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Zuo Z, Ma J, Yan M, Ge W, Yao T, Zhou L, Zeng Y, Liu Y. Machine learning-derived prognostic signature for progression-free survival in non-metastatic nasopharyngeal carcinoma. Head Neck 2024. [PMID: 39077955 DOI: 10.1002/hed.27895] [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: 03/11/2024] [Revised: 07/01/2024] [Accepted: 07/16/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND Early detection of high-risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning-derived prognostic signature (MLDPS) by combining three machine learning (ML) models to predict progression-free survival (PFS) in patients with non-metastatic NPC. METHODS A cohort of 653 patients with non-metastatic NPC was divided into a training (n = 457) and validation (n = 196) dataset (7:3 ratio). The study included clinicopathological characteristics, hematologic markers, and MRI findings in three machine learning models-random forest (RF), extreme gradient boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO)-to predict progression-free survival (PFS). A Venn diagram identified the overlapping signatures from the three ML algorithms. Cox proportional hazard analysis determined the MLDPS for PFS. RESULTS The RF, XGBoost, and LASSO algorithms identified six consensus factors from the 33 signatures. Cox proportional hazards analysis showed that the MLDPS includes age, lymphocyte count, number of positive lymph nodes, and regional lymph node density. Additionally, MLDPS effectively stratified prognosis, with low-risk individuals showing better PFS than high-risk individuals (p < 0.001). CONCLUSION MLDPS, based on clinicopathological characteristics, hematologic markers, and MRI findings, is crucial for guiding clinical management and personalizing treatments for patients with non-metastatic NPC.
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Affiliation(s)
- Zhichao Zuo
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, China
| | - Jie Ma
- Department of Medical Imaging, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Mi Yan
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, China
| | - Wu Ge
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, China
| | - Ting Yao
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, China
| | - Lu Zhou
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, China
| | - Ying Zeng
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, China
| | - Yang Liu
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
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Moharrami M, Azimian Zavareh P, Watson E, Singhal S, Johnson AEW, Hosni A, Quinonez C, Glogauer M. Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review. PLoS One 2024; 19:e0307531. [PMID: 39046953 PMCID: PMC11268644 DOI: 10.1371/journal.pone.0307531] [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: 04/30/2024] [Accepted: 07/07/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data. METHODS A systematic search was conducted across the Medline, Scopus, Embase, Web of Science, and Google Scholar databases. The methodological characteristics and performance metrics of studies that developed and validated ML models were assessed. The risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS Out of 5,560 unique records, 34 articles were included. For survival outcome, the ML model outperformed the Cox proportional hazards model in time-to-event analyses for HNC, with a concordance index of 0.70-0.79 vs. 0.66-0.76, and for all sub-sites including oral cavity (0.73-0.89 vs. 0.69-0.77) and larynx (0.71-0.85 vs. 0.57-0.74). In binary classification analysis, the area under the receiver operating characteristics (AUROC) of ML models ranged from 0.75-0.97, with an F1-score of 0.65-0.89 for HNC; AUROC of 0.61-0.91 and F1-score of 0.58-0.86 for the oral cavity; and AUROC of 0.76-0.97 and F1-score of 0.63-0.92 for the larynx. Disease-specific survival outcomes showed higher performance than overall survival outcomes, but the performance of ML models did not differ between three- and five-year follow-up durations. For disease progression outcomes, no time-to-event metrics were reported for ML models. For binary classification of the oral cavity, the only evaluated subsite, the AUROC ranged from 0.67 to 0.97, with F1-scores between 0.53 and 0.89. CONCLUSIONS ML models have demonstrated considerable potential in predicting post-treatment survival and disease progression, consistently outperforming traditional linear models and their derived nomograms. Future research should incorporate more comprehensive treatment features, emphasize disease progression outcomes, and establish model generalizability through external validations and the use of multicenter datasets.
