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Xu X, Xi L, Zhu J, Feng C, Zhou P, Liu K, Shang Z, Shao Z. Intelligent Diagnosis of Cervical Lymph Node Metastasis Using a CNN Model. J Dent Res 2025:220345251322508. [PMID: 40271993 DOI: 10.1177/00220345251322508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2025] Open
Abstract
Lymph node (LN) metastasis is a prevalent cause of recurrence in oral squamous cell carcinoma (OSCC). However, accurately identifying metastatic LNs (LNs+) remains challenging. This prospective clinical study aims to test the effectiveness of our convolutional neural network (CNN) model for identifying OSCC cervical LN+ in contrast-enhanced computed tomography (CECT) in clinical practice. A CNN model was developed and trained using a dataset of 8,380 CECT images from previous OSCC patients. It was then prospectively validated on 17,777 preoperative CECT images from 354 OSCC patients between October 17, 2023, and August 31, 2024. The model's predicted LN results were provided to the surgical team without influencing surgical or treatment plans. During surgery, the predicted LN+ were identified and sent for separate pathological examination. The accuracy of the model's predictions was compared with those of human experts and verified against pathology reports. The capacity of the model to assist radiologists in LN+ diagnosis was also assessed. The CNN model was trained over 40 epochs and successfully validated after each. Compared with human experts (2 radiologists, 2 surgeons, and 2 students), the CNN model achieved higher sensitivity (81.89% vs. 81.48%, 46.91%, 50.62%), specificity (99.31% vs. 99.15%, 98.36%, 96.27%), LN+ accuracy (76.19% vs. 75.43%, P = 0.854; 40.64%, P < 0.001; 37.44%, P < 0.001), and clinical accuracy (86.16% vs. 83%, 61%, 56%). With the model's assistance, the radiologists surpassed both the previous predictive results without the model's support and the model's performance alone. The CNN model demonstrated an accuracy comparable to that of radiologists in identifying, locating, and predicting cervical LN+ in OSCC patients. Furthermore, the model has the potential to assist radiologists in making more accurate diagnoses.
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Affiliation(s)
- X Xu
- The 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, China
- Day Surgery Center, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - L Xi
- School of Computer Science, Wuhan University, Wuhan, China
| | - J Zhu
- The 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, China
- Department of Geriatric Dentistry, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - C Feng
- The 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, China
| | - P Zhou
- Department of Radiology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - K Liu
- The 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, China
- Department of Oral and Maxillofacial Head Neck Surgery, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Z Shang
- The 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, China
| | - Z Shao
- The 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, China
- Day Surgery Center, School & Hospital of Stomatology, Wuhan University, Wuhan, China
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Sitaras S, Tsolakis IA, Gelsini M, Tsolakis AI, Schwendicke F, Wolf TG, Perlea P. Applications of Artificial Intelligence in Dental Medicine: A Critical Review. Int Dent J 2025; 75:474-486. [PMID: 39843259 PMCID: PMC11976566 DOI: 10.1016/j.identj.2024.11.009] [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: 10/14/2024] [Accepted: 11/12/2024] [Indexed: 01/24/2025] Open
Abstract
INTRODUCTION Artificial intelligence (AI), including its subfields of machine learning and deep learning, is a branch of computer science and engineering focused on creating machines capable of tasks requiring human-like intelligence, such as visual perception, decision-making, and natural language processing. AI applications have become increasingly prevalent in dental medicine, generating high expectations as well as raising ethical and practical concerns. METHODS This critical review evaluates the current applications of AI in dentistry, identifying key perspectives, challenges, and limitations in ongoing AI research. RESULTS AI models have been applied across various dental specialties, supporting diagnosis, treatment planning, and decision-making, while also reducing the burden of repetitive tasks and optimizing clinical workflows. However, ethical complexities and methodological limitations, such as inconsistent data quality, bias risk, lack of transparency, and limited clinical validation, undermine the quality of AI studies and hinder the effective integration of AI into routine dental practice. CONCLUSIONS To improve AI research, studies must adhere to standardized methodological and ethical guidelines, particularly in data collection, while ensuring transparency, privacy, and accountability. Developing a comprehensive framework for producing robust, reproducible AI research and clinically validated technologies will facilitate the seamless integration of AI into clinical practice, benefiting both clinicians and patients by improving dental care.
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Affiliation(s)
| | - Ioannis A Tsolakis
- Department of Orthodontics, School of Dentistry, Aristotle University of Thessaloniki, Thessaloniki, Greece; Department of Orthodontics, C.W.R.U., Cleveland, Ohio, USA
| | | | - Apostolos I Tsolakis
- Department of Orthodontics, C.W.R.U., Cleveland, Ohio, USA; Department of Orthodontics, National and Kapodistrian University of Athens, School of Dentistry, Athens, Greece
| | - Falk Schwendicke
- Department of Conservative Dentistry and Periodontology, Ludwig-Maximilians-University (LMU), Munich, North Dakota, Germany
| | - Thomas Gerhard Wolf
- Department of Restorative, Preventive and Pediatric Dentistry, School of Dental Medicine, University of Bern, Bern, Switzerland; Department of Periodontology and Operative Dentistry, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
| | - Paula Perlea
- Department of Endodontics, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
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Naemi A, Tashk A, Sorayaie Azar A, Samimi T, Tavassoli G, Bagherzadeh Mohasefi A, Nasiri Khanshan E, Heshmat Najafabad M, Tarighi V, Wiil UK, Bagherzadeh Mohasefi J, Pirnejad H, Niazkhani Z. Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review. Cancers (Basel) 2025; 17:558. [PMID: 39941923 PMCID: PMC11817159 DOI: 10.3390/cancers17030558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2024] [Revised: 01/18/2025] [Accepted: 02/05/2025] [Indexed: 02/16/2025] Open
Abstract
BACKGROUND/OBJECTIVES This systematic literature review examines the application of Artificial Intelligence (AI) in the diagnosis, treatment, and follow-up of metastatic gastrointestinal cancers. METHODS The databases PubMed, Scopus, Embase (Ovid), and Google Scholar were searched for published articles in English from January 2010 to January 2022, focusing on AI models in metastatic gastrointestinal cancers. RESULTS forty-six studies were included in the final set of reviewed papers. The critical appraisal and data extraction followed the checklist for systematic reviews of prediction modeling studies. The risk of bias in the included papers was assessed using the prediction risk of bias assessment tool. CONCLUSIONS AI techniques, including machine learning and deep learning models, have shown promise in improving diagnostic accuracy, predicting treatment outcomes, and identifying prognostic biomarkers. Despite these advancements, challenges persist, such as reliance on retrospective data, variability in imaging protocols, small sample sizes, and data preprocessing and model interpretability issues. These challenges limit the generalizability, clinical application, and integration of AI models.
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Affiliation(s)
- Amin Naemi
- Nordcee, Department of Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Ashkan Tashk
- Cognitive Systems, DTU Compute, The Technical University of Denmark (DTU), 2800 Copenhagen, Denmark;
| | - Amir Sorayaie Azar
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark; (A.S.A.); (U.K.W.); (J.B.M.)
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Tahereh Samimi
- Student Research Committee, Urmia University of Medical Sciences, Urmia 1138, Iran;
- Department of Medical Informatics, Urmia University of Medical Sciences, Urmia 1138, Iran
| | - Ghanbar Tavassoli
- Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 969, Iran;
| | - Anita Bagherzadeh Mohasefi
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Elaheh Nasiri Khanshan
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Mehrdad Heshmat Najafabad
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Vafa Tarighi
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Uffe Kock Wiil
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark; (A.S.A.); (U.K.W.); (J.B.M.)
| | - Jamshid Bagherzadeh Mohasefi
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark; (A.S.A.); (U.K.W.); (J.B.M.)
