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Oliver J, Alapati R, Lee J, Bur A. Artificial Intelligence in Head and Neck Surgery. Otolaryngol Clin North Am 2024:S0030-6665(24)00070-7. [PMID: 38910064 DOI: 10.1016/j.otc.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's potential in pattern recognition for early cancer detection, prognostication, and treatment planning, primarily through image analysis using clinical, endoscopic, and histopathologic images. Radiomics is also discussed at length, as well as the many ways that radiologic image analysis can be utilized, including for diagnosis, lymph node metastasis prediction, and evaluation of treatment response. The study highlights AI's promise and limitations, underlining the need for clinician-data scientist collaboration to enhance head and neck cancer care.
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Affiliation(s)
- Jamie Oliver
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Rahul Alapati
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Jason Lee
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Andrés Bur
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
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Wang CK, Wang TW, Lu CF, Wu YT, Hua MW. Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis. Diagnostics (Basel) 2024; 14:924. [PMID: 38732337 PMCID: PMC11082984 DOI: 10.3390/diagnostics14090924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/12/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.
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Affiliation(s)
- Chih-Keng Wang
- School of Medicine, College of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Ting-Wei Wang
- School of Medicine, College of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan
| | - Chia-Fung Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan;
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan
| | - Man-Wei Hua
- Department of Otolaryngology-Head and Neck Surgery, Taichung Veterans General Hospital, Taichung 407219, Taiwan
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Ren CX, Xu GX, Dai DQ, Lin L, Sun Y, Liu QS. Cross-site prognosis prediction for nasopharyngeal carcinoma from incomplete multi-modal data. Med Image Anal 2024; 93:103103. [PMID: 38368752 DOI: 10.1016/j.media.2024.103103] [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: 08/31/2022] [Revised: 12/05/2023] [Accepted: 02/05/2024] [Indexed: 02/20/2024]
Abstract
Accurate prognosis prediction for nasopharyngeal carcinoma based on magnetic resonance (MR) images assists in the guidance of treatment intensity, thus reducing the risk of recurrence and death. To reduce repeated labor and sufficiently explore domain knowledge, aggregating labeled/annotated data from external sites enables us to train an intelligent model for a clinical site with unlabeled data. However, this task suffers from the challenges of incomplete multi-modal examination data fusion and image data heterogeneity among sites. This paper proposes a cross-site survival analysis method for prognosis prediction of nasopharyngeal carcinoma from domain adaptation viewpoint. Utilizing a Cox model as the basic framework, our method equips it with a cross-attention based multi-modal fusion regularization. This regularization model effectively fuses the multi-modal information from multi-parametric MR images and clinical features onto a domain-adaptive space, despite the absence of some modalities. To enhance the feature discrimination, we also extend the contrastive learning technique to censored data cases. Compared with the conventional approaches which directly deploy a trained survival model in a new site, our method achieves superior prognosis prediction performance in cross-site validation experiments. These results highlight the key role of cross-site adaptability of our method and support its value in clinical practice.
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Affiliation(s)
- Chuan-Xian Ren
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China.
| | - Geng-Xin Xu
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Dao-Qing Dai
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Li Lin
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Ying Sun
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Qing-Shan Liu
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Li H, Huang W, Wang S, Balasubramanian PS, Wu G, Fang M, Xie X, Zhang J, Dong D, Tian J, Chen F. Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma. Vis Comput Ind Biomed Art 2023; 6:23. [PMID: 38036750 PMCID: PMC10689317 DOI: 10.1186/s42492-023-00149-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/27/2023] [Indexed: 12/02/2023] Open
Abstract
Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model's ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.
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Affiliation(s)
- Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, 570311, China
| | - Siwen Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | | | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, 570311, China
| | - Mengjie Fang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Xuebin Xie
- Department of Radiology, Kiang Wu Hospital, Santo António, Macao, 999078, China
| | - Jie Zhang
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Zhuhai, Guangdong, 519000, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China.
- Zhuhai Precision Medical Center, Zhuhai People's Hospital, Zhuhai, Guangdong, 519000, China.
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, 570311, China.
