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Alter IL, Chan K, Lechien J, Rameau A. An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review. Eur Arch Otorhinolaryngol 2024; 281:2723-2731. [PMID: 38393353 DOI: 10.1007/s00405-024-08512-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 01/25/2024] [Indexed: 02/25/2024]
Abstract
PURPOSE Despite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these tools. METHODS Recent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar. RESULTS Significant recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in explainability. CONCLUSIONS Potential implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is substantial. LEVEL OF EVIDENCE: 4
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Affiliation(s)
- Isaac L Alter
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA
| | - Karly Chan
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA
| | - Jérome Lechien
- Department of Otorhinolaryngology, Head and Neck Surgery, Hôpital Foch, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France
- Department of Human Anatomy and Experimental Oncology, Faculty of Medicine, UMONS Research Institute for Health and Sciences Technology, University of Mons (UMons), Mons, Belgium
| | - Anaïs Rameau
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA.
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Wibawa MS, Zhou JY, Wang R, Huang YY, Zhan Z, Chen X, Lv X, Young LS, Rajpoot N. AI-Based Risk Score from Tumour-Infiltrating Lymphocyte Predicts Locoregional-Free Survival in Nasopharyngeal Carcinoma. Cancers (Basel) 2023; 15:5789. [PMID: 38136336 PMCID: PMC10742296 DOI: 10.3390/cancers15245789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 11/28/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND Locoregional recurrence of nasopharyngeal carcinoma (NPC) occurs in 10% to 50% of cases following primary treatment. However, the current main prognostic markers for NPC, both stage and plasma Epstein-Barr virus DNA, are not sensitive to locoregional recurrence. METHODS We gathered 385 whole-slide images (WSIs) from haematoxylin and eosin (H&E)-stained NPC sections (n = 367 cases), which were collected from Sun Yat-sen University Cancer Centre. We developed a deep learning algorithm to detect tumour nuclei and lymphocyte nuclei in WSIs, followed by density-based clustering to quantify the tumour-infiltrating lymphocytes (TILs) into 12 scores. The Random Survival Forest model was then trained on the TILs to generate risk score. RESULTS Based on Kaplan-Meier analysis, the proposed methods were able to stratify low- and high-risk NPC cases in a validation set of locoregional recurrence with a statically significant result (p < 0.001). This finding was also found in distant metastasis-free survival (p < 0.001), progression-free survival (p < 0.001), and regional recurrence-free survival (p < 0.05). Furthermore, in both univariate analysis (HR: 1.58, CI: 1.13-2.19, p < 0.05) and multivariate analysis (HR:1.59, CI: 1.11-2.28, p < 0.05), we also found that our methods demonstrated a strong prognostic value for locoregional recurrence. CONCLUSION The proposed novel digital markers could potentially be utilised to assist treatment decisions in cases of NPC.
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Affiliation(s)
- Made Satria Wibawa
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; (M.S.W.); (R.W.)
| | - Jia-Yu Zhou
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; (J.-Y.Z.); (Y.-Y.H.); (Z.Z.); (X.C.); (X.L.)
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Ruoyu Wang
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; (M.S.W.); (R.W.)
| | - Ying-Ying Huang
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; (J.-Y.Z.); (Y.-Y.H.); (Z.Z.); (X.C.); (X.L.)
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Zejiang Zhan
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; (J.-Y.Z.); (Y.-Y.H.); (Z.Z.); (X.C.); (X.L.)
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Xi Chen
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; (J.-Y.Z.); (Y.-Y.H.); (Z.Z.); (X.C.); (X.L.)
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Xing Lv
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; (J.-Y.Z.); (Y.-Y.H.); (Z.Z.); (X.C.); (X.L.)
- Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Lawrence S. Young
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK;
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; (M.S.W.); (R.W.)
