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Rabindranath M, Naghibzadeh M, Zhao X, Holdsworth S, Brudno M, Sidhu A, Bhat M. Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold? Transplantation 2024; 108:1700-1708. [PMID: 39042768 DOI: 10.1097/tp.0000000000004876] [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] [Indexed: 12/08/2023]
Abstract
Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.
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Affiliation(s)
- Madhumitha Rabindranath
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Maryam Naghibzadeh
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Xun Zhao
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Sandra Holdsworth
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Michael Brudno
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Aman Sidhu
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
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Arvind A, Seif El Dahan K, Malhotra R, Daher D, Rich NE, Patel MS, VanWagner LB, Lieber SR, Cotter TG, Louissaint J, Mufti AR, Kulik L, Pillai A, Parikh ND, Singal AG. Association between bridging therapy and posttransplant outcomes in patients with HCC within Milan criteria: A systematic review and meta-analysis. Liver Transpl 2024; 30:595-606. [PMID: 38466889 DOI: 10.1097/lvt.0000000000000357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 01/15/2024] [Indexed: 03/13/2024]
Abstract
Liver transplantation is the curative therapy of choice for patients with early-stage HCC. Locoregional therapies are often employed as a bridge to reduce the risk of waitlist dropout; however, their association with posttransplant outcomes is unclear. We conducted a systematic review using Ovid MEDLINE and EMBASE to identify studies published between database inception and August 2, 2023, which reported posttransplant recurrence-free survival and overall survival among patients transplanted for HCC within Milan criteria, stratified by receipt of bridging therapy. Pooled HRs were calculated for each outcome using the DerSimonian and Laird method for a random-effects model. We identified 38 studies, including 19,671 patients who received and 20,148 patients who did not receive bridging therapy. Bridging therapy was not associated with significant differences in recurrence-free survival (pooled HR: 0.91, 95% CI: 0.77-1.08; I2 =39%) or overall survival (pooled HR: 1.09, 95% CI: 0.95-1.24; I2 =47%). Results were relatively consistent across subgroups, including geographic location and study period. Studies were discordant regarding the differential strength of association by pretreatment tumor burden and pathologic response, but potential benefits of locoregional therapy were mitigated in those who received 3 or more treatments. Adverse events were reported in a minority of studies, but when reported occurred in 6%-15% of the patients. Few studies reported loss to follow-up and most had a risk of residual confounding. Bridging therapy is not associated with improvements in posttransplant recurrence-free or overall survival among patients with HCC within Milan criteria. The risk-benefit ratio of bridging therapy likely differs based on the risk of waitlist dropout.
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Affiliation(s)
- Ashwini Arvind
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Karim Seif El Dahan
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Riya Malhotra
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Darine Daher
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Nicole E Rich
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Madhukar S Patel
- Department of Surgery, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Lisa B VanWagner
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Sarah R Lieber
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Thomas G Cotter
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Jeremy Louissaint
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Arjmand R Mufti
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Laura Kulik
- Department of Internal Medicine, Northwestern Medicine, Chicago, Illinois, USA
| | - Anjana Pillai
- Department of Internal Medicine, University of Chicago, Chicago, Illinois, USA
| | - Neehar D Parikh
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Amit G Singal
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
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Sasaki K, Melcher ML. Where is the perfect triangle in the liver allocation system? THE LANCET. HEALTHY LONGEVITY 2024; 5:e310-e311. [PMID: 38705149 DOI: 10.1016/s2666-7568(24)00064-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 04/03/2024] [Indexed: 05/07/2024] Open
Affiliation(s)
- Kazunari Sasaki
- Division of Abdominal Transplant, Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA.
| | - Marc L Melcher
- Division of Abdominal Transplant, Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
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Deng J, Zhou C, Xiao F, Chen J, Li C, Xie Y. Construction of a predictive model for blood transfusion in patients undergoing total hip arthroplasty and identification of clinical heterogeneity. Sci Rep 2024; 14:724. [PMID: 38184749 PMCID: PMC10771504 DOI: 10.1038/s41598-024-51240-2] [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: 06/29/2023] [Accepted: 01/02/2024] [Indexed: 01/08/2024] Open
Abstract
A precise forecast of the need for blood transfusions (BT) in patients undergoing total hip arthroplasty (THA) is a crucial step toward the implementation of precision medicine. To achieve this goal, we utilized supervised machine learning (SML) techniques to establish a predictive model for BT requirements in THA patients. Additionally, we employed unsupervised machine learning (UML) approaches to identify clinical heterogeneity among these patients. In this study, we recruited 224 patients undergoing THA. To identify factors predictive of BT during the perioperative period of THA, we employed LASSO regression and the random forest (RF) algorithm as part of supervised machine learning (SML). Using logistic regression, we developed a predictive model for BT in THA patients. Furthermore, we utilized unsupervised machine learning (UML) techniques to cluster THA patients who required BT based on similar clinical features. The resulting clusters were subsequently visualized and validated. We constructed a predictive model for THA patients who required BT based on six predictive factors: Age, Body Mass Index (BMI), Hemoglobin (HGB), Platelet (PLT), Bleeding Volume, and Urine Volume. Before surgery, 1 h after surgery, 1 day after surgery, and 1 week after surgery, significant differences were observed in HGB and PLT levels between patients who received BT and those who did not. The predictive model achieved an AUC of 0.899. Employing UML, we identified two distinct clusters with significantly heterogeneous clinical characteristics. Age, BMI, PLT, HGB, bleeding volume, and urine volume were found to be independent predictors of BT requirement in THA patients. The predictive model incorporating these six predictors demonstrated excellent predictive performance. Furthermore, employing UML enabled us to classify a heterogeneous cohort of THA patients who received BT in a meaningful and interpretable manner.