- Department of Computer Engineering, Urmia University, Urmia 165, Iran; (A.B.M.); (E.N.K.); (M.H.N.); (V.T.)
| | - Habibollah Pirnejad
- Patient Safety Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia 1138, Iran;
- Department of Family Medicine, Amsterdam University Medical Center, 7057 Amsterdam, The Netherlands
| | - Zahra Niazkhani
- Nephrology and Kidney Transplant Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia 1138, Iran;
- Erasmus School of Health Policy and Management (ESHPM), Erasmus University Rotterdam, 3000 Rotterdam, The Netherlands
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Alapati R, Renslo B, Wagoner SF, Karadaghy O, Serpedin A, Kim YE, Feucht M, Wang N, Ramesh U, Bon Nieves A, Lawrence A, Virgen C, Sawaf T, Rameau A, Bur AM. Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology. Laryngoscope 2025; 135:687-694. [PMID: 39258420 DOI: 10.1002/lary.31756] [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: 02/07/2024] [Revised: 07/25/2024] [Accepted: 08/23/2024] [Indexed: 09/12/2024]
Abstract
OBJECTIVE This study aimed to assess reporting quality of machine learning (ML) algorithms in the head and neck oncology literature using the TRIPOD-AI criteria. DATA SOURCES A comprehensive search was conducted using PubMed, Scopus, Embase, and Cochrane Database of Systematic Reviews, incorporating search terms related to "artificial intelligence," "machine learning," "deep learning," "neural network," and various head and neck neoplasms. REVIEW METHODS Two independent reviewers analyzed each published study for adherence to the 65-point TRIPOD-AI criteria. Items were classified as "Yes," "No," or "NA" for each publication. The proportion of studies satisfying each TRIPOD-AI criterion was calculated. Additionally, the evidence level for each study was evaluated independently by two reviewers using the Oxford Centre for Evidence-Based Medicine (OCEBM) Levels of Evidence. Discrepancies were reconciled through discussion until consensus was reached. RESULTS The study highlights the need for improvements in ML algorithm reporting in head and neck oncology. This includes more comprehensive descriptions of datasets, standardization of model performance reporting, and increased sharing of ML models, data, and code with the research community. Adoption of TRIPOD-AI is necessary for achieving standardized ML research reporting in head and neck oncology. CONCLUSION Current reporting of ML algorithms hinders clinical application, reproducibility, and understanding of the data used for model training. To overcome these limitations and improve patient and clinician trust, ML developers should provide open access to models, code, and source data, fostering iterative progress through community critique, thus enhancing model accuracy and mitigating biases. LEVEL OF EVIDENCE NA Laryngoscope, 135:687-694, 2025.
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Affiliation(s)
- Rahul Alapati
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Bryan Renslo
- Department of Otolaryngology-Head & Neck Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, U.S.A
| | - Sarah F Wagoner
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Omar Karadaghy
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Aisha Serpedin
- Department of Otolaryngology-Head & Neck Surgery, Weill Cornell, New York City, New York, U.S.A
| | - Yeo Eun Kim
- Department of Otolaryngology-Head & Neck Surgery, Weill Cornell, New York City, New York, U.S.A
| | - Maria Feucht
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Naomi Wang
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Uma Ramesh
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Antonio Bon Nieves
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Amelia Lawrence
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Celina Virgen
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Tuleen Sawaf
- Department of Otolaryngology-Head & Neck Surgery, University of Maryland, Baltimore, Maryland, U.S.A
| | - Anaïs Rameau
- Department of Otolaryngology-Head & Neck Surgery, Weill Cornell, New York City, New York, U.S.A
| | - Andrés M Bur
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
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5
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Chen W, Dhawan M, Liu J, Ing D, Mehta K, Tran D, Lawrence D, Ganhewa M, Cirillo N. Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review. Clin Exp Dent Res 2024; 10:e70035. [PMID: 39600121 PMCID: PMC11599430 DOI: 10.1002/cre2.70035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 09/19/2024] [Accepted: 10/20/2024] [Indexed: 11/29/2024] Open
Abstract
OBJECTIVES Artificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision-making in clinical dentistry and identify trends and research gaps in the current literature. MATERIAL AND METHODS This review followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). An electronic literature search was performed through PubMed and Scopus. After removing duplicates, a preliminary screening based on titles and abstracts was performed. A full-text review and analysis were performed according to predefined inclusion criteria, and data were extracted from eligible articles. RESULTS Of the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). AI was applied across all fields of dentistry - particularly oral medicine, oral surgery, and orthodontics - for direct clinical inference and segmentation. AI-based image analysis was use in several components of the clinical decision-making process, including diagnosis, detection or classification, prediction, and management. CONCLUSIONS A variety of machine learning and deep learning techniques are being used for dental image analysis to assist clinicians in making accurate diagnoses and choosing appropriate interventions in a timely manner.
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Affiliation(s)
- Wei Chen
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | - Monisha Dhawan
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | - Jonathan Liu
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | - Damie Ing
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | - Kruti Mehta
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | - Daniel Tran
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | | | - Max Ganhewa
- CoTreatAI, CoTreat Pty Ltd.MelbourneVictoriaAustralia
| | - Nicola Cirillo
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
- CoTreatAI, CoTreat Pty Ltd.MelbourneVictoriaAustralia
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Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:641-655. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
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Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Oliver J, Alapati R, Lee J, Bur A. Artificial Intelligence in Head and Neck Surgery. Otolaryngol Clin North Am 2024; 57:803-820. [PMID: 38910064 PMCID: PMC11374486 DOI: 10.1016/j.otc.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [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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Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024; 57:719-751. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [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] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
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Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
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Multidisciplinary Oropharyngeal Cancer Extra-Nodal Extension (OPC ENE) Assessment Working Group, Sahin O, Kamel S, Wahid KA, Dede C, Taku N, He R, Naser MA, Sharafi S, Mäkitie A, Kann BH, Kaski K, Sahlsten J, Jaskari J, Amit M, Chronowski GM, Diaz EM, Garden AS, Goepfert RP, Guenette JP, Gunn GB, Hirvonen J, Hoebers F, Hutcheson KA, Guha-Thakurta N, Johnson J, Kaya D, Khanpara SD, Nyman K, Lai SY, Lango M, Learned KO, Lee A, Lewis CM, Maniakas A, Moreno AC, Myers JN, Phan J, Pytynia KB, Rosenthal DI, Sandulache VC, Schellingerhout D, Shah SJ, Sikora AG, Mohamed ASR, Chen MM, Fuller CD. International Multi-Specialty Expert Physician Preoperative Identification of Extranodal Extension n Oropharyngeal Cancer Patients using Computed Tomography: Prospective Blinded Human Inter-Observer Performance Evaluation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.02.25.23286432. [PMID: 36865096 PMCID: PMC9980252 DOI: 10.1101/2023.02.25.23286432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Importance Extranodal extension (pENE) is a critical prognostic factor in oropharyngeal cancer (OPC) that drives therapeutic disposition. Determination of pENE from radiological imaging has been associated with high inter-observer variability. However, the impact of clinician specialty on human observer performance of imaging-detected extranodal extension (iENE) remains poorly understood. Objective To characterize the impact of clinician specialty on the accuracy of pre-operative iENE in human papillomavirus-positive (HPV+) OPC using computed tomography (CT) images. Design Setting and Participants This prospective observational human performance study analyzed pre-therapy CT images from 24 HPV+ OPC patients, with duplication of 6 scans (n=30) of which 21 were pathologically confirmed pENE. Thirty-four expert observers, including 11 radiologists, 12 surgeons, and 11 radiation oncologists, independently assessed these scans for iENE and reported human-detected radiologic criteria and observer confidence. Main Outcomes and Measures The primary outcomes included accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and Brier score for each physician, compared to ground-truth pENE. The significance of radiographic signs for prediction of pENE were determined through logistic regression analysis. Fleiss' kappa measured interobserver agreement, and Hanley-MacNeil AUC discrimination testing. Results Median accuracy across all specialties was 0.57 (95%CI 0.39 to 0.73), with no specialty showing discriminate performance greater than random estimation (median AUC 0.64, 95%CI 0.44 to 0.83). Significant differences between radiologists and surgeons in Brier scores (0.33 vs. 0.26, p < 0.01), radiation oncologists and surgeons in sensitivity (0.48 vs. 0.69, p > 0.1), and radiation oncologists and radiologists/surgeons in specificity (0.89 vs. 0.56, p > 0.1). Indistinct capsular contour and nodal necrosis were significant predictors of correct pENE status among all specialties. Interobserver agreement was weak for all the radiographic criteria, regardless of specialty (κ<0.6). Conclusions and Relevance Multiobserver testing shows physician discrimination of HPV+OPC pENE on pre-operative CT remains non-different than blind guessing, with high interrater variability and low diagnostic accuracy, regardless of clinician specialty. While minor differences in diagnostic performance among specialties are noted, they do not significantly affect the overall poor agreement and discrimination rates observed. The findings underscore the need for further research into automated detection systems or enhanced imaging techniques to improve the accuracy and reliability of iENE assessments in clinical practice.
