data set (70% of the cohort), internally validated (remaining 30% of the cohort) and then externally validated on patient data from another tertiary-care referral center. Repeated clinical measurements performed from 30 days before allo-HCT to 30 days afterwards were extracted from the electronic medical record and incorporated into the model to predict survival at 100 days, 1 year, and 2 years after allo-HCT. Naïve-Bayes machine learning models incorporating longitudinal data were significantly better than models constructed from baseline variables alone at predicting whether patients would be alive or deceased at the given time points. This proof-of-concept study demonstrates that unlike traditional prognostic tools that use fixed variables for risk assessment, incorporating dynamic variability using clinical and laboratory data improves the prediction of mortality in patients undergoing allo-HCT.
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Affiliation(s)
- Yiwang Zhou
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - Jesse Smith
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - Dinesh Keerthi
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children’s Research Hospital, Memphis, TN
| | - Cai Li
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - Yilun Sun
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - Suraj Sarvode Mothi
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - David C. Shyr
- Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA
| | - Barbara Spitzer
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Andrew Harris
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Avijit Chatterjee
- Digital, Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Subrata Chatterjee
- Digital, Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Roni Shouval
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
- Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Swati Naik
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children’s Research Hospital, Memphis, TN
| | - Alice Bertaina
- Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA
| | - Jaap Jan Boelens
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Brandon M. Triplett
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children’s Research Hospital, Memphis, TN
| | - Li Tang
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN
| | - Akshay Sharma
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children’s Research Hospital, Memphis, TN
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Yuan J, Zhang Y, Wang X. Application of machine learning in the management of lymphoma: Current practice and future prospects. Digit Health 2024; 10:20552076241247963. [PMID: 38628632 PMCID: PMC11020711 DOI: 10.1177/20552076241247963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/28/2024] [Indexed: 04/19/2024] Open
Abstract
In the past decade, digitization of medical records and multiomics data analysis in lymphoma has led to the accessibility of high-dimensional records. The digitization of medical records, the visualization of extensive volume data extracted from medical images, and the integration of multiomics methods into clinical decision-making have produced many datasets. As a promising auxiliary tool, machine learning (ML) intends to extract homologous features in large-scale data sets and encode them into various patterns to complete complicated tasks. At present, artificial intelligence and digital mining have shown promising prospects in the field of lymphoma pathological image analysis. The paradigm shift from qualitative analysis to quantitative analysis makes the pathological diagnosis more intelligent and the results more accurate and objective. ML can promote accurate lymphoma diagnosis and provide patients with prognostic information and more individualized treatment options. Based on the above, this comprehensive review of the general workflow of ML highlights recent advances in ML techniques in the diagnosis, treatment, and prognosis of lymphoma, and clarifies the boundedness and future orientation of the ML technique in the clinical practice of lymphoma.
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Affiliation(s)
- Junyun Yuan
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Ya Zhang
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong, China
| | - Xin Wang
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong, China
- Branch of National Clinical Research Center for Hematologic Diseases, Jinan, Shandong, China
- National Clinical Research Center for Hematologic Diseases, Hospital of Soochow University, Suzhou, China
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Och K, Turki AT, Götz KM, Selzer D, Brossette C, Theobald S, Braun Y, Graf N, Rauch J, Rohm K, Weiler G, Kiefer S, Schwarz U, Eisenberg L, Pfeifer N, Ihle M, Grandjean A, Fix S, Riede C, Rissland J, Smola S, Beelen DW, Kaddu‐Mulindwa D, Bittenbring J, Lehr T. A dynamic time-to-event model for prediction of acute graft-versus-host disease in patients after allogeneic hematopoietic stem cell transplantation. Cancer Med 2023; 13:e6833. [PMID: 38132807 PMCID: PMC10807572 DOI: 10.1002/cam4.6833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/26/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Acute graft-versus-host disease (aGvHD) is a major cause of death for patients following allogeneic hematopoietic stem cell transplantation (HSCT). Effective management of moderate to severe aGvHD remains challenging despite recent advances in HSCT, emphasizing the importance of prophylaxis and risk factor identification. METHODS In this study, we analyzed data from 1479 adults who underwent HSCT between 2005 and 2017 to investigate the effects of aGvHD prophylaxis and time-dependent risk factors on the development of grades II-IV aGvHD within 100 days post-HSCT. RESULTS Using a dynamic longitudinal time-to-event model, we observed a non-monotonic baseline hazard overtime with a low hazard during the first few days and a maximum hazard at day 17, described by Bateman function with a mean transit time of approximately 11 days. Multivariable analysis revealed significant time-dependent effects of white blood cell counts and cyclosporine A exposure as well as static effects of female donors for male recipients, patients with matched related donors, conditioning regimen consisting of fludarabine plus total body irradiation, and patient age in recipients of grafts from related donors on the risk to develop grades II-IV aGvHD. Additionally, we found that higher cumulative hazard on day 7 after allo-HSCT are associated with an increased incidence of grades II-IV aGvHD within 100 days indicating that an individual assessment of the cumulative hazard on day 7 could potentially serve as valuable predictor for later grades II-IV aGvHD development. Using the final model, stochastic simulations were performed to explore covariate effects on the cumulative incidence over time and to estimate risk ratios. CONCLUSION Overall, the presented model showed good descriptive and predictive performance and provides valuable insights into the interplay of multiple static and time-dependent risk factors for the prediction of aGvHD.
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Affiliation(s)
- Katharina Och
- Department of Clinical PharmacySaarland UniversitySaarbrückenGermany
| | - Amin T. Turki
- Department of Hematology and Stem Cell Transplantation, West‐German Cancer CenterUniversity Hospital EssenEssenGermany
| | - Katharina M. Götz
- Department of Clinical PharmacySaarland UniversitySaarbrückenGermany
| | - Dominik Selzer
- Department of Clinical PharmacySaarland UniversitySaarbrückenGermany
| | - Christian Brossette
- Department of Pediatric Oncology and HematologySaarland UniversityHomburgGermany
| | - Stefan Theobald
- Department of Pediatric Oncology and HematologySaarland UniversityHomburgGermany
| | - Yvonne Braun
- Department of Pediatric Oncology and HematologySaarland UniversityHomburgGermany
| | - Norbert Graf
- Department of Pediatric Oncology and HematologySaarland UniversityHomburgGermany
| | - Jochen Rauch
- Department of Biomedical Data & BioethicsFraunhofer Institute for Biomedical Engineering (IBMT)SulzbachGermany
| | - Kerstin Rohm
- Department of Biomedical Data & BioethicsFraunhofer Institute for Biomedical Engineering (IBMT)SulzbachGermany
| | - Gabriele Weiler
- Department of Biomedical Data & BioethicsFraunhofer Institute for Biomedical Engineering (IBMT)SulzbachGermany
| | - Stephan Kiefer
- Department of Biomedical Data & BioethicsFraunhofer Institute for Biomedical Engineering (IBMT)SulzbachGermany
| | - Ulf Schwarz
- Institute for Formal Ontology and Medical Information ScienceSaarland UniversitySaarbrückenGermany
| | - Lisa Eisenberg
- Department of Computer ScienceUniversity of TübingenTübingenGermany
| | - Nico Pfeifer
- Department of Computer ScienceUniversity of TübingenTübingenGermany
| | | | | | | | | | - Jürgen Rissland
- Institute of VirologySaarland University Medical CentreHomburgGermany
| | - Sigrun Smola
- Institute of VirologySaarland University Medical CentreHomburgGermany
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI)Saarland University CampusSaarbrückenGermany
| | - Dietrich W. Beelen
- Department of Hematology and Stem Cell Transplantation, West‐German Cancer CenterUniversity Hospital EssenEssenGermany
| | | | - Jörg Bittenbring
- Department of Internal Medicine 1University Hospital of the SaarlandHomburgGermany
| | - Thorsten Lehr
- Department of Clinical PharmacySaarland UniversitySaarbrückenGermany
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Bayraktar E, Graf T, Ayuk FA, Beutel G, Penack O, Luft T, Brueder N, Castellani G, Reinhardt HC, Kröger N, Beelen DW, Turki AT. Data-driven grading of acute graft-versus-host disease. Nat Commun 2023; 14:7799. [PMID: 38017035 PMCID: PMC10684603 DOI: 10.1038/s41467-023-43372-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 11/08/2023] [Indexed: 11/30/2023] Open
Abstract
Despite advances in allogeneic hematopoietic cell transplantation, acute graft-versus-host disease (aGVHD) remains its leading complication, yet with heterogeneous outcomes. Here, we analyzed aGVHD phenotypes and clinical classifications in depth in large, multicenter cohorts involving 3019 patients and addressed prevailing gaps by developing data-driven models. We compared, tested and verified these along with all conventional classifications in independent cohorts and found that data-driven grading outperformed conventional grading in Akaike information criterion and concordance index metrics. Data-driven classifications refined aGVHD assessment with up to 12 severity grades, which were associated with distinct nonrelapse mortality (NRM) and confirmed the key role of intestinal aGVHD. We developed an online calculator for physicians to implement principal component-derived grading (PC1). These results provide substantial insight into the evaluation of aGVHD phenotypes and multiorgan involvement, which relegates the exclusive reporting of overall aGVHD severity grades in transplant registries and clinical trials. Data-driven aGVHD grading provides an expandable platform to refine classification and transplant risk assessment.