- The Alan Turing Institute, London NW1 2DB, UK
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Mäkitie AA, Alabi RO, Ng SP, Takes RP, Robbins KT, Ronen O, Shaha AR, Bradley PJ, Saba NF, Nuyts S, Triantafyllou A, Piazza C, Rinaldo A, Ferlito A. Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews. Adv Ther 2023; 40:3360-3380. [PMID: 37291378 PMCID: PMC10329964 DOI: 10.1007/s12325-023-02527-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/20/2023] [Indexed: 06/10/2023]
Abstract
INTRODUCTION Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decision-makers are faced with a plethora of reviews regarding the state of the art of applications of AI for head and neck cancer (HNC) management. This article provides an analysis of systematic reviews on the current status, and of the limitations of the application of AI/ML as adjunctive decision-making tools in HNC management. METHODS Electronic databases (PubMed, Medline via Ovid, Scopus, and Web of Science) were searched from inception until November 30, 2022. The study selection, searching and screening processes, inclusion, and exclusion criteria followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A risk of bias assessment was conducted using a tailored and modified version of the Assessment of Systematic Review (AMSTAR-2) tool and quality assessment using the Risk of Bias in Systematic Reviews (ROBIS) guidelines. RESULTS Of the 137 search hits retrieved, 17 fulfilled the inclusion criteria. This analysis of systematic reviews revealed that the application of AI/ML as a decision aid in HNC management can be thematized as follows: (1) detection of precancerous and cancerous lesions within histopathologic slides; (2) prediction of the histopathologic nature of a given lesion from various sources of medical imaging; (3) prognostication; (4) extraction of pathological findings from imaging; and (5) different applications in radiation oncology. In addition, the challenges in implementation of AI/ML models for clinical evaluations include the lack of standardized methodological guidelines for the collection of clinical images, development of these models, reporting of their performance, external validation procedures, and regulatory frameworks. CONCLUSION At present, there is a paucity of evidence to suggest the adoption of these models in clinical practice due to the aforementioned limitations. Therefore, this manuscript highlights the need for development of standardized guidelines to facilitate the adoption and implementation of these models in the daily clinical practice. In addition, adequately powered, prospective, randomized controlled trials are urgently needed to further assess the potential of AI/ML models in real-world clinical settings for the management of HNC.
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Affiliation(s)
- Antti A Mäkitie
- Department of Otorhinolaryngology-Head and Neck Surgery, Helsinki University Hospital, University of Helsinki, P.O. Box 263, 00029, HUS, 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 Institute and Karolinska University Hospital, Stockholm, Sweden.
| | - 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
| | - Sweet Ping Ng
- Department of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Austin Health, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
- School of Cancer Medicine, La Trobe University, Melbourne, Australia
- School of Imaging and Radiation Sciences, Monash University, Melbourne, Australia
| | - Robert P Takes
- Department of Otolaryngology and Head and Neck Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - K Thomas Robbins
- Department of Otolaryngology Head Neck Surgery, SIU School of Medicine, Southern Illinois University, Springfield, IL, USA
| | - Ohad Ronen
- Department of Otolaryngology-Head and Neck Surgery, Galilee Medical Center Affiliated with Azrieil Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Ashok R Shaha
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Patrick J Bradley
- The University of Nottingham, Department of ORLHNS, Queens Medical Centre Campus, Nottingham University Hospital, Derby Road, Nottingham, NG7 2UH, UK
| | - Nabil F Saba
- Department of Hematology and Medical Oncology, The Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Sandra Nuyts
- Laboratory of Experimental Radiotherapy, Department of Oncology, KU Leuven, 3000, Leuven, Belgium
- Department of Radiation Oncology, Leuven Cancer Institute, University Hospitals Leuven, 3000, Leuven, Belgium
| | - Asterios Triantafyllou
- Department of Pathology, Liverpool Clinical Laboratories, School of Dentistry, University of Liverpool, Liverpool, UK
| | - Cesare Piazza
- Unit of Otorhinolaryngology-Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, School of Medicine, University of Brescia, Brescia, Italy
| | | | - Alfio Ferlito
- Coordinator of the International Head and Neck Scientific Group, Padua, Italy
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Omobolaji Alabi R, Sjöblom A, Carpén T, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma: A validation of ProgTOOL. Int J Med Inform 2023; 175:105064. [PMID: 37094545 DOI: 10.1016/j.ijmedinf.2023.105064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND In recent years, there has been a surge in machine learning-based models for diagnosis and prognostication of outcomes in oncology. However, there are concerns relating to the model's reproducibility and generalizability to a separate patient cohort (i.e., external validation). OBJECTIVES This study primarily provides a validation study for a recently introduced and publicly available machine learning (ML) web-based prognostic tool (ProgTOOL) for overall survival risk stratification of oropharyngeal squamous cell carcinoma (OPSCC). Additionally, we reviewed the published studies that have utilized ML for outcome prognostication in OPSCC to examine how many of these models were externally validated, type of external validation, characteristics of the external dataset, and diagnostic performance characteristics on the internal validation (IV) and external validation (EV) datasets were extracted and compared. METHODS We used a total of 163 OPSCC patients obtained from the Helsinki University Hospital to externally validate the ProgTOOL for generalizability. In addition, PubMed, OvidMedline, Scopus, and Web of Science databases were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS The ProgTOOL produced a predictive performance of 86.5% balanced accuracy, Mathew's correlation coefficient of 0.78, Net Benefit (0.7) and Brier score (0.06) for overall survival stratification of OPSCC patients as either low-chance or high-chance. In addition, out of a total of 31 studies found to have used ML for the prognostication of outcomes in OPSCC, only seven (22.6%) reported a form of EV. Three studies (42.9%) each used either temporal EV or geographical EV while only one study (14.2%) used expert as a form of EV. Most of the studies reported a reduction in performance when externally validated. CONCLUSION The performance of the model in this validation study indicates that it may be generalized, therefore, bringing recommendations of the model for clinical evaluation closer to reality. However, the number of externally validated ML-based models for OPSCC is still relatively small. This significantly limits the transfer of these models for clinical evaluation and subsequently reduces the likelihood of the use of these models in daily clinical practice. As a gold standard, we recommend the use of geographical EV and validation studies to reveal biases and overfitting of these models. These recommendations are poised to facilitate the implementation of these models in clinical practice.