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Liu Y, Sun S, Zhang Y, Huang X, Wang K, Qu Y, Chen X, Wu R, Zhang J, Luo J, Li Y, Wang J, Yi J. Predictive function of tumor burden-incorporated machine-learning algorithms for overall survival and their value in guiding management decisions in patients with locally advanced nasopharyngeal carcinoma. JOURNAL OF THE NATIONAL CANCER CENTER 2023; 3:295-305. [PMID: 39036668 PMCID: PMC11256522 DOI: 10.1016/j.jncc.2023.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/09/2023] [Accepted: 10/09/2023] [Indexed: 07/23/2024] Open
Abstract
Objective Accurate prognostic predictions and personalized decision-making on induction chemotherapy (IC) for individuals with locally advanced nasopharyngeal carcinoma (LA-NPC) remain challenging. This research examined the predictive function of tumor burden-incorporated machine-learning algorithms for overall survival (OS) and their value in guiding treatment in patients with LA-NPC. Methods Individuals with LA-NPC were reviewed retrospectively. Tumor burden signature-based OS prediction models were established using a nomogram and two machine-learning methods, the interpretable eXtreme Gradient Boosting (XGBoost) risk prediction model, and DeepHit time-to-event neural network. The models' prediction performances were compared using the concordance index (C-index) and the area under the curve (AUC). The patients were divided into two cohorts based on the risk predictions of the most successful model. The efficacy of IC combined with concurrent chemoradiotherapy was compared to that of chemoradiotherapy alone. Results The 1 221 eligible individuals, assigned to the training (n = 813) or validation (n = 408) set, showed significant respective differences in the C-indices of the XGBoost, DeepHit, and nomogram models (0.849 and 0.768, 0.811 and 0.767, 0.730 and 0.705). The training and validation sets had larger AUCs in the XGBoost and DeepHit models than the nomogram model in predicting OS (0.881 and 0.760, 0.845 and 0.776, and 0.764 and 0.729, P < 0.001). IC presented survival benefits in the XGBoost-derived high-risk but not low-risk group. Conclusion This research used machine-learning algorithms to create and verify a comprehensive model integrating tumor burden with clinical variables to predict OS and determine which patients will most likely gain from IC. This model could be valuable for delivering patient counseling and conducting clinical evaluations.
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Affiliation(s)
- Yang Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Shiran Sun
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ye Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xiaodong Huang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Kai Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yuan Qu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xuesong Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Runye Wu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jianghu Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jingwei Luo
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yexiong Li
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jingbo Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Junlin Yi
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences (CAMS), Langfang 065001, China
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Li S, Zhang W, Liang B, Huang W, Luo C, Zhu Y, Kou KI, Ruan G, Liu L, Zhang G, Li H. A Rulefit-based prognostic analysis using structured MRI report to select potential beneficiaries from induction chemotherapy in advanced nasopharyngeal carcinoma: A dual-centre study. Radiother Oncol 2023; 189:109943. [PMID: 37813309 DOI: 10.1016/j.radonc.2023.109943] [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: 05/13/2023] [Revised: 09/12/2023] [Accepted: 10/04/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND AND PURPOSE Structured MRI report facilitate prognostic prediction for nasopharyngeal carcinoma (NPC). However, the intrinsic association among structured variables is not fully utilised. This study aimed to investigate the performance of a Rulefit-based model in feature integration behind structured MRI report and prognostic prediction in advanced NPC. MATERIALS AND METHODS We retrospectively enrolled 1207 patients diagnosed with non-metastatic advanced NPC from two centres, and divided into training (N = 544), internal testing (N = 367), and external testing (N = 296) cohorts. Machine learning algorithms including multivariate analysis, deep learning, Lasso, and Rulefit were used to establish corresponding prognostic models. The concordance indices (C- indices) of three clinical and six combined models with different algorithms for overall survival (OS) prediction were compared. Survival benefits of induction chemotherapy (IC) were calculated among risk groups stratified by different models. A website was established for individualised survival visualisation. RESULTS Incorporating structured variables into Stage model significantly improved the prognostic prediction performance. Six prognostic rules with structured variables were identified by Rulefit. OS prediction of Rules model was comparable to Lasso model in internal testing cohort (C-index: 0.720 vs. 0.713, P = 0.100) and achieved the highest C-index of 0.711 in external testing cohort, indicating better generalisability. The Rules model stratified patients into risk groups with significant 5-year OS differences in each cohort, and revealed significant survival benefits from additional IC in high-risk group. CONCLUSION The Rulefit-based Rules model, with the revelation of intrinsic associations behind structured variables, is promising in risk stratification and guiding individualised IC treatment for advanced NPC.