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Affiliation(s)
- Evren Bayraktar
- Computational Hematology Lab, West-German Cancer Center, University Hospital Essen, Hufelandstr. 55, 45122, Essen, Germany
- Department of Hematology and Stem Cell Transplantation, West-German Cancer Center, University Hospital Essen, Hufelandstr. 55, 45122, Essen, Germany
- Chair III of Applied Mathematics, TU Dortmund University of Applied Sciences, Vogelpothsweg 87, 44227, Dortmund, Germany
| | - Theresa Graf
- Computational Hematology Lab, West-German Cancer Center, University Hospital Essen, Hufelandstr. 55, 45122, Essen, Germany
- Department of Hematology and Stem Cell Transplantation, West-German Cancer Center, University Hospital Essen, Hufelandstr. 55, 45122, Essen, Germany
| | - Francis A Ayuk
- Department for Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, 20251, Hamburg, Germany
| | - Gernot Beutel
- Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Olaf Penack
- Department of Hematology, Oncology and Tumorimmunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Thomas Luft
- Department of Internal Medicine V, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany
| | - Nicole Brueder
- Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Gastone Castellani
- Department of Medical and Surgical Sciences- DIMEC, Applied Physics and Biophysics group, University of Bologna, Via Zamboni 33, 40126, Bologna, Italy
| | - H Christian Reinhardt
- Department of Hematology and Stem Cell Transplantation, West-German Cancer Center, University Hospital Essen, Hufelandstr. 55, 45122, Essen, Germany
- German Cancer Consortium (DKTK), Partner sites Essen/Düsseldorf, Hufelandstr. 55, 45122, Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), Partner site Essen, Hufelandstr. 55, 45122, Essen, Germany
| | - Nicolaus Kröger
- Department for Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, 20251, Hamburg, Germany
| | - Dietrich W Beelen
- Department of Hematology and Stem Cell Transplantation, West-German Cancer Center, University Hospital Essen, Hufelandstr. 55, 45122, Essen, Germany
- German Cancer Consortium (DKTK), Partner sites Essen/Düsseldorf, Hufelandstr. 55, 45122, Essen, Germany
| | - Amin T Turki
- Computational Hematology Lab, West-German Cancer Center, University Hospital Essen, Hufelandstr. 55, 45122, Essen, Germany.
- Department of Hematology and Stem Cell Transplantation, West-German Cancer Center, University Hospital Essen, Hufelandstr. 55, 45122, Essen, Germany.
- German Cancer Consortium (DKTK), Partner sites Essen/Düsseldorf, Hufelandstr. 55, 45122, Essen, Germany.
- Cancer Research Center Cologne Essen (CCCE), Partner site Essen, Hufelandstr. 55, 45122, Essen, Germany.
- Department of Hematology and Oncology, Marienhospital University Hospital, Ruhr-University Bochum, Universitätsstr. 150, 44801, Bochum, Germany.
- Institute for Experimental Cellular Therapy, University Hospital Essen, Hufelandstr. 55, 45122, Essen, Germany.
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Mushtaq AH, Shafqat A, Salah HT, Hashmi SK, Muhsen IN. Machine learning applications and challenges in graft-versus-host disease: a scoping review. Curr Opin Oncol 2023; 35:594-600. [PMID: 37820094 DOI: 10.1097/cco.0000000000000996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
PURPOSE OF REVIEW This review delves into the potential of artificial intelligence (AI), particularly machine learning (ML), in enhancing graft-versus-host disease (GVHD) risk assessment, diagnosis, and personalized treatment. RECENT FINDINGS Recent studies have demonstrated the superiority of ML algorithms over traditional multivariate statistical models in donor selection for allogeneic hematopoietic stem cell transplantation. ML has recently enabled dynamic risk assessment by modeling time-series data, an upgrade from the static, "snapshot" assessment of patients that conventional statistical models and older ML algorithms offer. Regarding diagnosis, a deep learning model, a subset of ML, can accurately identify skin segments affected with chronic GVHD with satisfactory results. ML methods such as Q-learning and deep reinforcement learning have been utilized to develop adaptive treatment strategies (ATS) for the personalized prevention and treatment of acute and chronic GVHD. SUMMARY To capitalize on these promising advancements, there is a need for large-scale, multicenter collaborations to develop generalizable ML models. Furthermore, addressing pertinent issues such as the implementation of stringent ethical guidelines is crucial before the widespread introduction of AI into GVHD care.
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Affiliation(s)
- Ali Hassan Mushtaq
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Areez Shafqat
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Haneen T Salah
- Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, Texas
| | - Shahrukh K Hashmi
- Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Department of Medicine, Sheikh Shakbout Medical City
- Medical Affairs, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Ibrahim N Muhsen
- Section of Hematology and Oncology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
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Xue L, Song L, Yu X, Yang X, Xia F, Ding X, Huang C, Wu D, Miao L. Assessment of risk factors for acute graft- versus-host disease post-hematopoietic stem cell transplantation: a retrospective study based on a proportional odds model using a nonlinear mixed-effects model. Ther Adv Hematol 2023; 14:20406207231205406. [PMID: 37872970 PMCID: PMC10590544 DOI: 10.1177/20406207231205406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/12/2023] [Indexed: 10/25/2023] Open
Abstract
Background Acute graft-versus-host disease (aGVHD) is a major complication following hematopoietic stem cell transplantation (HSCT). Objective This study aimed to explore the risk factors for the incidence of aGVHD in patients post-HSCT. Design This was a retrospective study. Methods A total of 407 patients were enrolled. The patients' data were recorded from the medical records. The exposure of cyclosporine was estimated based on a population pharmacokinetics model. The occurrence of aGVHD was clinically graded and staged in severity from grades I to IV. A proportional odds model that estimated the cumulative probabilities of aGVHD was used to analyze the data using a nonlinear mixed-effects model. Then, the model parameters and plausibility were evaluated by bootstrap and visual predictive checks. Results The typical probabilities were 18.9% and 17.9% for grade II and grades III-IV, respectively. The incidence of grade II and grade III-IV aGVHD for human leukocyte antigen (HLA) haplo sibling donor patients was higher than that for HLA-matched donor patients. The incidence of grade II and grade III-IV aGVHD decreased with increasing early cyclosporine trough concentration; however, cyclosporine exposure was not associated with the incidence of aGVHD. Conclusion HLA matching and early cyclosporine trough concentration were important factors for the occurrence of aGVHD.