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Affiliation(s)
| | - Onur Sahin
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Kareem A. Wahid
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Cem Dede
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Nicolette Taku
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Renjie He
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Setareh Sharafi
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Antti Mäkitie
- University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Benjamin H. Kann
- Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | | | | | | | - Moran Amit
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Eduardo M. Diaz
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Adam S. Garden
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Jeffrey P. Guenette
- Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - G. Brandon Gunn
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Jussi Hirvonen
- Tampere University, Faculty of Medicine and Health Technology and Tampere University Hospital, Tampere, Finland
| | - Frank Hoebers
- Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | | | | | - Jason Johnson
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Diana Kaya
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Kristofer Nyman
- University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Stephen Y. Lai
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Miriam Lango
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Kim O. Learned
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Anna Lee
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Carol M. Lewis
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Amy C. Moreno
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Jack Phan
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | | | - Vlad C. Sandulache
- Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, USA
| | | | - Shalin J. Shah
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Abdallah S. R. Mohamed
- The University of Texas MD Anderson Cancer Center, Houston, USA
- Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, Houston, USA
| | - Melissa M. Chen
- The University of Texas MD Anderson Cancer Center, Houston, USA
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Ayub B, Qureshi FA, Hassan NH, Shaukat F, Qureshi TA. Optimising head and neck cancer patient management: the crucial contributions of multidisciplinary tumour board decision-making. Ecancermedicalscience 2024; 18:1710. [PMID: 39021536 PMCID: PMC11254396 DOI: 10.3332/ecancer.2024.1710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Indexed: 07/20/2024] Open
Abstract
Introduction Squamous cell carcinoma (SCC) of the head and neck is a great burden globally, which is being tackled through treatment options of surgery, radiation therapy, chemotherapy, or a combination of these, to avoid disease-related mortality. Multidisciplinary tumour boards play a pivotal role in customising and deciding management plan based on clinical aspects. The objective of the study is to determine the concordance of opinion between the treatment plan of a primary physician and board members. Material and methods This is a retrospective cross-sectional study that includes 137 head and neck carcinoma cases. They were discussed in the multidisciplinary tumour board meeting and were reviewed; all demographics were analysed including the tumour staging and the decisions of the primary physician was compared with those of the board. To check the concordance between primary surgeon plans or after board discussion Kappa agreement test was used. Results Total of 137 patients were included in the study out of which 63 cases were pre-treatment and 74 cases were post-treatment, i.e., surgically treated cases, with the distribution being 46% and 54%, respectively. Most cases, totaling 120, were SCC, accounting for 80% of the total cases. Among the pre-treatment cases, T4a and N0 were the most common categories, with 29 and 40 cases, respectively. Similarly, in post-treatment cases, the majority fell into the T4a and N1 categories, with 29 and 38 cases, respectively. When comparing the primary surgeon's plan with the tumour board meeting decision, the agreement showed a value of 0.273, indicating a slight level of agreement between the two entities. Conclusion Our data indicates that the multidisciplinary head and neck tumour board may have influenced the treatment plans of the primary surgeon, in approximately one in two patients (43.06%).
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Affiliation(s)
- Bushra Ayub
- Department of Learning Research Centre, Patel Hospital, Karachi 75300, Pakistan
| | - Fizza Asif Qureshi
- Department of Otolaryngology and Head and Neck Surgery, Patel Hospital, Karachi 75300, Pakistan
| | - Nabeel Humayun Hassan
- Department of Otolaryngology and Head and Neck Surgery, Patel Hospital, Karachi 75300, Pakistan
| | - Fatima Shaukat
- Cyberknife and Tomotherapy Centre, Jinnah Postgraduate Medical Centre, Karachi 75510, Pakistan
| | - Talha Ahmed Qureshi
- Department of Otolaryngology and Head and Neck Surgery, Patel Hospital, Karachi 75300, Pakistan
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Li C, Chen X, Chen C, Gong Z, Pataer P, Liu X, Lv X. Application of deep learning radiomics in oral squamous cell carcinoma-Extracting more information from medical images using advanced feature analysis. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024; 125:101840. [PMID: 38548062 DOI: 10.1016/j.jormas.2024.101840] [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: 02/15/2024] [Revised: 03/07/2024] [Accepted: 03/20/2024] [Indexed: 04/02/2024]
Abstract
OBJECTIVE To conduct a systematic review with meta-analyses to assess the recent scientific literature addressing the application of deep learning radiomics in oral squamous cell carcinoma (OSCC). MATERIALS AND METHODS Electronic and manual literature retrieval was performed using PubMed, Web of Science, EMbase, Ovid-MEDLINE, and IEEE databases from 2012 to 2023. The ROBINS-I tool was used for quality evaluation; random-effects model was used; and results were reported according to the PRISMA statement. RESULTS A total of 26 studies involving 64,731 medical images were included in quantitative synthesis. The meta-analysis showed that, the pooled sensitivity and specificity were 0.88 (95 %CI: 0.87∼0.88) and 0.80 (95 %CI: 0.80∼0.81), respectively. Deeks' asymmetry test revealed there existed slight publication bias (P = 0.03). CONCLUSIONS The advances in the application of radiomics combined with learning algorithm in OSCC were reviewed, including diagnosis and differential diagnosis of OSCC, efficacy assessment and prognosis prediction. The demerits of deep learning radiomics at the current stage and its future development direction aimed at medical imaging diagnosis were also summarized and analyzed at the end of the article.
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Affiliation(s)
- Chenxi Li
- Oncological Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Xinjiang Medical University, School / Hospital of Stomatology. Urumqi 830054, PR China; Stomatological Research Institute of Xinjiang Uygur Autonomous Region. Urumqi 830054, PR China; Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, School of Stomatology, Tongji Medical College, Union Hospital, Huazhong University of Science and Technology, Wuhan 430022, PR China.
| | - Xinya Chen
- College of Information Science and Engineering, Xinjiang University. Urumqi 830008, PR China
| | - Cheng Chen
- College of Software, Xinjiang University. Urumqi 830046, PR China
| | - Zhongcheng Gong
- Oncological Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Xinjiang Medical University, School / Hospital of Stomatology. Urumqi 830054, PR China; Stomatological Research Institute of Xinjiang Uygur Autonomous Region. Urumqi 830054, PR China.
| | - Parekejiang Pataer
- Oncological Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Xinjiang Medical University, School / Hospital of Stomatology. Urumqi 830054, PR China
| | - Xu Liu
- Department of Maxillofacial Surgery, Hospital of Stomatology, Key Laboratory of Dental-Maxillofacial Reconstruction and Biological Intelligence Manufacturing of Gansu Province, Faculty of Dentistry, Lanzhou University. Lanzhou 730013, PR China
| | - Xiaoyi Lv
- College of Information Science and Engineering, Xinjiang University. Urumqi 830008, PR China; College of Software, Xinjiang University. Urumqi 830046, PR China
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Warin K, Suebnukarn S. Deep learning in oral cancer- a systematic review. BMC Oral Health 2024; 24:212. [PMID: 38341571 PMCID: PMC10859022 DOI: 10.1186/s12903-024-03993-5] [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: 10/27/2023] [Accepted: 02/06/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Oral cancer is a life-threatening malignancy, which affects the survival rate and quality of life of patients. The aim of this systematic review was to review deep learning (DL) studies in the diagnosis and prognostic prediction of oral cancer. METHODS This systematic review was conducted following the PRISMA guidelines. Databases (Medline via PubMed, Google Scholar, Scopus) were searched for relevant studies, from January 2000 to June 2023. RESULTS Fifty-four qualified for inclusion, including diagnostic (n = 51), and prognostic prediction (n = 3). Thirteen studies showed a low risk of biases in all domains, and 40 studies low risk for concerns regarding applicability. The performance of DL models was reported of the accuracy of 85.0-100%, F1-score of 79.31 - 89.0%, Dice coefficient index of 76.0 - 96.3% and Concordance index of 0.78-0.95 for classification, object detection, segmentation, and prognostic prediction, respectively. The pooled diagnostic odds ratios were 2549.08 (95% CI 410.77-4687.39) for classification studies. CONCLUSIONS The number of DL studies in oral cancer is increasing, with a diverse type of architectures. The reported accuracy showed promising DL performance in studies of oral cancer and appeared to have potential utility in improving informed clinical decision-making of oral cancer.
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Affiliation(s)
- Kritsasith Warin
- Faculty of Dentistry, Thammasat University, Pathum Thani, Thailand.
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13
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Can S, Türk Ö, Ayral M, Kozan G, Arı H, Akdağ M, Baylan MY. Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy? Eur Arch Otorhinolaryngol 2024; 281:359-367. [PMID: 37578497 DOI: 10.1007/s00405-023-08181-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/07/2023] [Indexed: 08/15/2023]
Abstract
INTRODUCTION We aimed to develop a diagnostic deep learning model using contrast-enhanced CT images and to investigate whether cervical lymphadenopathies can be diagnosed with these deep learning methods without radiologist interpretations and histopathological examinations. MATERIAL METHOD A total of 400 patients who underwent surgery for lymphadenopathy in the neck between 2010 and 2022 were retrospectively analyzed. They were examined in four groups of 100 patients: the granulomatous diseases group, the lymphoma group, the squamous cell tumor group, and the reactive hyperplasia group. The diagnoses of the patients were confirmed histopathologically. Two CT images from all the patients in each group were used in the study. The CT images were classified using ResNet50, NASNetMobile, and DenseNet121 architecture input. RESULTS The classification accuracies obtained with ResNet50, DenseNet121, and NASNetMobile were 92.5%, 90.62, and 87.5, respectively. CONCLUSION Deep learning is a useful diagnostic tool in diagnosing cervical lymphadenopathy. In the near future, many diseases could be diagnosed with deep learning models without radiologist interpretations and invasive examinations such as histopathological examinations. However, further studies with much larger case series are needed to develop accurate deep-learning models.