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Affiliation(s)
- Shuqi Li
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, Guangdong 510060, China
| | - Weijing Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, Guangdong 510060, China
| | - Baodan Liang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, Guangdong 510060, China
| | - Wenjie Huang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, Guangdong 510060, China
| | - Chao Luo
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, Guangdong 510060, China
| | - Yuliang Zhu
- Nasopharyngeal Head-and-Neck Tumor Radiotherapy Department, Zhongshan City People's Hospital, China
| | - Kit Ian Kou
- Department of Mathematics, Faculty of Science and Technology, University of Macau, China
| | - Guangying Ruan
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, Guangdong 510060, China
| | - Lizhi Liu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, Guangdong 510060, China
| | - Guoyi Zhang
- Cancer center, the First People's Hospital of Foshan, Foshan 528000, Guangdong, China.
| | - Haojiang Li
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng Road East, Guangzhou, Guangdong 510060, China.
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Yang X, Wu J, Chen X. Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma. J Clin Med 2023; 12:jcm12093077. [PMID: 37176518 PMCID: PMC10178972 DOI: 10.3390/jcm12093077] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023] Open
Abstract
Artificial intelligence (AI) is an interdisciplinary field that encompasses a wide range of computer science disciplines, including image recognition, machine learning, human-computer interaction, robotics and so on. Recently, AI, especially deep learning algorithms, has shown excellent performance in the field of image recognition, being able to automatically perform quantitative evaluation of complex medical image features to improve diagnostic accuracy and efficiency. AI has a wider and deeper application in the medical field of diagnosis, treatment and prognosis. Nasopharyngeal carcinoma (NPC) occurs frequently in southern China and Southeast Asian countries and is the most common head and neck cancer in the region. Detecting and treating NPC early is crucial for a good prognosis. This paper describes the basic concepts of AI, including traditional machine learning and deep learning algorithms, and their clinical applications of detecting and assessing NPC lesions, facilitating treatment and predicting prognosis. The main limitations of current AI technologies are briefly described, including interpretability issues, privacy and security and the need for large amounts of annotated data. Finally, we discuss the remaining challenges and the promising future of using AI to diagnose and treat NPC.
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Affiliation(s)
- Xinggang Yang
- Division of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
| | - Juan Wu
- Out-Patient Department, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
| | - Xiyang Chen
- Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
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Li S, Wan X, Deng YQ, Hua HL, Li SL, Chen XX, Zeng ML, Zha Y, Tao ZZ. Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued. Cancer Imaging 2023; 23:14. [PMID: 36759889 PMCID: PMC9912633 DOI: 10.1186/s40644-023-00530-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 02/01/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC. METHODS A total of 381 NPC patients who were divided into high- and low-risk groups according to progression-free survival were retrospectively included. Deeplab v3 and U-Net were trained to build segmentation models for the automatic segmentation of the tumor and suspicious lymph nodes. Five datasets were constructed by expanding 5, 10, 20, 40, and 60 pixels outward from the edge of the automatically segmented region. Inception-Resnet-V2, ECA-ResNet50t, EfficientNet-B3, and EfficientNet-B0 were trained with the original, segmented, and the five new constructed datasets to establish the classification models. The receiver operating characteristic curve was used to evaluate the performance of each model. RESULTS The Dice coefficients of Deeplab v3 and U-Net were 0.741(95%CI:0.722-0.760) and 0.737(95%CI:0.720-0.754), respectively. The average areas under the curve (aAUCs) of deep learning models for classification trained with the original and segmented images and with images expanded by 5, 10, 20, 40, and 60 pixels were 0.717 ± 0.043, 0.739 ± 0.016, 0.760 ± 0.010, 0.768 ± 0.018, 0.802 ± 0.013, 0.782 ± 0.039, and 0.753 ± 0.014, respectively. The models trained with the images expanded by 20 pixels obtained the best performance. CONCLUSIONS The peritumoral region NPC contains information related to prognosis, and the incorporation of this region could improve the performance of deep learning models for prognosis prediction.