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Affiliation(s)
- Ling Xue
- Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China
- Department of Pharmacology, Faculty of Medicine, University of the Basque Country – (UPV/EHU), Leioa, Spain
| | - Lin Song
- Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Xun Yu
- Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiao Yang
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Fan Xia
- Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaoliang Ding
- Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chenrong Huang
- Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Depei Wu
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, No. 188, Shizi Street, Suzhou 215006, China
| | - Liyan Miao
- Department of Pharmacy, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou 215006, China
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Shourabizadeh H, Aleman DM, Rousseau LM, Law AD, Viswabandya A, Michelis FV. Machine Learning for the Prediction of Survival Post-Allogeneic Hematopoietic Cell Transplantation: A Single-Center Experience. Acta Haematol 2023; 147:280-291. [PMID: 37769635 DOI: 10.1159/000533665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 08/14/2023] [Indexed: 10/03/2023]
Abstract
INTRODUCTION Prediction of outcomes following allogeneic hematopoietic cell transplantation (HCT) remains a major challenge. Machine learning (ML) is a computational procedure that may facilitate the generation of HCT prediction models. We sought to investigate the prognostic potential of multiple ML algorithms when applied to a large single-center allogeneic HCT database. METHODS Our registry included 2,697 patients that underwent allogeneic HCT from January 1976 to December 2017. 45 pretransplant baseline variables were included in the predictive assessment of each ML algorithm on overall survival (OS) as determined by area under the curve (AUC). Pretransplant variables used in the EBMT ML study (Shouval et al., 2015) were used as a benchmark for comparison. RESULTS On the entire dataset, the random forest (RF) algorithm performed best (AUC 0.71 ± 0.04) compared to the second-best model, logistic regression (LR) (AUC = 0.69 ± 0.04) (p < 0.001). Both algorithms demonstrated improved AUC scores using all 45 variables compared to the limited variables examined by the EBMT study. Survival at 100 days post-HCT using RF on the full dataset discriminated patients into different prognostic groups with different 2-year OS (p < 0.0001). We then examined the ML methods that allow for significant individual variable identification, including LR and RF, and identified matched related donors (HR = 0.49, p < 0.0001), increasing TBI dose (HR = 1.60, p = 0.006), increasing recipient age (HR = 1.92, p < 0.0001), higher baseline Hb (HR = 0.59, p = 0.0002), and increased baseline FEV1 (HR = 0.73, p = 0.02), among others. CONCLUSION The application of multiple ML techniques on single-center allogeneic HCT databases warrants further investigation and may provide a useful tool to identify variables with prognostic potential.
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Affiliation(s)
- Hamed Shourabizadeh
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Dionne M Aleman
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Louis-Martin Rousseau
- Department of Mathematical and Industrial Engineering, Polytechnique Montreal, Montreal, Québec, Canada
| | - Arjun D Law
- Hans Messner Allogeneic Transplant Program, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Auro Viswabandya
- Hans Messner Allogeneic Transplant Program, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Fotios V Michelis
- Hans Messner Allogeneic Transplant Program, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
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10
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Jo T, Arai Y, Kanda J, Kondo T, Ikegame K, Uchida N, Doki N, Fukuda T, Ozawa Y, Tanaka M, Ara T, Kuriyama T, Katayama Y, Kawakita T, Kanda Y, Onizuka M, Ichinohe T, Atsuta Y, Terakura S. A convolutional neural network-based model that predicts acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation. COMMUNICATIONS MEDICINE 2023; 3:67. [PMID: 37193882 DOI: 10.1038/s43856-023-00299-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 05/02/2023] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND Forecasting acute graft-versus-host disease (aGVHD) after allogeneic hematopoietic stem cell transplantation (HSCT) is highly challenging with conventional statistical techniques due to complex parameters and their interactions. The primary object of this study was to establish a convolutional neural network (CNN)-based prediction model for aGVHD. METHOD We analyzed adult patients who underwent allogeneic HSCT between 2008 and 2018, using the Japanese nationwide registry database. The CNN algorithm, equipped with a natural language processing technique and an interpretable explanation algorithm, was applied to develop and validate prediction models. RESULTS Here, we evaluate 18,763 patients between 16 and 80 years of age (median, 50 years). In total, grade II-IV and grade III-IV aGVHD is observed among 42.0% and 15.6%. The CNN-based model eventually allows us to calculate a prediction score of aGVHD for an individual case, which is validated to distinguish the high-risk group of aGVHD in the test cohort: cumulative incidence of grade III-IV aGVHD at Day 100 after HSCT is 28.8% for patients assigned to a high-risk group by the CNN model, compared to 8.4% among low-risk patients (hazard ratio, 4.02; 95% confidence interval, 2.70-5.97; p < 0.01), suggesting high generalizability. Furthermore, our CNN-based model succeeds in visualizing the learning process. Moreover, contributions of pre-transplant parameters other than HLA information to the risk of aGVHD are determined. CONCLUSIONS Our results suggest that CNN-based prediction provides a faithful prediction model for aGVHD, and can serve as a valuable tool for decision-making in clinical practice.
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Affiliation(s)
- Tomoyasu Jo
- Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Center for Research and Application of Cellular Therapy, Kyoto University Hospital, Kyoto, Japan
| | - Yasuyuki Arai
- Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
- Center for Research and Application of Cellular Therapy, Kyoto University Hospital, Kyoto, Japan.
| | - Junya Kanda
- Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tadakazu Kondo
- Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kazuhiro Ikegame
- Department of Hematology, Hyogo Medical University Hospital, Hyogo, Japan
| | - Naoyuki Uchida
- Department of Hematology, Federation of National Public Service Personnel Mutual Aid Associations Toranomon Hospital, Tokyo, Japan
| | - Noriko Doki
- Hematology Division, Tokyo Metropolitan Cancer and Infectious Diseases Center, Komagome Hospital, Tokyo, Japan
| | - Takahiro Fukuda
- Department of Hematopoietic Stem Cell Transplantation, National Cancer Center Hospital, Tokyo, Japan
| | - Yukiyasu Ozawa
- Department of Hematology, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, Nagoya, Japan
| | - Masatsugu Tanaka
- Department of Hematology, Kanagawa Cancer Center, Yokohama, Japan
| | - Takahide Ara
- Department of Hematology, Hokkaido University Hospital, Sapporo, Japan
| | - Takuro Kuriyama
- Department of Hematology, Hamanomachi Hospital, Fukuoka, Japan
| | - Yuta Katayama
- Department of Hematology, Hiroshima Red Cross Hospital & Atomic-bomb Survivors Hospital, Hiroshima, Japan
| | - Toshiro Kawakita
- Department of Hematology, National Hospital Organization Kumamoto Medical Center, Kumamoto, Japan
| | - Yoshinobu Kanda
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Makoto Onizuka
- Department of Hematology/Oncology, Tokai University School of Medicine, Isehara, Japan
| | - Tatsuo Ichinohe
- Department of Hematology and Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan
| | - Yoshiko Atsuta
- Japanese Data Center for Hematopoietic Cell Transplantation, Nagoya, Japan
- Department of Registry Science for Transplant and Cellular Therapy, Aichi Medical University School of Medicine, Nagakute, Japan
| | - Seitaro Terakura
- Department of Hematology and Oncology, Nagoya University Graduate School of Medicine, Nagoya, Japan
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11
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Wang P, Liu C, Wei Z, Jiang W, Sun H, Wang Y, Hou J, Sun J, Huang Y, Wang H, Wang Y, He X, Wang X, Qian X, Zhai X. Nomogram for Predicting Early Mortality after Umbilical Cord Blood Transplantation in Children with Inborn Errors of Immunity. J Clin Immunol 2023:10.1007/s10875-023-01505-8. [PMID: 37155023 DOI: 10.1007/s10875-023-01505-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 04/27/2023] [Indexed: 05/10/2023]
Abstract
PURPOSE Pediatric patients with inborn errors of immunity (IEI) undergoing umbilical cord blood transplantation (UCBT) are at risk of early mortality. Our aim was to develop and validate a prediction model for early mortality after UCBT in pediatric IEI patients based on pretransplant factors. METHODS Data from 230 pediatric IEI patients who received their first UCBT between 2014 and 2021 at a single center were analyzed retrospectively. Data from 2014-2019 and 2020-2021 were used as training and validation sets, respectively. The primary outcome of interest was early mortality. Machine learning algorithms were used to identify risk factors associated with early mortality and to build predictive models. The model with the best performance was visualized using a nomogram. Discriminative ability was measured using the area under the curve (AUC) and decision curve analysis. RESULTS Fifty days was determined as the cutoff for distinguishing early mortality in pediatric IEI patients undergoing UCBT. Of the 230 patients, 43 (18.7%) suffered early mortality. Multivariate logistic regression with pretransplant albumin, CD4 (absolute count), elevated C-reactive protein, and medical history of sepsis showed good discriminant AUC values of 0.7385 (95% CI, 0.5824-0.8945) and 0.827 (95% CI, 0.7409-0.9132) in predicting early mortality in the validation and training sets, respectively. The sensitivity and specificity were 0.5385 and 0.8154 for validation and 0.7667 and 0.7705 for training, respectively. The final model yielded net benefits across a reasonable range of risk thresholds. CONCLUSION The developed nomogram can predict early mortality in pediatric IEI patients undergoing UCBT.