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Affiliation(s)
- Jicai Deng
- Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
- Department of Anesthesiology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Chenxing Zhou
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Fei Xiao
- Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
| | - Jing Chen
- Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
| | - Chunlai Li
- Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
| | - Yubo Xie
- Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China.
- Guangxi Key Laboratory of Enhanced Recovery After Surgery for Gastrointestinal Cancer, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
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Bezjak M, Kocman B, Jadrijević S, Filipec Kanižaj T, Antonijević M, Dalbelo Bašić B, Mikulić D. Use of machine learning models for identification of predictors of survival and tumour recurrence in liver transplant recipients with hepatocellular carcinoma. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:345. [PMID: 37675331 PMCID: PMC10477658 DOI: 10.21037/atm-22-6469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/19/2023] [Indexed: 09/08/2023]
Abstract
Background Hepatocellular carcinoma (HCC) is one of the leading indications for liver transplantation (LT) however, selection criteria remain controversial. We aimed to identify survival factors and predictors for tumour recurrence using machine learning (ML) methods. We also compared ML models to the Cox regression model. Methods Thirty pretransplant donor and recipient general and tumour specific parameters were analysed from 170 patients who underwent orthotopic liver transplantation for HCC between March 2013 and December 2019 at the University Hospital Merkur, Zagreb. Survival rates were calculated using the Kaplan-Meier method and multivariate analysis was performed using the Cox proportional hazards regression model. Data was also processed through Coxnet (a regularized Cox regression model), Random Survival Forest (RSF), Survival Support Vector Machine (SVM) and Survival Gradient Boosting models, which included pre-processing, variable selection, imputation of missing data, training and cross-validation of the models. The cross-validated concordance index (CI) was used as an evaluation metric and to determine the best performing model. Results Kaplan-Meier curves for 5-year survival time showed survival probability of 80% for recipient survival and 82% for graft survival. The 5-year HCC recurrence was observed in 19% of patients. The best predictive accuracy was observed in the RSF model with CI of 0.72, followed by the Survival SVM model (CI 0.70). Overall ML models outperform the Cox regression model with respect to their limitations. Random Forest analysis provided several relevant outcome predictors: alpha fetoprotein (AFP), donor C-reactive protein (CRP), recipient age and neutrophil to lymphocyte ratio (NLR). Cox multivariate analysis showed similarities with RSF models in identifying detrimental variables. Some variables such as donor age and number of transarterial chemoembolization treatments (TACE) were pointed out, but these were not influential in our RSF model. Conclusions Using ML methods in addition to classical statistical analysis, it is possible to develop sufficient prognostic models, which, compared to established risk scores, could help us quantify survival probability and make changes in organ utilization.
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Affiliation(s)
- Miran Bezjak
- Division of Abdominal Surgery and Organ Transplantation, Department of Surgery, University Hospital Merkur, Zagreb, Croatia
| | - Branislav Kocman
- Division of Abdominal Surgery and Organ Transplantation, Department of Surgery, University Hospital Merkur, Zagreb, Croatia
| | - Stipislav Jadrijević
- Division of Abdominal Surgery and Organ Transplantation, Department of Surgery, University Hospital Merkur, Zagreb, Croatia
| | - Tajana Filipec Kanižaj
- Division of Gastroenterology, Department of Internal Medicine, University Hospital Merkur, Zagreb, Croatia
| | | | - Bojana Dalbelo Bašić
- Faculty of Electrical Engineering and Computing, Department of Electronics, Microelectronics, Computer and Intelligent Systems, Zagreb, Croatia
| | - Danko Mikulić
- Division of Abdominal Surgery and Organ Transplantation, Department of Surgery, University Hospital Merkur, Zagreb, Croatia
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Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol 2023; 78:1216-1233. [PMID: 37208107 DOI: 10.1016/j.jhep.2023.01.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 05/21/2023]
Abstract
Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.