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Affiliation(s)
- Song Li
- grid.89957.3a0000 0000 9255 8984Department of Otorhinolaryngology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029 China ,grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Xia Wan
- grid.510937.9Department of Otolaryngology-Head & Neck Surgery, Ezhou Central Hospital, No. 9 Wenxing Road, Ezhou, 436000 P.R. China
| | - Yu-Qin Deng
- grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Hong-Li Hua
- grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Sheng-Lan Li
- grid.412632.00000 0004 1758 2270Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Xi-Xiang Chen
- grid.412632.00000 0004 1758 2270Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Man-Li Zeng
- grid.510937.9Department of Otolaryngology-Head & Neck Surgery, Ezhou Central Hospital, No. 9 Wenxing Road, Ezhou, 436000 P.R. China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei, 430060, P.R. China.
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei, 430060, P.R. China.
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Chen H, Li H, Yang S, Huang W, Gong Q, Ruan G, Chen S, Liu L. Prognostic potential of a voxelwise invasion risk map of nasopharyngeal carcinoma based on a coordinate system of the nasopharynx. Quant Imaging Med Surg 2023; 13:982-998. [PMID: 36819252 PMCID: PMC9929427 DOI: 10.21037/qims-22-744] [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: 07/16/2022] [Accepted: 12/08/2022] [Indexed: 01/09/2023]
Abstract
Background Tumor invasion risk (TIR) is an important prognostic factor in nasopharyngeal carcinoma (NPC). We propose a novel prognostic analytic method for NPC based on a voxelwise analysis of TIR in a coordinate system of the nasopharynx. Methods A stable nasopharynx coordinate system was constructed based on anatomical landmarks to obtain an accurate TIR profile for NPC. The coordinate system was validated by image registration of the lateral pterygoid muscle (LPM). The tumors were registered to the coordinate system through shift, scale, and rotation transformations. The voxelwise TIR map for NPC was obtained by superposition of all registered and mirrored tumor regions of interest. The minimum risk (MinR) point of the tumor region was used as an independent prognostic factor for NPC. The cutoff value was calculated with density plot and validated with restricted cubic splines (RCSs), and then the patients were divided into 2 groups for overall survival (OS) analysis. Results The first voxelwise TIR map of NPC was obtained based on 778 patients. The OS of patients with a low TIR was 76.8% and was 92.6% for patients with a high TIR [P<0.001; hazard ratio (HR) =1/0.45; 95% CI: 0.27-0.77; adjusted P=0.004]. Thus, patients with a low TIR had a poor prognosis, whereas patients with a high TIR had a good prognosis. The MinR may be better at grading the prognosis of patients compared to the American Joint Committee on Cancer (AJCC) staging or tumor/node (T/N) classification systems. Conclusions The voxelwise TIR map provides a new method for the prognostic analysis of NPC. Potential clinical applications of voxelwise TIR mapping are clinical target volume (CTV) delineation and dose-painting for NPC.
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Affiliation(s)
- Hongbo Chen
- School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, China
| | - Haojiang Li
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shixin Yang
- School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, China
| | - Wenjie Huang
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qiong Gong
- School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, China
| | - Guangying Ruan
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shuchao Chen
- School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, China
| | - Lizhi Liu
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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10
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Real AD, Real OD, Sardina S, Oyonarte R. Use of automated artificial intelligence to predict the need for orthodontic extractions. Korean J Orthod 2022; 52:102-111. [PMID: 35321949 PMCID: PMC8964473 DOI: 10.4041/kjod.2022.52.2.102] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 09/29/2021] [Accepted: 10/13/2021] [Indexed: 11/10/2022] Open
Abstract
Objective To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.