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Affiliation(s)
- Ping Wang
- Department of Hematology/Oncology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Chao Liu
- Yidu Cloud Technology Inc, Beijing, 100083, China
- Nanjing YiGenCloud Institute, Nanjing, 211899, China
| | - Zhongling Wei
- Department of Hematology/Oncology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Wenjin Jiang
- Department of Hematology/Oncology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Hua Sun
- Department of Gastroenterology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Yuhuan Wang
- Department of Gastroenterology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Jia Hou
- Department of Clinical Immunology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Jinqiao Sun
- Department of Clinical Immunology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Ying Huang
- Department of Gastroenterology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Hongsheng Wang
- Department of Hematology/Oncology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Yao Wang
- Yidu Cloud Technology Inc, Beijing, 100083, China
| | - Xinjun He
- Yidu Cloud Technology Inc, Beijing, 100083, China
- Nanjing YiGenCloud Institute, Nanjing, 211899, China
| | - Xiaochuan Wang
- Department of Clinical Immunology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Xiaowen Qian
- Department of Hematology/Oncology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Xiaowen Zhai
- Department of Hematology/Oncology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China.
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12
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Song L, Huang CR, Pan SZ, Zhu JG, Cheng ZQ, Yu X, Xue L, Xia F, Zhang JY, Wu DP, Miao LY. A model based on machine learning for the prediction of cyclosporin A trough concentration in Chinese allo-HSCT patients. Expert Rev Clin Pharmacol 2023; 16:83-91. [PMID: 36373407 DOI: 10.1080/17512433.2023.2142561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Cyclosporin A is a calcineurin inhibitor which has a narrow therapeutic window and high interindividual variability. Various population pharmacokinetic models have been reported; however, professional software and technical personnel were needed and the variables of the models were limited. Therefore, the aim of this study was to establish a model based on machine learning to predict CsA trough concentrations in Chinese allo-HSCT patients. METHODS A total of 7874 cases of CsA therapeutic drug monitoring data from 2069 allo-HSCT patients were retrospectively included. Sequential forward selection was used to select variable subsets, and eight different algorithms were applied to establish the prediction model. RESULTS XGBoost exhibited the highest prediction ability. Except for the variables that were identified by previous studies, some rarely reported variables were found, such as norethindrone, WBC, PAB, and hCRP. The prediction accuracy within ±30% of the actual trough concentration was above 0.80, and the predictive ability of the models was demonstrated to be effective in external validation. CONCLUSION In this study, models based on machine learning technology were established to predict CsA levels 3-4 days in advance during the early inpatient phase after HSCT. A new perspective for CsA clinical application is provided.
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Affiliation(s)
- Lin Song
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China.,College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Chen-Rong Huang
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shi-Zheng Pan
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China.,College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Jian-Guo Zhu
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zong-Qi Cheng
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xun Yu
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Ling Xue
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Fan Xia
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
| | | | - De-Pei Wu
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Li-Yan Miao
- Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China.,College of Pharmaceutical Sciences, Soochow University, Suzhou, China.,National Clinical Research Center for Hematologic Diseases, The First Affiliated Hospital of Soochow University, Suzhou, China
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13
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Eisenberg L, Brossette C, Rauch J, Grandjean A, Ottinger H, Rissland J, Schwarz U, Graf N, Beelen DW, Kiefer S, Pfeifer N, Turki AT, Bittenbring J, Kaddu‐Mulindwa D, Götz K, Och K, Lehr T, Brossette C, Theobald S, Braun Y, Graf N, Kadir A, Schwarz U, Grandjean A, Ihle M, Riede C, Fix S, Turki AT, Beelen DW, Ottinger H, Tsachakis‐Mück N, Bogdanov R, Koldehoff M, Steckel N, Yi J, Fokaite A, Klisanin V, Kordelas L, Garay D, Gavilanes X, Lams RF, Pillibeit A, Leserer S, Graf T, Hilbig S, Weiß J, Brossette C, Rauch J, Grandjean A, Ottinger H, Rissland J, Schwarz U, Graf N, Beelen DW, Kiefer S, Pfeifer N, Turki AT. Time-dependent prediction of mortality and cytomegalovirus reactivation after allogeneic hematopoietic cell transplantation using machine learning. Am J Hematol 2022; 97:1309-1323. [PMID: 36071578 DOI: 10.1002/ajh.26671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/21/2022] [Accepted: 07/22/2022] [Indexed: 01/24/2023]
Abstract
Allogeneic hematopoietic cell transplantation (HCT) effectively treats high-risk hematologic diseases but can entail HCT-specific complications, which may be minimized by appropriate patient management, supported by accurate, individual risk estimation. However, almost all HCT risk scores are limited to a single risk assessment before HCT without incorporation of additional data. We developed machine learning models that integrate both baseline patient data and time-dependent laboratory measurements to individually predict mortality and cytomegalovirus (CMV) reactivation after HCT at multiple time points per patient. These gradient boosting machine models provide well-calibrated, time-dependent risk predictions and achieved areas under the receiver-operating characteristic of 0.92 and 0.83 and areas under the precision-recall curve of 0.58 and 0.62 for prediction of mortality and CMV reactivation, respectively, in a 21-day time window. Both models were successfully validated in a prospective, non-interventional study and performed on par with expert hematologists in a pilot comparison.
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Affiliation(s)
- Lisa Eisenberg
- Department of Computer Science, University of Tübingen, Tübingen, Germany.,Institute of Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
| | | | - Christian Brossette
- Department of Pediatric Oncology and Hematology, Saarland University, Homburg, Germany
| | - Jochen Rauch
- Department of Biomedical Data & Bioethics, Fraunhofer Institute for Biomedical Engineering (IBMT), Sulzbach, Germany
| | | | - Hellmut Ottinger
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, Essen, Germany
| | - Jürgen Rissland
- Institute of Virology, Saarland University Medical Center, Homburg, Germany
| | - Ulf Schwarz
- Institute for Formal Ontology and Medical Information Science (IFOMIS), Saarland University, Saarbrücken, Germany
| | - Norbert Graf
- Department of Pediatric Oncology and Hematology, Saarland University, Homburg, Germany
| | - Dietrich W Beelen
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, Essen, Germany
| | - Stephan Kiefer
- Department of Biomedical Data & Bioethics, Fraunhofer Institute for Biomedical Engineering (IBMT), Sulzbach, Germany
| | - Nico Pfeifer
- Department of Computer Science, University of Tübingen, Tübingen, Germany.,Institute of Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
| | - Amin T Turki
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, Essen, Germany
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Christian Brossette
- Department of Pediatric Oncology and Hematology Saarland University Homburg Germany
| | - Jochen Rauch
- Fraunhofer Institute for Biomedical Engineering (IBMT) Sulzbach Germany
| | | | - Hellmut Ottinger
- Department of Hematology and Stem Cell Transplantation University Hospital Essen Essen Germany
| | - Jürgen Rissland
- Institute of Virology Saarland University Medical Center Homburg Germany
| | - Ulf Schwarz
- Institute for Formal Ontology and Medical Information Science (IFOMIS) Saarland University Saarbrücken Germany
| | - Norbert Graf
- Department of Pediatric Oncology and Hematology Saarland University Homburg Germany
| | - Dietrich W. Beelen
- Department of Hematology and Stem Cell Transplantation University Hospital Essen Essen Germany
| | - Stephan Kiefer
- Fraunhofer Institute for Biomedical Engineering (IBMT) Sulzbach Germany
| | - Nico Pfeifer
- Department of Computer Science University of Tübingen Tübingen Germany
- Institute of Bioinformatics and Medical Informatics (IBMI) University of Tübingen Tübingen Germany
| | - Amin T. Turki
- Department of Hematology and Stem Cell Transplantation University Hospital Essen Essen Germany
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14
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Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9391136. [PMID: 36199778 PMCID: PMC9527434 DOI: 10.1155/2022/9391136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/31/2022] [Accepted: 09/05/2022] [Indexed: 11/17/2022]
Abstract
Bone marrow transplant (BMT) is an effective surgical treatment for bone marrow-related disorders. However, several associated risk factors can impair long-term survival after BMT. Machine learning (ML) technologies have been proven useful in survival prediction of BMT receivers along with the influences that limit their resilience. In this study, an efficient classification model predicting the survival of children undergoing BMT is presented using a public dataset. Several supervised ML methods were investigated in this regard with an 80-20 train-test split ratio. To ensure prediction with minimal time and resources, only the top 11 out of the 59 dataset features were considered using Chi-square feature selection method. Furthermore, hyperparameter optimization (HPO) using the grid search cross-validation (GSCV) technique was adopted to increase the accuracy of prediction. Four experiments were conducted utilizing a combination of default and optimized hyperparameters on the original and reduced datasets. Our investigation revealed that the top 11 features of HPO had the same prediction accuracy (94.73%) as the entire dataset with default parameters, however, requiring minimal time and resources. Hence, the proposed approach may aid in the development of a computer-aided diagnostic system with satisfactory accuracy and minimal computation time by utilizing medical data records.