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Affiliation(s)
- Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
| | - Madhumitha Rabindranath
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Beatriz Sordi Chara
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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Sun X, Zhou C, Zhu J, Wu S, Liang T, Jiang J, Chen J, Chen T, Huang SS, Chen L, Ye Z, Guo H, Zhan X, Liu C. Identification of clinical heterogeneity and construction of a novel subtype predictive model in patients with ankylosing spondylitis: An unsupervised machine learning study. Int Immunopharmacol 2023; 117:109879. [PMID: 36822084 DOI: 10.1016/j.intimp.2023.109879] [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: 11/27/2022] [Revised: 01/20/2023] [Accepted: 02/06/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND Accurate classification of patients with ankylosing spondylitis (AS) is the premise of precision medicine so as to perform different medical interventions for different patient types. AS pathology is closely related to the changes in the immune microenvironment. In this study, we used unsupervised machine learning (UML) to classify patients with AS based on clinical characteristics. We then constructed a novel subtype predictive model for AS based on the clinical classification, after which we investigated the difference in the immune microenvironment to unravel the AS pathogenesis. METHODS Overall, 196 patients with AS were enrolled. UML was used to cluster AS patients by similar clinical characteristics. Functional ability, disease status, and grading of radiologic features were assessed to verify the accuracy and heterogeneity of UML clustering. Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest algorithm were used to screen and identify predictive factors for the novel subtype of AS. Logistic regression was also performed to construct a predictive model of this novel subtype. Datasets were downloaded from the Gene Expression Omnibus database to assess immune cell infiltration, and the results were validated using data of routine blood tests from 3671 AS patients and 5720 non-AS patients. The differential expression of Fat Mass and Obesity-Associated Protein (FTO), an m6A regulator, between AS patients and healthy control subjects was confirmed using immunohistochemistry. RESULTS UML clustering identified two clusters. The clinical characteristics of the two clusters were significantly heterogeneous. For the novel subtype of AS identified in UML clustering, a predictive model was built using three predictive factors, namely, C-reactive protein (CRP), absolute value of neutrophils (NEU), and absolute value of monocytes (MONO). The area under the curve of the predictive model was 0.983. Heterogeneity in the neutrophil and monocyte counts in AS was verified through immune cell infiltration analysis. Data from routine blood tests revealed that NEU and MONO were significantly higher in AS patients than in non-AS patients (p < 0.001). FTO expression was negatively correlated with both NEU and MONO. Immunohistochemistry analysis confirmed the downregulated expression of FTO. CONCLUSIONS UML provides an explicable and remarkable classification of a heterogeneous cohort of AS patients. A novel subtype of AS was identified in UML clustering. CRP, NEU, and MONO were the independent predictive factors for the novel subtype of AS. FTO expression was correlated with immune cell infiltration in AS patients.
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Affiliation(s)
- Xuhua Sun
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Chenxing Zhou
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Jichong Zhu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Shaofeng Wu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Tuo Liang
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Jie Jiang
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Jiarui Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Tianyou Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Sheng Sheng Huang
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Liyi Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Zhen Ye
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Hao Guo
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Xinli Zhan
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
| | - Chong Liu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
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Trotter JF. When worlds collide. Liver Transpl 2023; 29:132-133. [PMID: 36668690 DOI: 10.1097/lvt.0000000000000006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 01/22/2023]
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Zhou C, Huang S, Liang T, Jiang J, Chen J, Chen T, Chen L, Sun X, Zhu J, Wu S, Ye Z, Guo H, Chen W, Liu C, Zhan X. Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics. Front Surg 2022; 9:935656. [PMID: 35959114 PMCID: PMC9357891 DOI: 10.3389/fsurg.2022.935656] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 06/30/2022] [Indexed: 12/01/2022] Open
Abstract
Background Anterior cervical decompression and fusion can effectively treat cervical spondylotic myelopathy (CSM). Accurately classifying patients with CSM who have undergone anterior cervical decompression and fusion is the premise of precision medicine. In this study, we used machine learning algorithms to classify patients and compare the postoperative efficacy of each classification. Methods A total of 616 patients with cervical spondylotic myelopathy who underwent anterior cervical decompression and fusion were enrolled. Unsupervised machine learning algorithms (UMLAs) were used to cluster subjects according to similar clinical characteristics. Then, the results of clustering were visualized. The surgical outcomes were used to verify the accuracy of machine learning clustering. Results We identified two clusters in these patients who had significantly different baseline clinical characteristics, preoperative complications, the severity of neurological symptoms, and the range of decompression required for surgery. UMLA divided the CSM patients into two clusters according to the severity of their illness. The repose to surgical treatment between the clusters was significantly different. Conclusions Our results showed that UMLA could be used to rationally classify a heterogeneous cohort of CSM patients effectively, and thus, it might be used as the basis for a data-driven platform for identifying the cluster of patients who can respond to a particular treatment method.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Chong Liu
- Correspondence: Chong Liu Xinli Zhan
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