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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.
| | - Anni Sjöblom
- Department of Pathology, University of Helsinki, Helsinki, Finland
| | - Timo Carpén
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, 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; University of Turku, Institute of Biomedicine, Pathology, Turku, 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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Abstract
Nasopharyngeal carcinoma (NPC) is a metastasis-prone malignancy closely associated with the Epstein-Barr virus (EBV). Despite ubiquitous infection of EBV worldwide, NPC incidences displayed predominance in certain ethnic groups and endemic regions. The majority of NPC patients are diagnosed with advanced-stage disease, as a result of anatomical isolation and non-specific clinical manifestation. Over the decades, researchers have gained insights into the molecular mechanisms underlying NPC pathogenesis as a result of the interplay of EBV infection with several environmental and genetic factors. EBV-associated biomarkers were also used for mass population screening for the early detection of NPC. EBV and its encoded products also serve as potential targets for the development of therapeutic strategies and tumour-specific drug delivery. This review will discuss the pathogenic role of EBV in NPC and efforts in exploiting the potential of EBV-associated molecules as biomarkers and therapeutic targets. The current knowledge on the role of EBV and its associated products in NPC tumorigenesis, development and progression will offer a new outlook and potential intervention strategy against this EBV-associated malignancy.
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Affiliation(s)
- Zhi Yi Su
- Faculty of Medicine and Health Sciences, UCSI University, Bandar Springhill, Negeri Sembilan, Malaysia
| | - Pui Yan Siak
- Faculty of Medicine and Health Sciences, UCSI University, Bandar Springhill, Negeri Sembilan, Malaysia
| | - Chee-Onn Leong
- AGTC Genomics Sdn Bhd, Pusat Perdagangan Bandar, Persiaran Jalil 1, Bukit Jalil, Wilayah Persekutuan Kuala Lumpur, Malaysia
| | - Shiau-Chuen Cheah
- Faculty of Medicine and Health Sciences, UCSI University, Bandar Springhill, Negeri Sembilan, Malaysia
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Ng WT, Chow JCH, Beitler JJ, Corry J, Mendenhall W, Lee AWM, Robbins KT, Nuyts S, Saba NF, Smee R, Stokes WA, Strojan P, Ferlito A. Current Radiotherapy Considerations for Nasopharyngeal Carcinoma. Cancers (Basel) 2022; 14. [PMID: 36497254 DOI: 10.3390/cancers14235773] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022] Open
Abstract
Radiotherapy is the primary treatment modality for nasopharyngeal carcinoma (NPC). Successful curative treatment requires optimal radiotherapy planning and precise beam delivery that maximizes locoregional control while minimizing treatment-related side effects. In this article, we highlight considerations in target delineation, radiation dose, and the adoption of technological advances with the aim of optimizing the benefits of radiotherapy in NPC patients.