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15
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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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16
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Choi EJ, Jun TJ, Park HS, Lee JH, Lee KH, Kim YH, Lee YS, Kang YA, Jeon M, Kang H, Woo J, Lee JH. Predicting Long-term Survival After Allogeneic Hematopoietic Cell Transplantation in Patients With Hematologic Malignancies: Machine Learning-Based Model Development and Validation. JMIR Med Inform 2022; 10:e32313. [PMID: 35254275 PMCID: PMC8938832 DOI: 10.2196/32313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/10/2021] [Accepted: 12/29/2021] [Indexed: 11/13/2022] Open
Abstract
Background Scoring systems developed for predicting survival after allogeneic hematopoietic cell transplantation (HCT) show suboptimal prediction power, and various factors affect posttransplantation outcomes. Objective A prediction model using a machine learning–based algorithm can be an alternative for concurrently applying multiple variables and can reduce potential biases. In this regard, the aim of this study is to establish and validate a machine learning–based predictive model for survival after allogeneic HCT in patients with hematologic malignancies. Methods Data from 1470 patients with hematologic malignancies who underwent allogeneic HCT between December 1993 and June 2020 at Asan Medical Center, Seoul, South Korea, were retrospectively analyzed. Using the gradient boosting machine algorithm, we evaluated a model predicting the 5-year posttransplantation survival through 10-fold cross-validation. Results The prediction model showed good performance with a mean area under the receiver operating characteristic curve of 0.788 (SD 0.03). Furthermore, we developed a risk score predicting probabilities of posttransplantation survival in 294 randomly selected patients, and an agreement between the estimated predicted and observed risks of overall death, nonrelapse mortality, and relapse incidence was observed according to the risk score. Additionally, the calculated score demonstrated the possibility of predicting survival according to the different transplantation-related factors, with the visualization of the importance of each variable. Conclusions We developed a machine learning–based model for predicting long-term survival after allogeneic HCT in patients with hematologic malignancies. Our model provides a method for making decisions regarding patient and donor candidates or selecting transplantation-related resources, such as conditioning regimens.
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Affiliation(s)
- Eun-Ji Choi
- Department of Hematology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Tae Joon Jun
- Big Data Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
| | - Han-Seung Park
- Department of Hematology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jung-Hee Lee
- Department of Hematology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Kyoo-Hyung Lee
- Department of Hematology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young-Hak Kim
- Division of Cardiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young-Shin Lee
- Department of Hematology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young-Ah Kang
- Department of Hematology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Mijin Jeon
- Department of Hematology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyeran Kang
- Department of Hematology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jimin Woo
- Department of Hematology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Je-Hwan Lee
- Department of Hematology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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17
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Gupta V. Post-transplant dynamic risk prediction. NATURE COMPUTATIONAL SCIENCE 2022; 2:144-145. [PMID: 38177450 DOI: 10.1038/s43588-022-00220-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Affiliation(s)
- Vibhuti Gupta
- Department of Computer Science and Data Science, School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, USA.
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18
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Liu X, Cao Y, Guo Y, Gong X, Feng Y, Wang Y, Wang M, Cui M, Guo W, Zhang L, Zhao N, Song X, Zheng X, Chen X, Shen Q, Zhang S, Song Z, Li L, Feng S, Han M, Zhu X, Jiang E, Chen J. Dynamic forecasting of severe acute graft-versus-host disease after transplantation. NATURE COMPUTATIONAL SCIENCE 2022; 2:153-159. [PMID: 38177449 PMCID: PMC10766514 DOI: 10.1038/s43588-022-00213-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/14/2022] [Indexed: 01/06/2024]
Abstract
Forecasting of severe acute graft-versus-host disease (aGVHD) after transplantation is a challenging 'large p, small n' problem that suffers from nonuniform data sampling. We propose a dynamic probabilistic algorithm, daGOAT, that accommodates sampling heterogeneity, integrates multidimensional clinical data and continuously updates the daily risk score for severe aGVHD onset within a two-week moving window. In the studied cohorts, the cross-validated area under the receiver operator characteristic curve (AUROC) of daGOAT rose steadily after transplantation and peaked at ≥0.78 in both the adult and pediatric cohorts, outperforming the two-biomarker MAGIC score, three-biomarker Ann Arbor score, peri-transplantation features-based models and XGBoost. Simulation experiments indicated that the daGOAT algorithm is well suited for short time-series scenarios where the underlying process for event generation is smooth, multidimensional and where there are frequent and irregular data missing. daGOAT's broader utility was demonstrated by performance testing on a remotely different task, that is, prediction of imminent human postural change based on smartphone inertial sensor time-series data.
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Affiliation(s)
- Xueou Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Yigeng Cao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Ye Guo
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Xiaowen Gong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Yahui Feng
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Yao Wang
- Yidu Cloud Technology Inc., Beijing, China
| | - Mingyang Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | | | - Wenwen Guo
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Luyang Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Ningning Zhao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Xiaoqiang Song
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Xuetong Zheng
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Xia Chen
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Qiujin Shen
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Song Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Zhen Song
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Linfeng Li
- Yidu Cloud Technology Inc., Beijing, China
| | - Sizhou Feng
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Mingzhe Han
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Xiaofan Zhu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
| | - Erlie Jiang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
| | - Junren Chen
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
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Taheriyan M, Safaee Nodehi S, Niakan Kalhori SR, Mohammadzadeh N. A systematic review of the predicted outcomes related to hematopoietic stem cell transplantation: focus on applied machine learning methods' performance. Expert Rev Hematol 2022; 15:137-156. [PMID: 35184654 DOI: 10.1080/17474086.2022.2042248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION : Hematopoietic stem cell transplantation (HSCT) is a critical therapeutic procedure in blood diseases, and the investigation of HSCT data can provide valuable information. Machine learning (ML) techniques are novel and useful data analysis tools that have been applied in many studies to predict HSCT survival and estimate the risk of transplantation. AREAS COVERED : A systematic review was performed with a search of PubMed, Science Direct, Embase, Scopus, and the European Society for Blood and Marrow Transplantation, the Center for International Blood and Marrow Transplant Research, and the American Society for Transplantation and Cellular Therapy publications for articles published by September 2020. EXPERT OPINION : After investigating the results, 24 papers that met eligibility criteria were included in this study. The applied ML algorithms with the highest performance were Random Survival Forests (AUC=0.72) for survival-related, Random Survival Forests and Logistic Regression (AUC=0.77) for mortality-related, Deep Learning (AUC=0.8) for relapse, L2-Regularized Logistic Regression (AUC=0.66) for Acute-Graft Versus Host Disease, Random Survival Forests (AUC=0.88) for sepsis, Elastic-Net Regression (AUC=0.89) for cognitive impairment, and Bayesian Network (AUC=0.997) for oral mucositis outcome. This review reveals the potential of ML techniques to predict HSCT outcomes and apply them to developing clinical decision support systems.