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Affiliation(s)
- Mohammad Moharrami
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
| | - Parnia Azimian Zavareh
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
| | - Erin Watson
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - Sonica Singhal
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Chronic Disease and Injury Prevention Department, Health Promotion, Public Health Ontario, Toronto, Canada
| | - Alistair E. W. Johnson
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - Ali Hosni
- Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Toronto, Canada
| | - Carlos Quinonez
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Schulich School of Medicine & Dentistry, Western University, London, Canada
| | - Michael Glogauer
- Faculty of Dentistry, University of Toronto, Toronto, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Canada
- Department of Dentistry, Centre for Advanced Dental Research and Care, Mount Sinai Hospital, Toronto, Canada
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Li X, Bao H, Shi Y, Zhu W, Peng Z, Yan L, Chen J, Shu X. Machine learning methods for accurately predicting survival and guiding treatment in stage I and II hepatocellular carcinoma. Medicine (Baltimore) 2023; 102:e35892. [PMID: 37960763 PMCID: PMC10637529 DOI: 10.1097/md.0000000000035892] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 10/11/2023] [Indexed: 11/15/2023] Open
Abstract
Accurately predicting survival in patients with early hepatocellular carcinoma (HCC) is essential for making informed decisions about treatment and prognosis. Herein, we have developed a machine learning (ML) model that can predict patient survival and guide treatment decisions. We obtained patient demographic information, tumor characteristics, and treatment details from the SEER database. To analyze the data, we employed a Cox proportional hazards (CoxPH) model as well as 3 ML algorithms: neural network multitask logistic regression (N-MLTR), DeepSurv, and random survival forest (RSF). Our evaluation relied on the concordance index (C-index) and Integrated Brier Score (IBS). Additionally, we provided personalized treatment recommendations regarding surgery and chemotherapy choices and validated models' efficacy. A total of 1136 patients with early-stage (I, II) hepatocellular carcinoma (HCC) who underwent liver resection or transplantation were randomly divided into training and validation cohorts at a ratio of 3:7. Feature selection was conducted using Cox regression analyses. The ML models (NMLTR: C-index = 0.6793; DeepSurv: C-index = 0.7028; RSF: C-index = 0.6890) showed better discrimination in predicting survival than the standard CoxPH model (C-index = 0.6696). Patients who received recommended treatments had higher survival rates than those who received unrecommended treatments. ML-based surgery treatment recommendations yielded higher hazard ratios (HRs): NMTLR HR = 0.36 (95% CI: 0.25-0.51, P < .001), DeepSurv HR = 0.34 (95% CI: 0.24-0.49, P < .001), and RSF HR = 0.37 (95% CI: 0.26-0.52, P = <.001). Chemotherapy treatment recommendations were associated with significantly improved survival for DeepSurv (HR: 0.57; 95% CI: 0.4-0.82, P = .002) and RSF (HR: 0.66; 95% CI: 0.46-0.94, P = .020). The ML survival model has the potential to benefit prognostic evaluation and treatment of HCC. This novel analytical approach could provide reliable information on individual survival and treatment recommendations.
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Affiliation(s)
- Xianguo Li
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haijun Bao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yongping Shi
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhong Zhu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zuojie Peng
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lizhao Yan
- Department of Hand Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinhuang Chen
- Department of Emergency Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaogang Shu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP. Sci Rep 2023; 13:8984. [PMID: 37268685 DOI: 10.1038/s41598-023-35795-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/24/2023] [Indexed: 06/04/2023] Open
Abstract
Nasopharyngeal cancer (NPC) has a unique histopathology compared with other head and neck cancers. Individual NPC patients may attain different outcomes. This study aims to build a prognostic system by combining a highly accurate machine learning model (ML) model with explainable artificial intelligence to stratify NPC patients into low and high chance of survival groups. Explainability is provided using Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) techniques. A total of 1094 NPC patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database for model training and internal validation. We combined five different ML algorithms to form a uniquely stacked algorithm. The predictive performance of the stacked algorithm was compared with a state-of-the-art algorithm-extreme gradient boosting (XGBoost) to stratify the NPC patients into chance of survival groups. We validated our model with temporal validation (n = 547) and geographic external validation (Helsinki University Hospital NPC cohort, n = 60). The developed stacked predictive ML model showed an accuracy of 85.9% while the XGBoost had 84.5% after the training and testing phases. This demonstrated that both XGBoost and the stacked model showed comparable performance. External geographic validation of XGBoost model showed a c-index of 0.74, accuracy of 76.7%, and area under curve of 0.76. The SHAP technique revealed that age of the patient at diagnosis, T-stage, ethnicity, M-stage, marital status, and grade were among the prominent input variables in decreasing order of significance for the overall survival of NPC patients. LIME showed the degree of reliability of the prediction made by the model. In addition, both techniques showed how each feature contributed to the prediction made by the model. LIME and SHAP techniques provided personalized protective and risk factors for each NPC patient and unraveled some novel non-linear relationships between input features and survival chance. The examined ML approach showed the ability to predict the chance of overall survival of NPC patients. This is important for effective treatment planning care and informed clinical decisions. To enhance outcome results, including survival in NPC, ML may aid in planning individualized therapy for this patient population.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki, Helsinki, Finland