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Affiliation(s)
- Sermin Can
- Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey.
| | - Ömer Türk
- Department of Computer Programming, Mardin Artuklu University Vocational School, Mardin, Turkey
| | - Muhammed Ayral
- Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey
| | - Günay Kozan
- Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey
| | - Hamza Arı
- Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey
| | - Mehmet Akdağ
- Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey
| | - Müzeyyen Yıldırım Baylan
- Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey
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Esce AR, Baca AL, Redemann JP, Rebbe RW, Schultz F, Agarwal S, Hanson JA, Olson GT, Martin DR, Boyd NH. Predicting nodal metastases in squamous cell carcinoma of the oral tongue using artificial intelligence. Am J Otolaryngol 2024; 45:104102. [PMID: 37948827 DOI: 10.1016/j.amjoto.2023.104102] [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: 08/02/2023] [Accepted: 10/29/2023] [Indexed: 11/12/2023]
Abstract
OBJECTIVE The presence of occult nodal metastases in patients with squamous cell carcinoma (SCC) of the oral tongue has implications for treatment. Upwards of 30% of patients will have occult nodal metastases, yet a significant number of patients undergo unnecessary neck dissection to confirm nodal status. This study sought to predict the presence of nodal metastases in patients with SCC of the oral tongue using a convolutional neural network (CNN) that analyzed visual histopathology from the primary tumor alone. METHODS Cases of SCC of the oral tongue were identified from the records of a single institution. Only patients with complete pathology data were included in the study. The primary tumors were randomized into 2 groups for training and testing, which was performed at 2 different levels of supervision. Board-certified pathologists annotated each slide. HALO-AI convolutional neural network and image software was used to perform training and testing. Receiver operator characteristic (ROC) curves and the Youden J statistic were used for primary analysis. RESULTS Eighty-nine cases of SCC of the oral tongue were included in the study. The best performing algorithm had a high level of supervision and a sensitivity of 65% and specificity of 86% when identifying nodal metastases. The area under the curve (AUC) of the ROC curve for this algorithm was 0.729. CONCLUSION A CNN can produce an algorithm that is able to predict nodal metastases in patients with squamous cell carcinoma of the oral tongue by analyzing the visual histopathology of the primary tumor alone.
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Affiliation(s)
- Antoinette R Esce
- Department of Surgery, Division of Otolaryngology Head and Neck Surgery, 1 University of New Mexico, MSC10 5610, Albuquerque, NM, 87131, USA.
| | - Andrewe L Baca
- The University of New Mexico School of Medicine, 1 University of New Mexico, MSC08 4720, Albuquerque, NM 87131, USA
| | - Jordan P Redemann
- Department of Pathology, 1 University of New Mexico, MSC08 4640, Albuquerque, NM, 87131, USA.
| | - Ryan W Rebbe
- Department of Pathology, 1 University of New Mexico, MSC08 4640, Albuquerque, NM, 87131, USA.
| | - Fred Schultz
- Department of Pathology, 1 University of New Mexico, MSC08 4640, Albuquerque, NM, 87131, USA.
| | - Shweta Agarwal
- Department of Pathology, 1 University of New Mexico, MSC08 4640, Albuquerque, NM, 87131, USA.
| | - Joshua A Hanson
- Department of Pathology, 1 University of New Mexico, MSC08 4640, Albuquerque, NM, 87131, USA.
| | - Garth T Olson
- Department of Surgery, Division of Otolaryngology Head and Neck Surgery, 1 University of New Mexico, MSC10 5610, Albuquerque, NM, 87131, USA.
| | - David R Martin
- Department of Surgery, Division of Otolaryngology Head and Neck Surgery, 1 University of New Mexico, MSC10 5610, Albuquerque, NM, 87131, USA.
| | - Nathan H Boyd
- Department of Surgery, Division of Otolaryngology Head and Neck Surgery, 1 University of New Mexico, MSC10 5610, Albuquerque, NM, 87131, USA.
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Rokhshad R, Salehi SN, Yavari A, Shobeiri P, Esmaeili M, Manila N, Motamedian SR, Mohammad-Rahimi H. Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis. Oral Radiol 2024; 40:1-20. [PMID: 37855976 DOI: 10.1007/s11282-023-00715-5] [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: 05/09/2023] [Accepted: 09/23/2023] [Indexed: 10/20/2023]
Abstract
PURPOSE This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radiographic data. METHODS Through January 2023, a PubMed, Scopus, Embase, Google Scholar, IEEE, and arXiv search were carried out. The inclusion criteria were implementing head and neck medical images (computed tomography (CT), positron emission tomography (PET), MRI, Planar scans, and panoramic X-ray) of human subjects with segmentation, object detection, and classification deep learning models for head and neck cancers. The risk of bias was rated with the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. For the meta-analysis diagnostic odds ratio (DOR) was calculated. Deeks' funnel plot was used to assess publication bias. MIDAS and Metandi packages were used to analyze diagnostic test accuracy in STATA. RESULTS From 1967 studies, 32 were found eligible after the search and screening procedures. According to the QUADAS-2 tool, 7 included studies had a low risk of bias for all domains. According to the results of all included studies, the accuracy varied from 82.6 to 100%. Additionally, specificity ranged from 66.6 to 90.1%, sensitivity from 74 to 99.68%. Fourteen studies that provided sufficient data were included for meta-analysis. The pooled sensitivity was 90% (95% CI 0.820.94), and the pooled specificity was 92% (CI 95% 0.87-0.96). The DORs were 103 (27-251). Publication bias was not detected based on the p-value of 0.75 in the meta-analysis. CONCLUSION With a head and neck screening deep learning model, detectable screening processes can be enhanced with high specificity and sensitivity.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
| | - Seyyede Niloufar Salehi
- Executive Secretary of Research Committee, Board Director of Scientific Society, Dental Faculty, Azad University, Tehran, Iran
| | - Amirmohammad Yavari
- Student Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Mahdieh Esmaeili
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Nisha Manila
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
- Department of Diagnostic Sciences, Louisiana State University Health Science Center School of Dentistry, Louisiana, USA
| | - Saeed Reza Motamedian
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany.
- Dentofacial Deformities Research Center, Research Institute of Dental, Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjou Blvd, Tehran, Iran.
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
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Michelutti L, Tel A, Zeppieri M, Ius T, Sembronio S, Robiony M. The Use of Artificial Intelligence Algorithms in the Prognosis and Detection of Lymph Node Involvement in Head and Neck Cancer and Possible Impact in the Development of Personalized Therapeutic Strategy: A Systematic Review. J Pers Med 2023; 13:1626. [PMID: 38138853 PMCID: PMC10745006 DOI: 10.3390/jpm13121626] [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: 10/25/2023] [Revised: 11/17/2023] [Accepted: 11/18/2023] [Indexed: 12/24/2023] Open
Abstract
Given the increasingly important role that the use of artificial intelligence algorithms is taking on in the medical field today (especially in oncology), the purpose of this systematic review is to analyze the main reports on such algorithms applied for the prognostic evaluation of patients with head and neck malignancies. The objective of this paper is to examine the currently available literature in the field of artificial intelligence applied to head and neck oncology, particularly in the prognostic evaluation of the patient with this kind of tumor, by means of a systematic review. The paper exposes an overview of the applications of artificial intelligence in deriving prognostic information related to the prediction of survival and recurrence and how these data may have a potential impact on the choice of therapeutic strategy, making it increasingly personalized. This systematic review was written following the PRISMA 2020 guidelines.
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Affiliation(s)
- Luca Michelutti
- Clinic of Maxillofacial Surgery, Head-Neck and NeuroScience Department, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy (A.T.)
| | - Alessandro Tel
- Clinic of Maxillofacial Surgery, Head-Neck and NeuroScience Department, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy (A.T.)
| | - Marco Zeppieri
- Department of Ophthalmology, University Hospital of Udine, Piazzale S. Maria della Misericordia 15, 33100 Udine, Italy
| | - Tamara Ius
- Neurosurgery Unit, Head-Neck and NeuroScience Department, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
| | - Salvatore Sembronio
- Clinic of Maxillofacial Surgery, Head-Neck and NeuroScience Department, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy (A.T.)
| | - Massimo Robiony
- Clinic of Maxillofacial Surgery, Head-Neck and NeuroScience Department, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy (A.T.)
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Henson CE, Abou-Foul AK, Morton DJ, McDowell L, Baliga S, Bates J, Lee A, Bonomo P, Szturz P, Nankivell P, Huang SH, Lydiatt WM, O’Sullivan B, Mehanna H. Diagnostic challenges and prognostic implications of extranodal extension in head and neck cancer: a state of the art review and gap analysis. Front Oncol 2023; 13:1263347. [PMID: 37799466 PMCID: PMC10548228 DOI: 10.3389/fonc.2023.1263347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Extranodal extension (ENE) is a pattern of cancer growth from within the lymph node (LN) outward into perinodal tissues, critically defined by disruption and penetration of the tumor through the entire thickness of the LN capsule. The presence of ENE is often associated with an aggressive cancer phenotype in various malignancies including head and neck squamous cell carcinoma (HNSCC). In HNSCC, ENE is associated with increased risk of distant metastasis and lower rates of locoregional control. ENE detected on histopathology (pathologic ENE; pENE) is now incorporated as a risk-stratification factor in human papillomavirus (HPV)-negative HNSCC in the eighth edition of the American Joint Committee on Cancer (AJCC) and the Union for International Cancer Control (UICC) TNM classification. Although ENE was first described almost a century ago, several issues remain unresolved, including lack of consensus on definitions, terminology, and widely accepted assessment criteria and grading systems for both pENE and ENE detected on radiological imaging (imaging-detected ENE; iENE). Moreover, there is conflicting data on the prognostic significance of iENE and pENE, particularly in the context of HPV-associated HNSCC. Herein, we review the existing literature on ENE in HNSCC, highlighting areas of controversy and identifying critical gaps requiring concerted research efforts.