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Affiliation(s)
- Alberto Del Real
- Graduate Orthodontic Program, Discipline of Orthodontics, Faculty of Odontology, Universidad de los Andes, Santiago, Chile.,Private Practice, Santiago, Chile
| | - Octavio Del Real
- Graduate Orthodontic Program, Discipline of Orthodontics, Faculty of Odontology, Universidad de los Andes, Santiago, Chile.,Private Practice, Santiago, Chile
| | - Sebastian Sardina
- Department of Computer Science, School of Computing Technologies, RMIT University, Melbourne, Australia
| | - Rodrigo Oyonarte
- Graduate Orthodontic Program, Discipline of Orthodontics, Faculty of Odontology, Universidad de los Andes, Santiago, Chile.,Private Practice, Santiago, Chile
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11
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Duan W, Xiong B, Tian T, Zou X, He Z, Zhang L. Radiomics in Nasopharyngeal Carcinoma. CLINICAL MEDICINE INSIGHTS: ONCOLOGY 2022; 16:11795549221079186. [PMID: 35237090 PMCID: PMC8883403 DOI: 10.1177/11795549221079186] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 01/20/2022] [Indexed: 12/24/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common head and neck malignancies, and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy alone, concurrent chemoradiotherapy, and induction chemotherapy combined with concurrent chemoradiotherapy can be used according to different grades. Treatment options and prognoses vary greatly depending on the grade of disease in the patients. Accurate grading and risk assessment are required. Recently, radiomics has combined a large amount of invisible high-dimensional information extracted from computed tomography, magnetic resonance imaging, or positron emission tomography with powerful computing capabilities of machine-learning algorithms, providing the possibility to achieve an accurate diagnosis and individualized treatment for cancer patients. As an effective tumor biomarker of NPC, the radiomic signature has been widely used in grading, differential diagnosis, prediction of prognosis, evaluation of treatment response, and early identification of therapeutic complications. The process of radiomic research includes image segmentation, feature extraction, feature selection, model establishment, and evaluation. Many open-source or commercial tools can be used to achieve these procedures. The development of machine-learning algorithms provides more possibilities for radiomics research. This review aimed to summarize the application of radiomics in NPC and introduce the basic process of radiomics research.
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Affiliation(s)
- Wenyue Duan
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Bingdi Xiong
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Ting Tian
- Department of Clinical Medicine, Chengdu Medical College, Chengdu, People's Republic of China
| | - Xinyun Zou
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Zhennan He
- Department of Clinical Medicine, Chengdu Medical College, Chengdu, People's Republic of China
| | - Ling Zhang
- Department of Oncology, People's Liberation Army The General Hospital of Western Theater Command, Chengdu, People's Republic of China
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12
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Li S, Gong Q, Li H, Chen S, Liu Y, Ruan G, Zhu L, Liu L, Chen H. Automatic location scheme of anatomical landmarks in 3D head MRI based on the scale attention hourglass network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106564. [PMID: 34894558 DOI: 10.1016/j.cmpb.2021.106564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 11/04/2021] [Accepted: 11/27/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE An anatomical landmark is biologically meaningful point in medical images and often used for medical image registration. The purpose of this study is to automatically locate anatomical landmarks from 3D medical images. METHODS A two-step automatic location scheme of anatomical landmarks in 3D medical image was designed in this study. In the first step, the full convolutional neural network was used for slice detection from a 3D medical image. In the second step, the scale attention hourglass network was used for landmark location in the detected slice and could overcome the difficulty of similar anatomical structures and different image parameters. This method was implemented and tested on four stable anatomical landmarks in 3D head MRI. RESULTS A total of 500 and 300 3D head volumes were used for training and testing, respectively. Results showed that the slice detection accuracy reached 85.7% and that the maximum location error was less than one slice. The average accuracy of the four anatomical landmarks in the detected slice reached 87.2%, and the spatial distance was 2.4 ± 2.4, which obtained better performance compared with hourglass network and feature pyramid networks. CONCLUSIONS This method can be useful for locating anatomical landmarks in 3D head MRI and provides technical support for medical image registration and big data analysis.
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Affiliation(s)
- Sai Li
- School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin 541004, China
| | - Qiong Gong
- School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin 541004, China
| | - Haojiang Li
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Shuchao Chen
- School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin 541004, China
| | - Yifei Liu
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Guangying Ruan
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Lin Zhu
- School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin 541004, China
| | - Lizhi Liu
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
| | - Hongbo Chen
- School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin 541004, China.
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13
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Ng WT, But B, Choi HCW, de Bree R, Lee AWM, Lee VHF, López F, Mäkitie AA, Rodrigo JP, Saba NF, Tsang RKY, Ferlito A. Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management - A Systematic Review. Cancer Manag Res 2022; 14:339-366. [PMID: 35115832 PMCID: PMC8801370 DOI: 10.2147/cmar.s341583] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/25/2021] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Nasopharyngeal carcinoma (NPC) is endemic to Eastern and South-Eastern Asia, and, in 2020, 77% of global cases were diagnosed in these regions. Apart from its distinct epidemiology, the natural behavior, treatment, and prognosis are different from other head and neck cancers. With the growing trend of artificial intelligence (AI), especially deep learning (DL), in head and neck cancer care, we sought to explore the unique clinical application and implementation direction of AI in the management of NPC. METHODS The search protocol was performed to collect publications using AI, machine learning (ML) and DL in NPC management from PubMed, Scopus and Embase. The articles were filtered using inclusion and exclusion criteria, and the quality of the papers was assessed. Data were extracted from the finalized articles. RESULTS A total of 78 articles were reviewed after removing duplicates and papers that did not meet the inclusion and exclusion criteria. After quality assessment, 60 papers were included in the current study. There were four main types of applications, which were auto-contouring, diagnosis, prognosis, and miscellaneous applications (especially on radiotherapy planning). The different forms of convolutional neural networks (CNNs) accounted for the majority of DL algorithms used, while the artificial neural network (ANN) was the most frequent ML model implemented. CONCLUSION There is an overall positive impact identified from AI implementation in the management of NPC. With improving AI algorithms, we envisage AI will be available as a routine application in a clinical setting soon.