- Faculty of Dentistry, Misurata University, Misurata, Libya
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Jiang C, Wang K, Yan L, Yao H, Shi H, Lin R. Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database. Cancer Med 2023; 12:12413-12424. [PMID: 37165971 PMCID: PMC10278508 DOI: 10.1002/cam4.5949] [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: 01/20/2023] [Revised: 03/18/2023] [Accepted: 04/02/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND The study aims to evaluate the performance of three advanced machine learning algorithms and a traditional Cox proportional hazard (CoxPH) model in predicting the overall survival (OS) of patients with pancreatic neuroendocrine neoplasms (PNENs). METHOD The clinicopathological dataset obtained from the Surveillance, Epidemiology, and End Results database was randomly assigned to the training set and testing set at a ratio of 7:3. The concordance index (C-index) and integrated Brier score (IBS) were used to compare the predictive performance of the models. The accuracy of the model in predicting the 5-year and 10-year survival rates was compared using the receiver operating characteristic curve, decision curve analysis (DCA) and calibration curve. RESULTS This study included 3239 patients with PNENs in total. The DeepSurv model had the highest C-index of 0.7882 in the testing set and training set and the lowest IBS of 0.1278 in the testing set compared with the CoxPH, neural multitask logistic and random survival forest models (C-index = 0.7501, 0.7616, and 0.7612, respectively; IBS = 0.1397, 0.1418, and 0.1432, respectively). Moreover, the DeepSurv model had the highest accuracy in predicting 5- and 10-year OS rates (area under the curve: 0.87 and 0.90). DCA showed that the DeepSurv model had high potential for clinical decisions in 5- and 10-year OS models. Finally, we developed an online application based on the DeepSurv model for clinical use (https://whuh-ml-neuroendocrinetumor-app-predict-oyw5km.streamlit.app/). CONCLUSIONS All four models analyzed above can predict the prognosis of PNENs well, among which the DeepSurv model has the best prediction performance.
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Affiliation(s)
- Chen Jiang
- Department of Gastroenterology, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Kan Wang
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Lizhao Yan
- Department of Hand Surgery, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Hailing Yao
- Department of Gastroenterology, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Huiying Shi
- Department of Gastroenterology, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Rong Lin
- Department of Gastroenterology, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
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Zhang Q, Wu G, Yang Q, Dai G, Li T, Chen P, Li J, Huang W. Survival rate prediction of nasopharyngeal carcinoma patients based on MRI and gene expression using a deep neural network. Cancer Sci 2022; 114:1596-1605. [PMID: 36541519 PMCID: PMC10067413 DOI: 10.1111/cas.15704] [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: 07/13/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 12/24/2022] Open
Abstract
To achieve a better treatment regimen and follow-up assessment design for intensity-modulated radiotherapy (IMRT)-treated nasopharyngeal carcinoma (NPC) patients, an accurate progression-free survival (PFS) time prediction algorithm is needed. We propose developing a PFS prediction model of NPC patients after IMRT treatment using a deep learning method and comparing that with the traditional texture analysis method. One hundred and fifty-one NPC patients were included in this retrospective study. T1-weighted, proton density and dynamic contrast-enhanced magnetic resonance (MR) images were acquired. The expression level of five genes (HIF-1α, EGFR, PTEN, Ki-67, and VEGF) and infection of Epstein-Barr (EB) virus were tested. A residual network was trained to predict PFS from MR images. The output as well as patient characteristics were combined using a linear regression model to provide a final PFS prediction. The prediction accuracy was compared with that of the traditional texture analysis method. A regression model combining the deep learning output with HIF-1α expression and Epstein-Barr infection provides the best PFS prediction accuracy (Spearman correlation R2 = 0.53; Harrell's C-index = 0.82; receiver operative curve [ROC] analysis area under the curve [AUC] = 0.88; log-rank test hazard ratio [HR] = 8.45), higher than a regression model combining texture analysis with HIF-1α expression (Spearman correlation R2 = 0.14; Harrell's C-index =0.68; ROC analysis AUC = 0.76; log-rank test HR = 2.85). The deep learning method does not require a manually drawn tumor region of interest. MR image processing using deep learning combined with patient characteristics can provide accurate PFS prediction for nasopharyngeal carcinoma patients and does not rely on specific kernels or tumor regions of interest, which is needed for the texture analysis method.