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Affiliation(s)
- Christina E. Henson
- Department of Radiation Oncology and Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Ahmad K. Abou-Foul
- Institute of Head and Neck Studies and Education, School of Cancer Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Daniel J. Morton
- Department of Pediatrics and Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Lachlan McDowell
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Sujith Baliga
- Department of Radiation Oncology, Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - James Bates
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, United States
| | - Anna Lee
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Pierluigi Bonomo
- Department of Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Petr Szturz
- Department of Oncology, University of Lausanne and Lausanne University Hospital, Lausanne, Switzerland
| | - Paul Nankivell
- Institute of Head and Neck Studies and Education, School of Cancer Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Shao Hui Huang
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada
| | - William M. Lydiatt
- Department of Surgery, Creighton University, and Nebraska Methodist Health System, Omaha, NE, United States
| | - Brian O’Sullivan
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada
| | - Hisham Mehanna
- Institute of Head and Neck Studies and Education, School of Cancer Sciences, University of Birmingham, Birmingham, United Kingdom
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Huang TT, Lin YC, Yen CH, Lan J, Yu CC, Lin WC, Chen YS, Wang CK, Huang EY, Ho SY. Prediction of extranodal extension in head and neck squamous cell carcinoma by CT images using an evolutionary learning model. Cancer Imaging 2023; 23:84. [PMID: 37700385 PMCID: PMC10496246 DOI: 10.1186/s40644-023-00601-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 08/08/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Extranodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC) correlates to poor prognoses and influences treatment strategies. Deep learning may yield promising performance of predicting ENE in HNSCC but lack of transparency and interpretability. This work proposes an evolutionary learning method, called EL-ENE, to establish a more interpretable ENE prediction model for aiding clinical diagnosis. METHODS There were 364 HNSCC patients who underwent neck lymph node (LN) dissection with pre-operative contrast-enhanced computerized tomography images. All the 778 LNs were divided into training and test sets with the ratio 8:2. EL-ENE uses an inheritable bi-objective combinatorial genetic algorithm for optimal feature selection and parameter setting of support vector machine. The diagnostic performances of the ENE prediction model and radiologists were compared using independent test datasets. RESULTS The EL-ENE model achieved the test accuracy of 80.00%, sensitivity of 81.13%, and specificity of 79.44% for ENE detection. The three radiologists achieved the mean diagnostic accuracy of 70.4%, sensitivity of 75.6%, and specificity of 67.9%. The features of gray-level texture and 3D morphology of LNs played essential roles in predicting ENE. CONCLUSIONS The EL-ENE method provided an accurate, comprehensible, and robust model to predict ENE in HNSCC with interpretable radiomic features for expanding clinical knowledge. The proposed transparent prediction models are more trustworthy and may increase their acceptance in daily clinical practice.
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Affiliation(s)
- Tzu-Ting Huang
- Department of Radiation Oncology and Proton & Radiation Therapy Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 129, Dapi Road, Niaosong District, Kaohsiung, Taiwan
- Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, No. 1001 University Road, Hsinchu, Taiwan
| | - Yi-Chen Lin
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, No. 75 Po- Ai Street, Hsinchu, Taiwan
| | - Chia-Heng Yen
- Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, No. 1001 University Road, Hsinchu, Taiwan
| | - Jui Lan
- Department of Anatomic Pathology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Dapi Road, Niaosong District, Kaohsiung, Taiwan
| | - Chiun-Chieh Yu
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Dapi Road, Niaosong District, Kaohsiung, Taiwan
| | - Wei-Che Lin
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Dapi Road, Niaosong District, Kaohsiung, Taiwan
| | - Yueh-Shng Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Dapi Road, Niaosong District, Kaohsiung, Taiwan
| | - Cheng-Kang Wang
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Dapi Road, Niaosong District, Kaohsiung, Taiwan
| | - Eng-Yen Huang
- Department of Radiation Oncology and Proton & Radiation Therapy Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 129, Dapi Road, Niaosong District, Kaohsiung, Taiwan.
- School of Medicine, College of Medicine, National Sun Yat-sen University, No. 70, Lienhai Rd, 80424, Kaohsiung, Taiwan.
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, No. 75 Po- Ai Street, Hsinchu, Taiwan.
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, No. 1001 University Road, Hsinchu, Taiwan.
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS 2 B), National Yang Ming Chiao Tung University, No. 75 Po-Ai Street, Hsinchu, Taiwan.
- College of Health Sciences, Kaohsiung Medical University, No. 100, Shih-Chuan 1st Road, Sanmin District, Kaohsiung, Taiwan.
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20
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Kann BH, Likitlersuang J, Bontempi D, Ye Z, Aneja S, Bakst R, Kelly HR, Juliano AF, Payabvash S, Guenette JP, Uppaluri R, Margalit DN, Schoenfeld JD, Tishler RB, Haddad R, Aerts HJWL, Garcia JJ, Flamand Y, Subramaniam RM, Burtness BA, Ferris RL. Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial. Lancet Digit Health 2023; 5:e360-e369. [PMID: 37087370 PMCID: PMC10245380 DOI: 10.1016/s2589-7500(23)00046-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/18/2023] [Accepted: 02/21/2023] [Indexed: 04/24/2023]
Abstract
BACKGROUND Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. ECOG-ACRIN Cancer Research Group E3311 was a multicentre trial wherein patients with HPV-associated oropharyngeal carcinoma were treated surgically and assigned to a pathological risk-based adjuvant strategy of observation, radiation, or concurrent chemoradiation. Despite protocol exclusion of patients with overt radiographic ENE, more than 30% had pathological ENE and required postoperative chemoradiation. We aimed to evaluate a CT-based deep learning algorithm for prediction of ENE in E3311, a diagnostically challenging cohort wherein algorithm use would be impactful in guiding decision-making. METHODS For this retrospective evaluation of deep learning algorithm performance, we obtained pretreatment CTs and corresponding surgical pathology reports from the multicentre, randomised de-escalation trial E3311. All enrolled patients on E3311 required pretreatment and diagnostic head and neck imaging; patients with radiographically overt ENE were excluded per study protocol. The lymph node with largest short-axis diameter and up to two additional nodes were segmented on each scan and annotated for ENE per pathology reports. Deep learning algorithm performance for ENE prediction was compared with four board-certified head and neck radiologists. The primary endpoint was the area under the curve (AUC) of the receiver operating characteristic. FINDINGS From 178 collected scans, 313 nodes were annotated: 71 (23%) with ENE in general, 39 (13%) with ENE larger than 1 mm ENE. The deep learning algorithm AUC for ENE classification was 0·86 (95% CI 0·82-0·90), outperforming all readers (p<0·0001 for each). Among radiologists, there was high variability in specificity (43-86%) and sensitivity (45-96%) with poor inter-reader agreement (κ 0·32). Matching the algorithm specificity to that of the reader with highest AUC (R2, false positive rate 22%) yielded improved sensitivity to 75% (+ 13%). Setting the algorithm false positive rate to 30% yielded 90% sensitivity. The algorithm showed improved performance compared with radiologists for ENE larger than 1 mm (p<0·0001) and in nodes with short-axis diameter 1 cm or larger. INTERPRETATION The deep learning algorithm outperformed experts in predicting pathological ENE on a challenging cohort of patients with HPV-associated oropharyngeal carcinoma from a randomised clinical trial. Deep learning algorithms should be evaluated prospectively as a treatment selection tool. FUNDING ECOG-ACRIN Cancer Research Group and the National Cancer Institute of the US National Institutes of Health.