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Affiliation(s)
- Moloud Taheriyan
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Sharareh R Niakan Kalhori
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.,Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
| | - Niloofar Mohammadzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
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20
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Lee S, Lee E, Park SS, Park MS, Jung J, Min GJ, Park S, Lee SE, Cho BS, Eom KS, Kim YJ, Lee S, Kim HJ, Min CK, Cho SG, Lee JW, Hwang HJ, Yoon JH. Prediction and recommendation by machine learning through repetitive internal validation for hepatic veno-occlusive disease/sinusoidal obstruction syndrome and early death after allogeneic hematopoietic cell transplantation. Bone Marrow Transplant 2022; 57:538-546. [DOI: 10.1038/s41409-022-01583-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 12/23/2022]
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21
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Establishment of a Predictive Model for GvHD-free, Relapse-free Survival after Allogeneic HSCT using Ensemble Learning. Blood Adv 2021; 6:2618-2627. [PMID: 34933327 PMCID: PMC9043925 DOI: 10.1182/bloodadvances.2021005800] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/23/2021] [Indexed: 12/03/2022] Open
Abstract
Stacked ensemble of machine-learning algorithms could establish more accurate prediction model for survival analysis than existing methods. Stacked ensemble model can be applied to personalized prediction of HSCT outcomes from pretransplant characteristics.
Graft-versus-host disease-free, relapse-free survival (GRFS) is a useful composite end point that measures survival without relapse or significant morbidity after allogeneic hematopoietic stem cell transplantation (allo-HSCT). We aimed to develop a novel analytical method that appropriately handles right-censored data and competing risks to understand the risk for GRFS and each component of GRFS. This study was a retrospective data-mining study on a cohort of 2207 adult patients who underwent their first allo-HSCT within the Kyoto Stem Cell Transplantation Group, a multi-institutional joint research group of 17 transplantation centers in Japan. The primary end point was GRFS. A stacked ensemble of Cox Proportional Hazard (Cox-PH) regression and 7 machine-learning algorithms was applied to develop a prediction model. The median age for the patients was 48 years. For GRFS, the stacked ensemble model achieved better predictive accuracy evaluated by C-index than other state-of-the-art competing risk models (ensemble model: 0.670; Cox-PH: 0.668; Random Survival Forest: 0.660; Dynamic DeepHit: 0.646). The probability of GRFS after 2 years was 30.54% for the high-risk group and 40.69% for the low-risk group (hazard ratio compared with the low-risk group: 2.127; 95% CI, 1.19-3.80). We developed a novel predictive model for survival analysis that showed superior risk stratification to existing methods using a stacked ensemble of multiple machine-learning algorithms.
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22
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Clichet V, Harrivel V, Delette C, Guiheneuf E, Gautier M, Morel P, Assouan D, Merlusca L, Beaumont M, Lebon D, Caulier A, Marolleau JP, Matthes T, Vergez F, Garçon L, Boyer T. Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry. Br J Haematol 2021; 196:1175-1183. [PMID: 34730236 DOI: 10.1111/bjh.17933] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/13/2021] [Accepted: 10/18/2021] [Indexed: 12/19/2022]
Abstract
Monoclonal gammopathy of unknown significance (MGUS), smouldering multiple myeloma (SMM), and multiple myeloma (MM) are very common neoplasms. However, it is often difficult to distinguish between these entities. In the present study, we aimed to classify the most powerful markers that could improve diagnosis by multiparametric flow cytometry (MFC). The present study included 348 patients based on two independent cohorts. We first assessed how representative the data were in the discovery cohort (123 MM, 97 MGUS) and then analysed their respective plasma cell (PC) phenotype in order to obtain a set of correlations with a hypersphere visualisation. Cluster of differentiation (CD)27 and CD38 were differentially expressed in MGUS and MM (P < 0·001). We found by a gradient boosting machine method that the percentage of abnormal PCs and the ratio PC/CD117 positive precursors were the most influential parameters at diagnosis to distinguish MGUS and MM. Finally, we designed a decisional algorithm allowing a predictive classification ≥95% when PC dyscrasias were suspected, without any misclassification between MGUS and SMM. We validated this algorithm in an independent cohort of PC dyscrasias (n = 87 MM, n = 41 MGUS). This artificial intelligence model is freely available online as a diagnostic tool application website for all MFC centers worldwide (https://aihematology.shinyapps.io/PCdyscrasiasToolDg/).
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Affiliation(s)
- Valentin Clichet
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France
| | | | - Caroline Delette
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Eric Guiheneuf
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France
| | - Murielle Gautier
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France
| | - Pierre Morel
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Déborah Assouan
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Lavinia Merlusca
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Marie Beaumont
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Delphine Lebon
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Alexis Caulier
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Jean-Pierre Marolleau
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Thomas Matthes
- Service d'Hématologie, Hôpital Universitaire de Genève, Genève, Suisse
| | - François Vergez
- Laboratoire d'Hématologie, Institut Universitaire du Cancer de Toulouse, Toulouse, France
| | - Loïc Garçon
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Thomas Boyer
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
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23
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Machine learning-based scoring models to predict hematopoietic stem cell mobilization in allogeneic donors. Blood Adv 2021; 6:1991-2000. [PMID: 34555850 PMCID: PMC9006268 DOI: 10.1182/bloodadvances.2021005149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 07/16/2021] [Indexed: 11/20/2022] Open
Abstract
Mobilized peripheral blood has become the primary source of hematopoietic stem cells for both autologous and allogeneic stem cell transplantation. Granulocyte Colony-Stimulating Factor (G-CSF) is currently the standard agent used in the allogeneic setting. Despite the high mobilization efficacy in most donors, G-CSF requires 4-5 days of daily administration, and a small percentage of the donors fail to mobilize an optimal number of stem cells necessary for a safe allogeneic stem cell transplant. In this study, we retrospectively reviewed 1361 related allogeneic donors who underwent stem cell mobilization at Washington University. We compared the standard mobilization agent G-CSF with five alternative mobilization regimens, including GM-CSF, G-CSF+GM-CSF, GM-CSF + Plerixafor, Plerixafor and BL-8040. Cytokine-based mobilization strategies (G-CSF or in combination with GM-CSF) induce higher CD34 cell yield after 4-5 consecutive days of treatment, while CXCR4 antagonists (plerixafor and BL-8040) induce significantly less but rapid mobilization on the same day. Next, using a large dataset containing the demographic and baseline laboratory data from G-CSF-mobilized donors, we established machine learning (ML)-based scoring models that can be used to predict patients who may have less than optimal stem cell yields after a single leukapheresis session. To our knowledge, this is the first prediction model at the early donor screening stage, which may help identify allogeneic stem cell donors who may benefit from alternative approaches to enhance stem cell yields thus insuring safe and effective stem cell transplantation.
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24
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Salehnasab C, Hajifathali A, Asadi F, Parkhideh S, Kazemi A, Roshanpoor A, Mehdizadeh M, Tavakoli-Ardakani M, Roshandel E. An Intelligent Clinical Decision Support System for Predicting Acute Graft-versus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation. J Biomed Phys Eng 2021; 11:345-356. [PMID: 34189123 PMCID: PMC8236103 DOI: 10.31661/jbpe.v0i0.2012-1244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 04/10/2021] [Indexed: 11/16/2022]
Abstract
Background: Acute graft-versus-host disease (aGvHD) is a complex and often multisystem disease that causes morbidity and mortality in 35% of patients receiving allogeneic hematopoietic stem cell transplantation (AHSCT). Objective: This study aimed to implement a Clinical Decision Support System (CDSS) for predicting aGvHD following AHSCT on the transplantation day. Material and Methods: In this developmental study, the data of 182 patients with 31 attributes, which referred to Taleghani Hospital Tehran, Iran during 2009–2017, were analyzed by machine learning (ML) algorithms which included XGBClassifier, HistGradientBoostingClassifier, AdaBoostClassifier, and RandomForestClassifier. The criteria measurement used to evaluate these algorithms included accuracy, sensitivity, and specificity. Using the machine learning developed model, a CDSS was implemented. The performance of the CDSS was evaluated by Cohen’s Kappa coefficient. Results: Of the 31 included variables, albumin, uric acid, C-reactive protein, donor age, platelet, lactate Dehydrogenase, and Hemoglobin were identified as the most important predictors. The two algorithms XGBClassifier and HistGradientBoostingClassifier with an average accuracy of 90.70%, sensitivity of 92.5%, and specificity of 89.13% were selected as the most appropriate ML models for predicting aGvHD. The agreement between CDSS prediction and patient outcome was 92%. Conclusion: ML methods can reliably predict the likelihood of aGvHD at the time of transplantation. These methods can help us to limit the number of risk factors to those that have significant effects on the outcome. However, their performance is heavily dependent on selecting the appropriate methods and algorithms. The next generations of CDSS may use more and more machine learning approaches.