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Affiliation(s)
- Qihao Zhang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital, Hainan, China
| | - Qianyu Yang
- Department of Radiology, Hainan General Hospital, Hainan, China
| | - Ganmian Dai
- Department of Radiology, Hainan General Hospital, Hainan, China
| | - Tiansheng Li
- Department of Radiology, Hainan General Hospital, Hainan, China
| | - Pianpian Chen
- Department of Pathology, Hainan General Hospital, Hainan, China
| | - Jiao Li
- Department of Pathology, Hainan General Hospital, Hainan, China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital, Hainan, China
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Lee SW, Yang CC, Lai HY, Tsai HH, Yeh CF, Kuo YH, Kang NW, Chen TJ, Chang SL. Roundabout Guidance Receptor 1 Is an Emerging Prognostic Biomarker for Nasopharyngeal Carcinoma. CLINICAL MEDICINE INSIGHTS: ONCOLOGY 2022; 16:11795549221113244. [PMID: 35898392 PMCID: PMC9310334 DOI: 10.1177/11795549221113244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/21/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Nasopharyngeal carcinoma (NPC) is a malignant tumor originating from the nasopharynx with high morbidity and mortality in Southeast Asia and south of China. Roundabout guidance receptor 1 (ROBO1) can regulate axonogenesis (axon-like protrusion), which may play an important role in migration. However, the roles of ROBO1 in NPC have not been clarified. Methods: A comparative analysis employing the NPC transcriptome (GSE12452) and the axonogenesis-related genes (GO: 0050772) was performed. In total, 124 tissue blocks from patients primarily diagnosed as NPC (1993-2002) were examined using immunohistochemical staining. The connections between clinicopathological variables and protein immunoexpression were analyzed by Pearson’s chi-square test. The Kaplan–Meier method with a log-rank test was employed to plot survival curves. Multivariate analysis was performed using the Cox proportional hazards model to identify independent prognostic biomarker. Results: According to transcriptome analysis, we found that ROBO1 is significantly highly expressed in NPC tissues compared with normal tissues. The immunohistochemistry (IHC) staining showed that high expression of ROBO1 was significantly related to primary tumor (T1T2 and T3T4) ( P = .024), nodal metastasis status (N0N1 and N2N3) ( P = .030), stage (I-II and III-IV) ( P = .019), and histological grade (keratinizing, non-keratinizing, and undifferentiated) ( P = .065). Importantly, NPC patients with high ROBO1 expression had poorer disease-specific survival (DSS) ( P = .0001), distal metastasis-free survival (DMeFS) ( P < .0001), and local recurrence-free survival (LRFS) ( P = .0001) compared with NPC patients with low ROBO1 expression through the uni-/multivariate and the Kaplan–Meier survival analyses. Conclusion: Our report indicates that ROBO1 might be a potential prognostic biomarker for NPC.
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Affiliation(s)
- Sung-Wei Lee
- Department of Radiation Oncology, Chi Mei Medical Center, Liouying
| | - Ching-Chieh Yang
- Department of Radiation Oncology, Chi Mei Medical Center, Tainan
- Department of Pharmacy, Chia-Nan University of Pharmacy and Science, Tainan
| | - Hong-Yue Lai
- Department of Medical Research, Chi Mei Medical Center, Tainan
- Trans-Omic Laboratory for Precision Medicine, Chi Mei Medical Center, Tainan
| | - Hsin-Hwa Tsai
- Department of Medical Research, Chi Mei Medical Center, Tainan
- Trans-Omic Laboratory for Precision Medicine, Chi Mei Medical Center, Tainan
| | - Cheng-Fa Yeh
- Department of Internal Medicine, Chi Mei Medical Center, Tainan
| | - Yu-Hsuan Kuo
- Division of Hematology and Oncology, Department of Internal Medicine, Chi-Mei Medical Center, Tainan
- College of Pharmacy and Science, Chia Nan University, Tainan
| | - Nai-Wen Kang
- Division of Hematology and Oncology, Department of Internal Medicine, Chi-Mei Medical Center, Tainan
| | - Tzu-Ju Chen
- Department of Pet care and grooming, Chung Hwa University of Medical Technology, Tainan
- Department of Clinical Pathology, Chi-Mei Medical Center, Tainan
- Institute of Biomedical Science, National Sun Yat-Sen University, Kaohsiung
| | - Shih-Lun Chang
- Department of Pet care and grooming, Chung Hwa University of Medical Technology, Tainan
- Department of Otolaryngology, Chi Mei Medical Center, Tainan
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