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Affiliation(s)
- Benjamin H Kann
- Department of Radiation Oncology, Harvard Medical School, Boston, MA, USA; Mass General Brigham Artificial Intelligence in Medicine Program, Boston, MA, USA.
| | - Jirapat Likitlersuang
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Mass General Brigham Artificial Intelligence in Medicine Program, Boston, MA, USA
| | - Dennis Bontempi
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Mass General Brigham Artificial Intelligence in Medicine Program, Boston, MA, USA
| | - Zezhong Ye
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Mass General Brigham Artificial Intelligence in Medicine Program, Boston, MA, USA
| | - Sanjay Aneja
- Department of Therapeutic Radiology, New Haven, CT, USA
| | - Richard Bakst
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Amy F Juliano
- Mass Eye and Ear, Mass General Hospital, Boston, MA, USA
| | | | - Jeffrey P Guenette
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ravindra Uppaluri
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Danielle N Margalit
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jonathan D Schoenfeld
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Roy B Tishler
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert Haddad
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Mass General Brigham Artificial Intelligence in Medicine Program, Boston, MA, USA; Department of Radiology, Maastricht University, Maastricht, Netherlands
| | | | - Yael Flamand
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, ECOG-ACRIN Biostatistics Center, Boston, MA, USA
| | - Rathan M Subramaniam
- Department of Radiology and Nuclear Medicine, University of Notre Dame Australia, Sydney, NSW, Australia; Department of Radiology, Duke University, Durham, NC, USA
| | | | - Robert L Ferris
- Department of Otolaryngology, University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
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21
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Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics (Basel) 2022; 13:110. [PMID: 36611402 PMCID: PMC9818323 DOI: 10.3390/diagnostics13010110] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.
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Affiliation(s)
- Kuo Feng Hung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lun M. Wong
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Dion Tik Shun Li
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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22
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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: 16] [Impact Index Per Article: 5.3] [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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23
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Performance of Artificial Intelligence Models Designed for Diagnosis, Treatment Planning and Predicting Prognosis of Orthognathic Surgery (OGS)—A Scoping Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The technological advancements in the field of medical science have led to an escalation in the development of artificial intelligence (AI) applications, which are being extensively used in health sciences. This scoping review aims to outline the application and performance of artificial intelligence models used for diagnosing, treatment planning and predicting the prognosis of orthognathic surgery (OGS). Data for this paper was searched through renowned electronic databases such as PubMed, Google Scholar, Scopus, Web of science, Embase and Cochrane for articles related to the research topic that have been published between January 2000 and February 2022. Eighteen articles that met the eligibility criteria were critically analyzed based on QUADAS-2 guidelines and the certainty of evidence of the included studies was assessed using the GRADE approach. AI has been applied for predicting the post-operative facial profiles and facial symmetry, deciding on the need for OGS, predicting perioperative blood loss, planning OGS, segmentation of maxillofacial structures for OGS, and differential diagnosis of OGS. AI models have proven to be efficient and have outperformed the conventional methods. These models are reported to be reliable and reproducible, hence they can be very useful for less experienced practitioners in clinical decision making and in achieving better clinical outcomes.
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24
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Tomita H, Yamashiro T, Iida G, Tsubakimoto M, Mimura H, Murayama S. Radiomics analysis for differentiating of cervical lymphadenopathy between cancer of unknown primary and malignant lymphoma on unenhanced computed tomography. NAGOYA JOURNAL OF MEDICAL SCIENCE 2022; 84:269-285. [PMID: 35967951 PMCID: PMC9350581 DOI: 10.18999/nagjms.84.2.269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 08/03/2021] [Indexed: 12/03/2022]
Abstract
To investigate the usefulness of texture analysis to discriminate between cervical lymph node (LN) metastasis from cancer of unknown primary (CUP) and cervical LN involvement of malignant lymphoma (ML) on unenhanced computed tomography (CT). Cervical LN metastases in 17 patients with CUP and cervical LN involvement in 17 patients with ML were assessed by 18F-FDG PET/CT. The texture features were obtained in the total cross-sectional area (CSA) of the targeted LN, following the contour of the largest cervical LN on unenhanced CT. Values for the max standardized uptake value (SUVmax) and the mean SUV value (SUVmean), and 34 texture features were compared using a Mann-Whitney U test. The diagnostic accuracy and area under the curve (AUC) of the combination of the texture features were evaluated by support vector machine (SVM) with nested cross-validation. The SUVmax and SUVmean did not differ significantly between cervical LN metastases from CUP and cervical LN involvement from ML. However, significant differences of 9 texture features of the total CSA were observed (p = 0.001 - 0.05). The best AUC value of 0.851 for the texture feature of the total CSA were obtained from the correlation in the gray-level co-occurrence matrix features. SVM had the best AUC and diagnostic accuracy of 0.930 and 84.8%. Radiomics analysis appears to be useful for differentiating cervical LN metastasis from CUP and cervical LN involvement of ML on unenhanced CT.
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Affiliation(s)
- Hayato Tomita
- Department of Radiology, University of the Ryukyus Graduate School of Medicine, Nishihara, Japan
,Department of Radiology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Tsuneo Yamashiro
- Department of Radiology, University of the Ryukyus Graduate School of Medicine, Nishihara, Japan
| | - Gyo Iida
- Department of Radiology, University of the Ryukyus Graduate School of Medicine, Nishihara, Japan
| | - Maho Tsubakimoto
- Department of Radiology, University of the Ryukyus Graduate School of Medicine, Nishihara, Japan
| | - Hidefumi Mimura
- Department of Radiology, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Sadayuki Murayama
- Department of Radiology, University of the Ryukyus Graduate School of Medicine, Nishihara, Japan
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25
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Yang SY, Li SH, Liu JL, Sun XQ, Cen YY, Ren RY, Ying SC, Chen Y, Zhao ZH, Liao W. Histopathology-Based Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning. J Dent Res 2022; 101:1321-1327. [PMID: 35446176 DOI: 10.1177/00220345221089858] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Oral squamous cell carcinoma (OSCC) is prevalent around the world and is associated with poor prognosis. OSCC is typically diagnosed from tissue biopsy sections by pathologists who rely on their empirical experience. Deep learning models may improve the accuracy and speed of image classification, thus reducing human error and workload. Here we developed a custom-made deep learning model to assist pathologists in detecting OSCC from histopathology images. We collected and analyzed a total of 2,025 images, among which 1,925 images were included in the training set and 100 images were included in the testing set. Our model was able to automatically evaluate these images and arrive at a diagnosis with a sensitivity of 0.98, specificity of 0.92, positive predictive value of 0.924, negative predictive value of 0.978, and F1 score of 0.951. Using a subset of 100 images, we examined whether our model could improve the diagnostic performance of junior and senior pathologists. We found that junior pathologists were able to delineate OSCC in these images 6.26 min faster when assisted by the model than when working alone. When the clinicians were assisted by the model, their average F1 score improved from 0.9221 to 0.9566 in the case of junior pathologists and from 0.9361 to 0.9463 in the case of senior pathologists. Our findings indicate that deep learning can improve the accuracy and speed of OSCC diagnosis from histopathology images.
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Affiliation(s)
- S Y Yang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - S H Li
- National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - J L Liu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - X Q Sun
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Y Cen
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - R Y Ren
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - S C Ying
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Y Chen
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Z H Zhao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - W Liao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
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26
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Patil S, Albogami S, Hosmani J, Mujoo S, Kamil MA, Mansour MA, Abdul HN, Bhandi S, Ahmed SSSJ. Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls. Diagnostics (Basel) 2022; 12:diagnostics12051029. [PMID: 35626185 PMCID: PMC9139975 DOI: 10.3390/diagnostics12051029] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/12/2022] [Accepted: 04/18/2022] [Indexed: 12/19/2022] Open
Abstract
Background: Machine learning (ML) is a key component of artificial intelligence (AI). The terms machine learning, artificial intelligence, and deep learning are erroneously used interchangeably as they appear as monolithic nebulous entities. This technology offers immense possibilities and opportunities to advance diagnostics in the field of medicine and dentistry. This necessitates a deep understanding of AI and its essential components, such as machine learning (ML), artificial neural networks (ANN), and deep learning (DP). Aim: This review aims to enlighten clinicians regarding AI and its applications in the diagnosis of oral diseases, along with the prospects and challenges involved. Review results: AI has been used in the diagnosis of various oral diseases, such as dental caries, maxillary sinus diseases, periodontal diseases, salivary gland diseases, TMJ disorders, and oral cancer through clinical data and diagnostic images. Larger data sets would enable AI to predict the occurrence of precancerous conditions. They can aid in population-wide surveillance and decide on referrals to specialists. AI can efficiently detect microfeatures beyond the human eye and augment its predictive power in critical diagnosis. Conclusion: Although studies have recognized the benefit of AI, the use of artificial intelligence and machine learning has not been integrated into routine dentistry. AI is still in the research phase. The coming decade will see immense changes in diagnosis and healthcare built on the back of this research. Clinical significance: This paper reviews the various applications of AI in dentistry and illuminates the shortcomings faced while dealing with AI research and suggests ways to tackle them. Overcoming these pitfalls will aid in integrating AI seamlessly into dentistry.