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Affiliation(s)
- Cirruse Salehnasab
- PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Abbas Hajifathali
- MD, Hematopoietic Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farkhondeh Asadi
- PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sayeh Parkhideh
- MD, Hematopoietic Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Kazemi
- PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arash Roshanpoor
- PhD, Department of Computer Science, Sama Technical and Vocational Training College, Tehran Branch (Tehran), Islamic Azad University (IAU), Tehran, Iran
| | - Mahshid Mehdizadeh
- MD, Hematopoietic Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maria Tavakoli-Ardakani
- MD, Hematopoietic Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Elham Roshandel
- PhD, Hematopoietic Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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25
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Okamura H, Nakamae M, Koh S, Nanno S, Nakashima Y, Koh H, Nakane T, Hirose A, Hino M, Nakamae H. Interactive Web Application for Plotting Personalized Prognosis Prediction Curves in Allogeneic Hematopoietic Cell Transplantation Using Machine Learning. Transplantation 2021; 105:1090-1096. [PMID: 32541556 DOI: 10.1097/tp.0000000000003357] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Allogeneic hematopoietic cell transplantation (allo-HCT) is a curative treatment option for malignant hematological disorders. Transplant clinicians estimate patient-specific prognosis empirically in clinical practice based on previous studies on similar patients. However, this approach does not provide objective data. The present study primarily aimed to develop a tool capable of providing accurate personalized prognosis prediction after allo-HCT in an objective manner. METHODS We developed an interactive web application tool with a graphical user interface capable of plotting the personalized survival and cumulative incidence prediction curves after allo-HCT adjusted by 8 patient-specific factors, which are known as prognostic predictors, and assessed their predictive performances. A random survival forest model using the data of patients who underwent allo-HCT at our institution was applied to develop this application. RESULTS We succeeded in showing the personalized prognosis prediction curves of 1-year overall survival, progression-free survival, relapse/progression, and nonrelapse mortality (NRM) interactively using our web application (https://predicted-os-after-transplantation.shinyapps.io/RSF_model/). To assess its predictive performance, the entire cohort (363 cases) was split into a training cohort (70%) and a test cohort (30%) time-sequentially based on the patients' transplant dates. The areas under the receiver-operating characteristic curves for 1-year overall survival, progression-free survival, relapse/progression, and nonrelapse mortality in test cohort were 0.70, 0.72, 0.73, and 0.77, respectively. CONCLUSIONS The new web application could allow transplant clinicians to inform a new allo-HCT candidate of the objective personalized prognosis prediction and facilitate decision-making.
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Affiliation(s)
- Hiroshi Okamura
- Hematology, Graduate School of Medicine, Osaka City University, Osaka, Japan
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26
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Tang S, Chappell GT, Mazzoli A, Tewari M, Choi SW, Wiens J. Predicting Acute Graft-Versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data From Electronic Health Records. JCO Clin Cancer Inform 2021; 4:128-135. [PMID: 32083957 PMCID: PMC7049247 DOI: 10.1200/cci.19.00105] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
PURPOSE Acute graft-versus-host disease (aGVHD) remains a significant complication of allogeneic hematopoietic cell transplantation (HCT) and limits its broader application. The ability to predict grade II to IV aGVHD could potentially mitigate morbidity and mortality. To date, researchers have focused on using snapshots of a patient (eg, biomarkers at a single time point) to predict aGVHD onset. We hypothesized that longitudinal data collected and stored in electronic health records (EHRs) could distinguish patients at high risk of developing aGVHD from those at low risk. PATIENTS AND METHODS The study included a cohort of 324 patients undergoing allogeneic HCT at the University of Michigan C.S. Mott Children’s Hospital during 2014 to 2017. Using EHR data, specifically vital sign measurements collected within the first 10 days of transplantation, we built a predictive model using penalized logistic regression for identifying patients at risk for grade II to IV aGVHD. We compared the proposed model with a baseline model trained only on patient and donor characteristics collected at the time of transplantation and performed an analysis of the importance of different input features. RESULTS The proposed model outperformed the baseline model, with an area under the receiver operating characteristic curve of 0.659 versus 0.512 (P = .019). The feature importance analysis showed that the learned model relied most on temperature and systolic blood pressure, and temporal trends (eg, increasing or decreasing) were more important than the average values. CONCLUSION Leveraging readily available clinical data from EHRs, we developed a machine-learning model for aGVHD prediction in patients undergoing HCT. Continuous monitoring of vital signs, such as temperature, could potentially help clinicians more accurately identify patients at high risk for aGVHD.
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Affiliation(s)
- Shengpu Tang
- Division of Computer Science and Engineering, Department of Electronic Engineering and Computer Science, University of Michigan, Ann Arbor, MI
| | - Grant T Chappell
- Division of Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI
| | - Amanda Mazzoli
- Division of Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI
| | - Muneesh Tewari
- Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI.,Biointerfaces Institute, University of Michigan, Ann Arbor, MI
| | - Sung Won Choi
- Division of Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI
| | - Jenna Wiens
- Division of Computer Science and Engineering, Department of Electronic Engineering and Computer Science, University of Michigan, Ann Arbor, MI
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27
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Bachoud-Lévi AC, Massart R, Rosser A. Cell therapy in Huntington's disease: Taking stock of past studies to move the field forward. Stem Cells 2021; 39:144-155. [PMID: 33176057 PMCID: PMC10234449 DOI: 10.1002/stem.3300] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 10/01/2020] [Accepted: 10/20/2020] [Indexed: 06/02/2023]
Abstract
Huntington's disease (HD) is a rare inherited neurodegenerative disease that manifests mostly in adulthood with progressive cognitive, behavioral, and motor dysfunction. Neuronal loss occurs predominantly in the striatum but also extends to other brain regions, notably the cortex. Most patients die around 20 years after motor onset, although there is variability in the rate of progression and some phenotypic heterogeneity. The most advanced experimental therapies currently are huntingtin-lowering strategies, some of which are in stage 3 clinical trials. However, even if these approaches are successful, it is unlikely that they will be applicable to all patients or will completely halt continued loss of neural cells in all cases. On the other hand, cellular therapies have the potential to restore atrophied tissues and may therefore provide an important complementary therapeutic avenue. Pilot studies of fetal cell grafts in the 2000s reported the most dramatic clinical improvements yet achieved for this disease, but subsequent studies have so far failed to identify methodology to reliably reproduce these results. Moving forward, a major challenge will be to generate suitable donor cells from (nonfetal) cell sources, but in parallel there are a host of procedural and trial design issues that will be important for improving reliability of transplants and so urgently need attention. Here, we consider findings that have emerged from clinical transplant studies in HD to date, in particular new findings emerging from the recent multicenter intracerebral transplant HD study, and consider how these data may be used to inform future cell therapy trials.