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Ji L, Mao R, Wu J, Ge C, Xiao F, Xu X, Xie L, Gu X. Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12102478. [PMID: 36292167 PMCID: PMC9601165 DOI: 10.3390/diagnostics12102478] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/03/2022] [Accepted: 10/09/2022] [Indexed: 12/24/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common head and neck cancers. Early diagnosis plays a critical role in the treatment of NPC. To aid diagnosis, deep learning methods can provide interpretable clues for identifying NPC from magnetic resonance images (MRI). To identify the optimal models, we compared the discrimination performance of hierarchical and simple layered convolutional neural networks (CNN). Retrospectively, we collected the MRI images of patients and manually built the tailored NPC image dataset. We examined the performance of the representative CNN models including shallow CNN, ResNet50, ResNet101, and EfficientNet-B7. By fine-tuning, shallow CNN, ResNet50, ResNet101, and EfficientNet-B7 achieved the precision of 72.2%, 94.4%, 92.6%, and 88.4%, displaying the superiority of deep hierarchical neural networks. Among the examined models, ResNet50 with pre-trained weights demonstrated the best classification performance over other types of CNN with accuracy, precision, and an F1-score of 0.93, 0.94, and 0.93, respectively. The fine-tuned ResNet50 achieved the highest prediction performance and can be used as a potential tool for aiding the diagnosis of NPC tumors.
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Affiliation(s)
- Li Ji
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
| | - Rongzhi Mao
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Jian Wu
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
| | - Cheng Ge
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Feng Xiao
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
| | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
- Correspondence: (X.X.); (L.X.); (X.G.)
| | - Liangxu Xie
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
- Correspondence: (X.X.); (L.X.); (X.G.)
| | - Xiaofeng Gu
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
- Correspondence: (X.X.); (L.X.); (X.G.)
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Hu F, Nie H, Xu R, Cai X, Shao L, Zhang P. Vinpocetine and coenzyme Q10 combination alleviates cognitive impairment caused by ionizing radiation by improving mitophagy. Brain Res 2022; 1792:148032. [PMID: 35907514 DOI: 10.1016/j.brainres.2022.148032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 07/21/2022] [Accepted: 07/23/2022] [Indexed: 11/02/2022]
Abstract
OBJECTIVE This research was designed to ascertain the effect and mechanism of vinpocetine (VIN) and coenzyme Q10 (CoQ10) combination on cognitive impairment induced by ionizing radiation (IR). METHODS Cognitive impairment in mice was induced by 9-Gy IR, and they were intraperitoneally injected with VIN, CoQ10, or VIN + CoQ10. Then novel object recognition and Morris water maze tests were used to detect cognitive function. The number of hippocampal neurons and BrdU+Dcx+ cells was observed by Nissl and immunofluorescence staining. Mitochondrial respiratory complex I, adenosine triphosphate (ATP), and mitochondrial membrane potential (MMP) were evaluated, as well as oxidative stress injury. Mitophagy in hippocampal neurons was evaluated by observing the ultrastructure of hippocampal neurons and assessing the expression of mitophagy-related proteins. RESULTS IR reduced novel object discrimination index, the time for platform crossing, and the time spent in platform quadrant, in addition to neuron loss, downregulated levels of mitochondrial respiratory complex I, ATP, and MMP, aggravated oxidative stress injury, increased expression of LC3 II/I, Beclin1, PINK1, and parkin, and decreased P62 expression. VIN or CoQ10 treatment mitigated cognitive dysfunction, neurons loss, mitochondrial damage, and oxidative stress injury, and enhanced mitophagy in hippocampal neurons. VIN and CoQ10 combination further protected against IR-induced cognitive dysfunction than VIN or CoQ10 alone. CONCLUSION VIN combined with CoQ10 improved neuron damage, promoted mitophagy, and ameliorated cognitive impairment in IR mice.
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Affiliation(s)
- Fan Hu
- Department of Neurology, Jiangxi Provincial People's Hospital, the First Affiliated to Nanchang Medical College, Nanchang 330006, Jiangxi, China
| | - Hongbing Nie
- Department of Neurology, Jiangxi Provincial People's Hospital, the First Affiliated to Nanchang Medical College, Nanchang 330006, Jiangxi, China
| | - Renxu Xu
- Department of Neurology, Jiangxi Provincial People's Hospital, the First Affiliated to Nanchang Medical College, Nanchang 330006, Jiangxi, China
| | - Xinyong Cai
- Department of Cardiology, Jiangxi Provincial People's Hospital, the First Affiliated to Nanchang Medical College, Nanchang 330006, Jiangxi, China
| | - Liang Shao
- Department of Cardiology, Jiangxi Provincial People's Hospital, the First Affiliated to Nanchang Medical College, Nanchang 330006, Jiangxi, China
| | - Ping Zhang
- Department of Neurology, Jiangxi Provincial People's Hospital, the First Affiliated to Nanchang Medical College, Nanchang 330006, Jiangxi, China.
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