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Affiliation(s)
- Shankargouda Patil
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
- Correspondence:
| | - Sarah Albogami
- Department of Biotechnology, College of Science, Taif University, Taif 21944, Saudi Arabia;
| | - Jagadish Hosmani
- Department of Diagnostic Dental Sciences, Oral Pathology Division, Faculty of Dentistry, College of Dentistry, King Khalid University, Abha 61411, Saudi Arabia;
| | - Sheetal Mujoo
- Division of Oral Medicine & Radiology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Mona Awad Kamil
- Department of Preventive Dental Science, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Manawar Ahmad Mansour
- Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (M.A.M.); (H.N.A.)
| | - Hina Naim Abdul
- Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (M.A.M.); (H.N.A.)
| | - Shilpa Bhandi
- Department of Restorative Dental Sciences, Division of Operative Dentistry, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Shiek S. S. J. Ahmed
- Multi-Omics and Drug Discovery Lab, Chettinad Academy of Research and Education, Chennai 600130, India;
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27
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Elakkiya R, Subramaniyaswamy V, Vijayakumar V, Mahanti A. Cervical Cancer Diagnostics Healthcare System Using Hybrid Object Detection Adversarial Networks. IEEE J Biomed Health Inform 2022; 26:1464-1471. [PMID: 34214045 DOI: 10.1109/jbhi.2021.3094311] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Cervical cancer is one of the common cancers among women and it causes significant mortality in many developing countries. Diagnosis of cervical lesions is done using pap smear test or visual inspection using acetic acid (staining). Digital colposcopy, an inexpensive methodology, provides painless and efficient screening results. Therefore, automating cervical cancer screening using colposcopy images will be highly useful in saving many lives. Nowadays, many automation techniques using computer vision and machine learning in cervical screening gained attention, paving the way for diagnosing cervical cancer. However, most of the methods rely entirely on the annotation of cervical spotting and segmentation. This paper aims to introduce the Faster Small-Object Detection Neural Networks (FSOD-GAN) to address the cervical screening and diagnosis of cervical cancer and the type of cancer using digital colposcopy images. The proposed approach automatically detects the cervical spot using Faster Region-Based Convolutional Neural Network (FR-CNN) and performs the hierarchical multiclass classification of three types of cervical cancer lesions. Experimentation was done with colposcopy data collected from available open sources consisting of 1,993 patients with three cervical categories, and the proposed approach shows 99% accuracy in diagnosing the stages of cervical cancer.
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28
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Tomita H, Kobayashi T, Takaya E, Mishiro S, Hirahara D, Fujikawa A, Kurihara Y, Mimura H, Kobayashi Y. Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study. Eur Radiol 2022; 32:5353-5361. [PMID: 35201406 DOI: 10.1007/s00330-022-08630-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 01/15/2022] [Accepted: 02/02/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES This preliminary study aimed to develop a deep learning (DL) model using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to predict local recurrence and 2-year progression-free survival (PFS) in laryngeal and hypopharyngeal cancer patients treated with various forms of radiotherapy-related curative therapy. METHODS Seventy patients with laryngeal and hypopharyngeal cancers treated by radiotherapy, chemoradiotherapy, or induction-(chemo)radiotherapy were enrolled and divided into training (N = 49) and test (N = 21) groups based on presentation timeline. All patients underwent MR before and 4 weeks after the start of radiotherapy. The DL models that extracted imaging features on pre- and intra-treatment DWI and ADC maps were trained to predict the local recurrence within a 2-year follow-up. In the test group, each DL model was analyzed for recurrence prediction. Additionally, the Kaplan-Meier and multivariable Cox regression analyses were performed to evaluate the prognostic significance of the DL models and clinical variables. RESULTS The highest area under the receiver operating characteristics curve and accuracy for predicting the local recurrence in the DL model were 0.767 and 81.0%, respectively, using intra-treatment DWI (DWIintra). The log-rank test showed that DWIintra was significantly associated with PFS (p = 0.013). DWIintra was an independent prognostic factor for PFS in multivariate analysis (p = 0.023). CONCLUSION DL models using DWIintra may have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. The model-related findings may contribute to determining the therapeutic strategy in the early stage of the treatment. KEY POINTS • Deep learning models using intra-treatment diffusion-weighted imaging have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. • The findings from these models may contribute to determining the therapeutic strategy at the early stage of the treatment.
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Affiliation(s)
- Hayato Tomita
- Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan.
| | - Tatsuaki Kobayashi
- Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Eichi Takaya
- School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan
| | - Sono Mishiro
- Department of AI Research Lab, Harada Academy, 2-54-4, Higashitaniyama, Kagoshima, Kagoshima, 891-0113, Japan
| | - Daisuke Hirahara
- Department of AI Research Lab, Harada Academy, 2-54-4, Higashitaniyama, Kagoshima, Kagoshima, 891-0113, Japan
| | - Atsuko Fujikawa
- Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Yoshiko Kurihara
- Department of Radiology, Machida Municipal Hospital, 2-15-41 Asahi-cho, Machida, Tokyo, 194-0023, Japan
| | - Hidefumi Mimura
- Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
| | - Yasuyuki Kobayashi
- Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan
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Alabi RO, Bello IO, Youssef O, Elmusrati M, Mäkitie AA, Almangush A. Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review. FRONTIERS IN ORAL HEALTH 2022; 2:686863. [PMID: 35048032 PMCID: PMC8757862 DOI: 10.3389/froh.2021.686863] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/15/2021] [Indexed: 12/17/2022] Open
Abstract
The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases-PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.,Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ibrahim O Bello
- Department of Oral Medicine and Diagnostic Science, College of Dentistry, King Saud University, Riyadh, Saudi Arabia
| | - Omar Youssef
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Pathology, University of Helsinki, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - 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 Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Pathology, University of Helsinki, Helsinki, Finland.,Institute of Biomedicine, Pathology, University of Turku, Turku, Finland.,Faculty of Dentistry, University of Misurata, Misurata, Libya
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30
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Alabi RO, Almangush A, Elmusrati M, Mäkitie AA. Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine. FRONTIERS IN ORAL HEALTH 2022; 2:794248. [PMID: 35088057 PMCID: PMC8786902 DOI: 10.3389/froh.2021.794248] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/13/2021] [Indexed: 12/21/2022] Open
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.
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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
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki, Helsinki, Finland
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - 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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31
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Nozawa M, Ito H, Ariji Y, Fukuda M, Igarashi C, Nishiyama M, Ogi N, Katsumata A, Kobayashi K, Ariji E. Automatic segmentation of the temporomandibular joint disc on magnetic resonance images using a deep learning technique. Dentomaxillofac Radiol 2022; 51:20210185. [PMID: 34347537 PMCID: PMC8693319 DOI: 10.1259/dmfr.20210185] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVES The aims of the present study were to construct a deep learning model for automatic segmentation of the temporomandibular joint (TMJ) disc on magnetic resonance (MR) images, and to evaluate the performances using the internal and external test data. METHODS In total, 1200 MR images of closed and open mouth positions in patients with temporomandibular disorder (TMD) were collected from two hospitals (Hospitals A and B). The training and validation data comprised 1000 images from Hospital A, which were used to create a segmentation model. The performance was evaluated using 200 images from Hospital A (internal validity test) and 200 images from Hospital B (external validity test). RESULTS Although the analysis of performance determined with data from Hospital B showed low recall (sensitivity), compared with the performance determined with data from Hospital A, both performances were above 80%. Precision (positive predictive value) was lower when test data from Hospital A were used for the position of anterior disc displacement. According to the intra-articular TMD classification, the proportions of accurately assigned TMJs were higher when using images from Hospital A than when using images from Hospital B. CONCLUSION The segmentation deep learning model created in this study may be useful for identifying disc positions on MR images.
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Affiliation(s)
| | - Hirokazu Ito
- Department of Oral and Maxillofacial Radiology, Tsurumi University School of Dentistry, Yokohama, Japan
| | - Yoshiko Ariji
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan
| | - Motoki Fukuda
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan
| | - Chinami Igarashi
- Department of Oral and Maxillofacial Radiology, Tsurumi University School of Dentistry, Yokohama, Japan
| | - Masako Nishiyama
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan
| | - Nobumi Ogi
- Department of Oral and Maxillofacial Surgery, Aichi Gakuin University School of Dentistry, Nagoya, Japan
| | - Akitoshi Katsumata
- Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan
| | - Kaoru Kobayashi
- Department of Oral and Maxillofacial Radiology, Tsurumi University School of Dentistry, Yokohama, Japan
| | - Eiichiro Ariji
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan
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32
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Yan KX, Liu L, Li H. Application of machine learning in oral and maxillofacial surgery. Artif Intell Med Imaging 2021; 2:104-114. [DOI: 10.35711/aimi.v2.i6.104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 02/06/2023] Open
Abstract
Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.