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Affiliation(s)
- Anne-Catherine Bachoud-Lévi
- Assistance Publique-Hôpitaux de Paris, National Reference Center for Huntington's Disease, Neurology Department, Henri Mondor-Albert Chenevier Hospital, Créteil, France
- Département d'Etudes Cognitives, École Normale Supérieure, PSL University, Paris, France
- Inserm U955, Institut Mondor de Recherche Biomédicale, Equipe E01 NeuroPsychologie Interventionnelle, Créteil, France
- NeurATRIS, Créteil, France
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
| | - Renaud Massart
- Assistance Publique-Hôpitaux de Paris, National Reference Center for Huntington's Disease, Neurology Department, Henri Mondor-Albert Chenevier Hospital, Créteil, France
- Département d'Etudes Cognitives, École Normale Supérieure, PSL University, Paris, France
- Inserm U955, Institut Mondor de Recherche Biomédicale, Equipe E01 NeuroPsychologie Interventionnelle, Créteil, France
- NeurATRIS, Créteil, France
| | - Anne Rosser
- Centre for Trials Research, Cardiff University, Cardiff, UK
- Cardiff University Brain Repair Group, Life Sciences Building, School of Biosciences, Cardiff, UK
- Neuroscience and Mental Health Research Institute and Division of Psychological Medicine and Clinical Neurosciences, Hadyn Ellis Building, Cardiff, UK
- Brain Repair And Intracranial Neurotherapeutics (BRAIN) Unit, Cardiff University, Cardiff, UK
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28
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Radakovich N, Nagy M, Nazha A. Artificial Intelligence in Hematology: Current Challenges and Opportunities. Curr Hematol Malig Rep 2020; 15:203-210. [PMID: 32239350 DOI: 10.1007/s11899-020-00575-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI), and in particular its subcategory machine learning, is finding an increasing number of applications in medicine, driven in large part by an abundance of data and powerful, accessible tools that have made AI accessible to a larger circle of investigators. RECENT FINDINGS AI has been employed in the analysis of hematopathological, radiographic, laboratory, genomic, pharmacological, and chemical data to better inform diagnosis, prognosis, treatment planning, and foundational knowledge related to benign and malignant hematology. As more widespread implementation of clinical AI nears, attention has also turned to the effects this will have on other areas in medicine. AI offers many promising tools to clinicians broadly, and specifically in the practice of hematology. Ongoing research into its various applications will likely result in an increasing utilization of AI by a broader swath of clinicians.
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Affiliation(s)
- Nathan Radakovich
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Matthew Nagy
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Aziz Nazha
- Center for Clinical Artificial Intelligence, Cleveland Clinic, Cleveland, OH, USA.
- Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Desk R35 9500 Euclid Ave., Cleveland, OH, 44195, USA.
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29
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Eckardt JN, Bornhäuser M, Wendt K, Middeke JM. Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects. Blood Adv 2020; 4:6077-6085. [PMID: 33290546 PMCID: PMC7724910 DOI: 10.1182/bloodadvances.2020002997] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 10/26/2020] [Indexed: 12/19/2022] Open
Abstract
Machine learning (ML) is rapidly emerging in several fields of cancer research. ML algorithms can deal with vast amounts of medical data and provide a better understanding of malignant disease. Its ability to process information from different diagnostic modalities and functions to predict prognosis and suggest therapeutic strategies indicates that ML is a promising tool for the future management of hematologic malignancies; acute myeloid leukemia (AML) is a model disease of various recent studies. An integration of these ML techniques into various applications in AML management can assure fast and accurate diagnosis as well as precise risk stratification and optimal therapy. Nevertheless, these techniques come with various pitfalls and need a strict regulatory framework to ensure safe use of ML. This comprehensive review highlights and discusses recent advances in ML techniques in the management of AML as a model disease of hematologic neoplasms, enabling researchers and clinicians alike to critically evaluate this upcoming, potentially practice-changing technology.
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Affiliation(s)
- Jan-Niklas Eckardt
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Martin Bornhäuser
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
- National Center for Tumor Diseases, Dresden (NCT/UCC), Dresden, Germany
- German Consortium for Translational Cancer Research, DKFZ, Heidelberg, Germany; and
| | - Karsten Wendt
- Institute of Circuits and Systems, Technical University Dresden, Dresden, Germany
| | - Jan Moritz Middeke
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
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30
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Alsuliman T, Humaidan D, Sliman L. Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality? Curr Res Transl Med 2020; 68:245-251. [DOI: 10.1016/j.retram.2020.01.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 12/22/2019] [Accepted: 01/18/2020] [Indexed: 12/14/2022]
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Gupta V, Braun TM, Chowdhury M, Tewari M, Choi SW. A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT). SENSORS (BASEL, SWITZERLAND) 2020; 20:E6100. [PMID: 33120974 PMCID: PMC7663237 DOI: 10.3390/s20216100] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/19/2020] [Accepted: 10/25/2020] [Indexed: 12/11/2022]
Abstract
Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included "hematopoietic cell transplantation (HCT)," "autologous HCT," "allogeneic HCT," "machine learning," and "artificial intelligence." Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required.
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Affiliation(s)
- Vibhuti Gupta
- Michigan Medicine, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Thomas M. Braun
- School of Public Health, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Mosharaf Chowdhury
- Michigan Engineering, Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Muneesh Tewari
- Michigan Medicine, Department of Internal Medicine, Hematology/Oncology Division, University of Michigan, Ann Arbor, MI 48109, USA;
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Engineering, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sung Won Choi
- Michigan Medicine, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
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Shouval R, Fein JA, Savani B, Mohty M, Nagler A. Machine learning and artificial intelligence in haematology. Br J Haematol 2020; 192:239-250. [PMID: 32602593 DOI: 10.1111/bjh.16915] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Digitalization of the medical record and integration of genomic methods into clinical practice have resulted in an unprecedented wealth of data. Machine learning is a subdomain of artificial intelligence that attempts to computationally extract meaningful insights from complex data structures. Applications of machine learning in haematological scenarios are steadily increasing. However, basic concepts are often unfamiliar to clinicians and investigators. The purpose of this review is to provide readers with tools to interpret and critically appraise machine learning literature. We begin with the elucidation of standard terminology and then review examples in haematology. Guidelines for designing and evaluating machine-learning studies are provided. Finally, we discuss limitations of the machine-learning approach.
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Affiliation(s)
- Roni Shouval
- Adult Bone Marrow Transplant Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Hematology and Bone Marrow Transplantation Division, Chaim Sheba Medical Center, Tel-Hashomer, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Joshua A Fein
- University of Connecticut Medical Center, Farmington, CT, USA
| | - Bipin Savani
- Division of Hematology-Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mohamad Mohty
- European Society for Blood and Marrow Transplantation Paris Study Office/CEREST-TC, Paris, France.,Service d'Hématologie Clinique et de Thérapie Cellulaire, Hôpital Saint Antoine, AP-HP, Paris, France
| | - Arnon Nagler
- Hematology and Bone Marrow Transplantation Division, Chaim Sheba Medical Center, Tel-Hashomer, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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Adom D, Rowan C, Adeniyan T, Yang J, Paczesny S. Biomarkers for Allogeneic HCT Outcomes. Front Immunol 2020; 11:673. [PMID: 32373125 PMCID: PMC7186420 DOI: 10.3389/fimmu.2020.00673] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 03/25/2020] [Indexed: 12/23/2022] Open
Abstract
Allogeneic hematopoietic cell transplantation (HCT) remains the only curative therapy for many hematological malignant and non-malignant disorders. However, key obstacles to the success of HCT include graft-versus-host disease (GVHD) and disease relapse due to absence of graft-versus-tumor (GVT) effect. Over the last decade, advances in "omics" technologies and systems biology analysis, have allowed for the discovery and validation of blood biomarkers that can be used as diagnostic test and prognostic test (that risk-stratify patients before disease occurrence) for acute and chronic GVHD and recently GVT. There are also predictive biomarkers that categorize patients based on their likely to respond to therapy. Newer mathematical analysis such as machine learning is able to identify different predictors of GVHD using clinical characteristics pre-transplant and possibly in the future combined with other biomarkers. Biomarkers are not only useful to identify patients with higher risk of disease progression, but also help guide treatment decisions and/or provide a basis for specific therapeutic interventions. This review summarizes biomarkers definition, omics technologies, acute, chronic GVHD and GVT biomarkers currently used in clinic or with potential as targets for existing or new drugs focusing on novel published work.
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Affiliation(s)
- Djamilatou Adom
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States.,Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, IN, United States.,Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Courtney Rowan
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States.,Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, IN, United States.,Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Titilayo Adeniyan
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States.,Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, IN, United States.,Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Jinfeng Yang
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States.,Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, IN, United States.,Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Sophie Paczesny
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States.,Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, IN, United States.,Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States
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