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Dias AC, Jureidini RAG, Araujo-Filho JAB, Camerin GR, Zattar LC, Sernik RA, Malhotra A, Cerri LMO, Cerri GG. Advanced US of the Skin, Nerves, and Muscles of the Neck: Pearls and Pitfalls with Use of High-Frequency Transducers. Radiographics 2024; 44:e240029. [PMID: 39298354 DOI: 10.1148/rg.240029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2024]
Abstract
High-frequency US provides excellent visualization of superficial structures and lesions, is a preferred diagnostic modality for anatomic characterization of neck abnormalities, and has a central role in clinical decision making. Recent technological advancements have led to the development of transducers that surpass 20 MHz, elevating high-frequency US to a highly valuable diagnostic tool with broader clinical use and enabling greater spatial resolution in the assessment of skin and superficial nerves and muscles. The authors focus on evolving applications of high-frequency US in neck imaging, emphasizing practical insights and strategies in skin and neuromuscular applications. ©RSNA, 2024 Supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article.
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Affiliation(s)
- Alex C Dias
- From the Department of Radiology, Hospital Sirio-Libanes, Rua Adma Jafet, 91, São Paulo, SP 01308-050, Brazil (A.C.D., R.A.G.J., J.A.B.A.F., G.R.C., L.C.Z., R.A.S., L.M.O.C., G.G.C.); and Department of Diagnostic Radiology, Yale University School of Medicine. Yale New Haven Hospital, New Haven, Conn (A.M.)
| | - Regiany A G Jureidini
- From the Department of Radiology, Hospital Sirio-Libanes, Rua Adma Jafet, 91, São Paulo, SP 01308-050, Brazil (A.C.D., R.A.G.J., J.A.B.A.F., G.R.C., L.C.Z., R.A.S., L.M.O.C., G.G.C.); and Department of Diagnostic Radiology, Yale University School of Medicine. Yale New Haven Hospital, New Haven, Conn (A.M.)
| | - Jose A B Araujo-Filho
- From the Department of Radiology, Hospital Sirio-Libanes, Rua Adma Jafet, 91, São Paulo, SP 01308-050, Brazil (A.C.D., R.A.G.J., J.A.B.A.F., G.R.C., L.C.Z., R.A.S., L.M.O.C., G.G.C.); and Department of Diagnostic Radiology, Yale University School of Medicine. Yale New Haven Hospital, New Haven, Conn (A.M.)
| | - Gabriela R Camerin
- From the Department of Radiology, Hospital Sirio-Libanes, Rua Adma Jafet, 91, São Paulo, SP 01308-050, Brazil (A.C.D., R.A.G.J., J.A.B.A.F., G.R.C., L.C.Z., R.A.S., L.M.O.C., G.G.C.); and Department of Diagnostic Radiology, Yale University School of Medicine. Yale New Haven Hospital, New Haven, Conn (A.M.)
| | - Luciana C Zattar
- From the Department of Radiology, Hospital Sirio-Libanes, Rua Adma Jafet, 91, São Paulo, SP 01308-050, Brazil (A.C.D., R.A.G.J., J.A.B.A.F., G.R.C., L.C.Z., R.A.S., L.M.O.C., G.G.C.); and Department of Diagnostic Radiology, Yale University School of Medicine. Yale New Haven Hospital, New Haven, Conn (A.M.)
| | - Renato A Sernik
- From the Department of Radiology, Hospital Sirio-Libanes, Rua Adma Jafet, 91, São Paulo, SP 01308-050, Brazil (A.C.D., R.A.G.J., J.A.B.A.F., G.R.C., L.C.Z., R.A.S., L.M.O.C., G.G.C.); and Department of Diagnostic Radiology, Yale University School of Medicine. Yale New Haven Hospital, New Haven, Conn (A.M.)
| | - Ajay Malhotra
- From the Department of Radiology, Hospital Sirio-Libanes, Rua Adma Jafet, 91, São Paulo, SP 01308-050, Brazil (A.C.D., R.A.G.J., J.A.B.A.F., G.R.C., L.C.Z., R.A.S., L.M.O.C., G.G.C.); and Department of Diagnostic Radiology, Yale University School of Medicine. Yale New Haven Hospital, New Haven, Conn (A.M.)
| | - Luciana M O Cerri
- From the Department of Radiology, Hospital Sirio-Libanes, Rua Adma Jafet, 91, São Paulo, SP 01308-050, Brazil (A.C.D., R.A.G.J., J.A.B.A.F., G.R.C., L.C.Z., R.A.S., L.M.O.C., G.G.C.); and Department of Diagnostic Radiology, Yale University School of Medicine. Yale New Haven Hospital, New Haven, Conn (A.M.)
| | - Giovanni G Cerri
- From the Department of Radiology, Hospital Sirio-Libanes, Rua Adma Jafet, 91, São Paulo, SP 01308-050, Brazil (A.C.D., R.A.G.J., J.A.B.A.F., G.R.C., L.C.Z., R.A.S., L.M.O.C., G.G.C.); and Department of Diagnostic Radiology, Yale University School of Medicine. Yale New Haven Hospital, New Haven, Conn (A.M.)