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Affiliation(s)
- Wai Tong Ng
- Clinical Oncology Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, People’s Republic of China
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Barton But
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Horace C W Choi
- Department of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Remco de Bree
- Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Anne W M Lee
- Clinical Oncology Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, People’s Republic of China
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Victor H F Lee
- Clinical Oncology Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, People’s Republic of China
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Fernando López
- Department of Otolaryngology, Hospital Universitario Central de Asturias (HUCA), Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), University of Oviedo, Oviedo, 33011, Spain
- Spanish Biomedical Research Network Centre in Oncology, CIBERONC, Madrid, 28029, Spain
| | - Antti A Mäkitie
- Department of Otorhinolaryngology - Head and Neck Surgery, HUS Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Juan P Rodrigo
- Department of Otolaryngology, Hospital Universitario Central de Asturias (HUCA), Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), University of Oviedo, Oviedo, 33011, Spain
- Spanish Biomedical Research Network Centre in Oncology, CIBERONC, Madrid, 28029, Spain
| | - Nabil F Saba
- Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA, USA
| | - Raymond K Y Tsang
- Division of Otorhinolaryngology, Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Alfio Ferlito
- Coordinator of the International Head and Neck Scientific Group, Padua, Italy
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14
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Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021; 11:1523. [PMID: 34573865 PMCID: PMC8465998 DOI: 10.3390/diagnostics11091523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/10/2021] [Accepted: 08/19/2021] [Indexed: 12/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.
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Affiliation(s)
- Song Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Yu-Qin Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Zhi-Ling Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Hong-Li Hua
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
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15
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Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method. Neural Comput Appl 2021; 35:13037-13046. [PMID: 33424133 PMCID: PMC7783503 DOI: 10.1007/s00521-020-05592-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 12/11/2020] [Indexed: 01/08/2023]
Abstract
To predict the mortality of patients with coronavirus disease 2019 (COVID-19). We collected clinical data of COVID-19 patients between January 18 and March 29 2020 in Wuhan, China . Gradient boosting decision tree (GBDT), logistic regression (LR) model, and simplified LR were built to predict the mortality of COVID-19. We also evaluated different models by computing area under curve (AUC), accuracy, positive predictive value (PPV), and negative predictive value (NPV) under fivefold cross-validation. A total of 2924 patients were included in our evaluation, with 257 (8.8%) died and 2667 (91.2%) survived during hospitalization. Upon admission, there were 21 (0.7%) mild cases, 2051 (70.1%) moderate case, 779 (26.6%) severe cases, and 73 (2.5%) critically severe cases. The GBDT model exhibited the highest fivefold AUC, which was 0.941, followed by LR (0.928) and LR-5 (0.913). The diagnostic accuracies of GBDT, LR, and LR-5 were 0.889, 0.868, and 0.887, respectively. In particular, the GBDT model demonstrated the highest sensitivity (0.899) and specificity (0.889). The NPV of all three models exceeded 97%, while their PPV values were relatively low, resulting in 0.381 for LR, 0.402 for LR-5, and 0.432 for GBDT. Regarding severe and critically severe cases, the GBDT model also performed the best with a fivefold AUC of 0.918. In the external validation test of the LR-5 model using 72 cases of COVID-19 from Brunei, leukomonocyte (%) turned to show the highest fivefold AUC (0.917), followed by urea (0.867), age (0.826), and SPO2 (0.704). The findings confirm that the mortality prediction performance of the GBDT is better than the LR models in confirmed cases of COVID-19. The performance comparison seems independent of disease severity.
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