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Sunnetci KM, Kaba E, Celiker FB, Alkan A. Deep Network-Based Comprehensive Parotid Gland Tumor Detection. Acad Radiol 2024; 31:157-167. [PMID: 37271636 DOI: 10.1016/j.acra.2023.04.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 04/19/2023] [Accepted: 04/21/2023] [Indexed: 06/06/2023]
Abstract
RATIONALE AND OBJECTIVES Salivary gland tumors constitute 2%-6% of all head and neck tumors and are most common in the parotid gland. Magnetic resonance (MR) imaging is the most sensitive imaging modality for diagnosis. Tumor type, localization, and relationship with surrounding structures are important factors for treatment. Therefore, parotid gland tumor segmentation is important. Specialists widely use manual segmentation in diagnosis and treatment. However, considering the development of artificial intelligence-based models today, it is seen that artificial intelligence-based automatic segmentation models can be used instead of manual segmentation, which is a time-consuming technique. Therefore, we segmented parotid gland tumor (PGT) using deep learning-based architectures in the paper. MATERIALS AND METHODS The dataset used in the study includes 102 T1-w, 102 contrast-enhanced T1-w (T1C-w), and 102 T2-w MR images. After cropping the raw and manually segmented images by experts, we obtained the masks of these images. After standardizing the image sizes, we split these images into approximately 80% training set and 20% test set. Hereabouts, we trained six models for these images using ResNet18 and Xception-based DeepLab v3+. We prepared a user-friendly Graphical User Interface application that includes each of these models. RESULTS From the results, the accuracy and weighted Intersection over Union values of the ResNet18-based DeepLab v3+ architecture trained for T1C-w, which is the most successful model in the study, are equal to 0.96153 and 0.92601, respectively. Regarding the results and the literature, it can be seen that the proposed system is competitive in terms of both using MR images and training the models independently for T1-w, T1C-w, and T2-w. Expressing that PGT is usually segmented manually in the literature, we predict that our study can contribute significantly to the literature. CONCLUSION In this study, we prepared and presented a software application that can be easily used by users for automatic PGT segmentation. In addition to predicting the reduction of costs and workload through the study, we developed models with meaningful performance metrics according to the literature.
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Affiliation(s)
- Kubilay Muhammed Sunnetci
- Osmaniye Korkut Ata University, Department of Electrical and Electronics Engineering, Osmaniye 80000, Turkey (K.M.S.); Kahramanmaraş Sütçü İmam University, Department of Electrical and Electronics Engineering, Kahramanmaraş 46050, Turkey (K.M.S., A.A.).
| | - Esat Kaba
- Recep Tayyip Erdogan University, Department of Radiology, Rize, Turkey (E.K., F.B.C.)
| | - Fatma Beyazal Celiker
- Recep Tayyip Erdogan University, Department of Radiology, Rize, Turkey (E.K., F.B.C.)
| | - Ahmet Alkan
- Kahramanmaraş Sütçü İmam University, Department of Electrical and Electronics Engineering, Kahramanmaraş 46050, Turkey (K.M.S., A.A.)
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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Wagner DT, Tilmans L, Peng K, Niedermeier M, Rohl M, Ryan S, Yadav D, Takacs N, Garcia-Fraley K, Koso M, Dikici E, Prevedello LM, Nguyen XV. Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges. Diagnostics (Basel) 2023; 13:2670. [PMID: 37627929 PMCID: PMC10453240 DOI: 10.3390/diagnostics13162670] [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: 05/16/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.
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Affiliation(s)
- Daniel T. Wagner
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luke Tilmans
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Kevin Peng
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | | | - Matt Rohl
- College of Arts and Sciences, The Ohio State University, Columbus, OH 43210, USA
| | - Sean Ryan
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Divya Yadav
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Noah Takacs
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Krystle Garcia-Fraley
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Mensur Koso
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Engin Dikici
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luciano M. Prevedello
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Xuan V. Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
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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] [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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Santer M, Kloppenburg M, Gottfried TM, Runge A, Schmutzhard J, Vorbach SM, Mangesius J, Riedl D, Mangesius S, Widmann G, Riechelmann H, Dejaco D, Freysinger W. Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma-A Systematic Review. Cancers (Basel) 2022; 14:5397. [PMID: 36358815 PMCID: PMC9654953 DOI: 10.3390/cancers14215397] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 10/28/2022] [Accepted: 10/29/2022] [Indexed: 07/22/2023] Open
Abstract
Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10-258) and of LNs was 340 (SD ± 268; range 21-791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43-99%) and for testing sets 86% (SD ± 5%; range 76-92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI's role in LN-classification in locally-advanced HNSCC.
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Affiliation(s)
- Matthias Santer
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Marcel Kloppenburg
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Timo Maria Gottfried
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Annette Runge
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Joachim Schmutzhard
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Samuel Moritz Vorbach
- Department of Radiation-Oncology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Julian Mangesius
- Department of Radiation-Oncology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - David Riedl
- University Hospital of Psychiatry II, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Ludwig-Boltzmann Institute for Rehabilitation Research, 1100 Vienna, Austria
| | - Stephanie Mangesius
- Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Gerlig Widmann
- Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Herbert Riechelmann
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Daniel Dejaco
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Wolfgang Freysinger
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
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