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Affiliation(s)
- Kai-Xin Yan
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Lei Liu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hui Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
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33
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Chu CS, Lee NP, Ho JWK, Choi SW, Thomson PJ. Deep Learning for Clinical Image Analyses in Oral Squamous Cell Carcinoma: A Review. JAMA Otolaryngol Head Neck Surg 2021; 147:893-900. [PMID: 34410314 DOI: 10.1001/jamaoto.2021.2028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Importance Oral squamous cell carcinoma (SCC) is a lethal malignant neoplasm with a high rate of tumor metastasis and recurrence. Accurate diagnosis, prognosis prediction, and metastasis detection can improve patient outcomes. Deep learning for clinical image analysis can be used for diagnosis and prognosis in cancers, including oral SCC; its use in these areas can improve patient care and outcome. Observations This review is a summary of the use of deep learning models for diagnosis, prognosis, and metastasis detection for oral SCC by analyzing information from pathological and radiographic images. Specifically, deep learning has been used to classify different cell types, to differentiate cancer cells from nonmalignant cells, and to identify oral SCC from other cancer types. It can also be used to predict survival, to differentiate between tumor grades, and to detect lymph node metastasis. In general, the performance of these deep learning models has an accuracy ranging from 77.89% to 97.51% and 76% to 94.2% with the use of pathological and radiographic images, respectively. The review also discusses the importance of using good-quality clinical images in sufficient quantity on model performance. Conclusions and Relevance Applying pathological and radiographic images in deep learning models for diagnosis and prognosis of oral SCC has been explored, and most studies report results showing good classification accuracy. The successful use of deep learning in these areas has a high clinical translatability in the improvement of patient care.
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Affiliation(s)
- Chui Shan Chu
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Nikki P Lee
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.,Laboratory of Data Discovery for Health Limited (D 2 4H), Hong Kong Science Park, Hong Kong SAR, China
| | - Siu-Wai Choi
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Peter J Thomson
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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34
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Preliminary Study on the Diagnostic Performance of a Deep Learning System for Submandibular Gland Inflammation Using Ultrasonography Images. J Clin Med 2021; 10:jcm10194508. [PMID: 34640523 PMCID: PMC8509623 DOI: 10.3390/jcm10194508] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 09/24/2021] [Accepted: 09/28/2021] [Indexed: 11/24/2022] Open
Abstract
This study was performed to evaluate the diagnostic performance of deep learning systems using ultrasonography (USG) images of the submandibular glands (SMGs) in three different conditions: obstructive sialoadenitis, Sjögren’s syndrome (SjS), and normal glands. Fifty USG images with a confirmed diagnosis of obstructive sialoadenitis, 50 USG images with a confirmed diagnosis of SjS, and 50 USG images with no SMG abnormalities were included in the study. The training group comprised 40 obstructive sialoadenitis images, 40 SjS images, and 40 control images, and the test group comprised 10 obstructive sialoadenitis images, 10 SjS images, and 10 control images for deep learning analysis. The performance of the deep learning system was calculated and compared between two experienced radiologists. The sensitivity of the deep learning system in the obstructive sialoadenitis group, SjS group, and control group was 55.0%, 83.0%, and 73.0%, respectively, and the total accuracy was 70.3%. The sensitivity of the two radiologists was 64.0%, 72.0%, and 86.0%, respectively, and the total accuracy was 74.0%. This study revealed that the deep learning system was more sensitive than experienced radiologists in diagnosing SjS in USG images of two case groups and a group of healthy subjects in inflammation of SMGs.
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35
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Adeoye J, Tan JY, Choi SW, Thomson P. Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review. Int J Med Inform 2021; 154:104557. [PMID: 34455119 DOI: 10.1016/j.ijmedinf.2021.104557] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES Machine learning platforms are now being introduced into modern oncological practice for classification and prediction of patient outcomes. To determine the current status of the application of these learning models as adjunctive decision-making tools in oral cavity cancer management, this systematic review aims to summarize the accuracy of machine-learning based models for disease outcomes. METHODS Electronic databases including PubMed, Scopus, EMBASE, Cochrane Library, LILACS, SciELO, PsychINFO, and Web of Science were searched up until December 21, 2020. Pertinent articles detailing the development and accuracy of machine learning prediction models for oral cavity cancer outcomes were selected in a two-stage process. Quality assessment was conducted using the Quality in Prognosis Studies (QUIPS) tool and results of base studies were qualitatively synthesized by all authors. Outcomes of interest were malignant transformation of precancer lesions, cervical lymph node metastasis, as well as treatment response, and prognosis of oral cavity cancer. RESULTS Twenty-seven articles out of 950 citations identified from electronic and manual searching were included in this study. Five studies had low bias concerns on the QUIPS tool. Prediction of malignant transformation, cervical lymph node metastasis, treatment response, and prognosis were reported in three, six, eight, and eleven articles respectively. Accuracy of these learning models on the internal or external validation sets ranged from 0.85 to 0.97 for malignant transformation prediction, 0.78-0.91 for cervical lymph node metastasis prediction, 0.64-1.00 for treatment response prediction, and 0.71-0.99 for prognosis prediction. In general, most trained algorithms predicting these outcomes performed better than alternate methods of prediction. We also found that models including molecular markers in training data had better accuracy estimates for malignant transformation, treatment response, and prognosis prediction. CONCLUSION Machine learning algorithms have a satisfactory to excellent accuracy for predicting three of four oral cavity cancer outcomes i.e., malignant transformation, nodal metastasis, and prognosis. However, considering the training approach of many available classifiers, these models may not be streamlined enough for clinical application currently.
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Affiliation(s)
- John Adeoye
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Jia Yan Tan
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region.
| | - Siu-Wai Choi
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region.
| | - Peter Thomson
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region
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Shivakumar N, Chandrashekar A, Handa AI, Lee R. Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review. Postgrad Med J 2021; 98:e20. [PMID: 33688072 DOI: 10.1136/postgradmedj-2020-139620] [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: 12/16/2020] [Revised: 02/08/2021] [Accepted: 02/20/2021] [Indexed: 11/16/2022]
Abstract
CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis. This review aims to provide an overview on the use of DL in CT image analysis in the diagnostic evaluation of metastatic disease. A total of 29 studies were included which could be grouped together into three areas of research: the use of deep learning on the detection of metastatic disease from CT imaging, characterisation of lesions on CT into metastasis and prediction of the presence or development of metastasis based on the primary tumour. In conclusion, DL in CT image analysis could have a potential role in evaluating metastatic disease; however, prospective clinical trials investigating its clinical value are required.
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Affiliation(s)
- Natesh Shivakumar
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Anirudh Chandrashekar
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Ashok Inderraj Handa
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Regent Lee
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
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Huang SH, Chernock R, O'Sullivan B, Fakhry C. Assessment Criteria and Clinical Implications of Extranodal Extension in Head and Neck Cancer. Am Soc Clin Oncol Educ Book 2021; 41:265-278. [PMID: 34010048 DOI: 10.1200/edbk_320939] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Tumor breaching the capsule of a lymph node is termed extranodal extension (ENE). It reflects aggressiveness of a tumor, creates anatomic challenges for disease clearance, and increases the risk of distant metastasis. Extranodal extension can be assessed on a pathology specimen, by radiology studies, and by clinical examination. Presence of ENE in a pathology specimen has long been considered a high-risk feature of disease progression and would ordinarily benefit from the addition of chemotherapy to adjuvant radiotherapy. Although the eighth edition of the Union for International Cancer Control/American Joint Committee on Cancer stage classification dichotomizes pathologic ENE according to its presence or absence, emerging evidence suggests that the extent of a pathologic ENE may provide additional value for risk stratification to guide adjuvant therapy. Recent data suggest that the prognostic importance of pathologic ENE is also applicable for HPV-associated head and neck squamous cell carcinoma. In addition, compelling data demonstrate that indisputable radiologic ENE is a powerful risk stratification tool to identify patients at high risk for treatment failure, especially distant metastasis, applicable for both HPV-positive and HPV-negative head and neck squamous cell carcinoma. However, the definition and taxonomy of radiologic ENE requires standardization. The goal of this review is to clarify the contemporary understanding of the prognostic implications of ENE in head and neck squamous cell carcinoma, present the nuances of what is presently known and unknown, and elucidate how to classify ENE pathologically and radiologically with an understanding of the strengths and weaknesses of each approach. Finally, with the development of several risk stratification methods, the relative role of ENE and other prognostic schema will be explored.
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Affiliation(s)
- Shao Hui Huang
- Department of Radiation Oncology and Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Centre/University of Toronto, Toronto, Ontario, Canada
| | - Rebecca Chernock
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO
| | - Brian O'Sullivan
- Department of Radiation Oncology and Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Centre/University of Toronto, Toronto, Ontario, Canada
| | - Carole Fakhry
- Otolaryngology-Head & Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD
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Tomita H, Yamashiro T, Heianna J, Nakasone T, Kobayashi T, Mishiro S, Hirahara D, Takaya E, Mimura H, Murayama S, Kobayashi Y. Deep Learning for the Preoperative Diagnosis of Metastatic Cervical Lymph Nodes on Contrast-Enhanced Computed ToMography in Patients with Oral Squamous Cell Carcinoma. Cancers (Basel) 2021; 13:cancers13040600. [PMID: 33546279 PMCID: PMC7913286 DOI: 10.3390/cancers13040600] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 01/23/2021] [Accepted: 01/31/2021] [Indexed: 12/04/2022] Open
Abstract
Simple Summary Cervical lymph node (LN) metastasis in patients with oral squamous cell carcinoma is one of the important prognostic factors. Pretreatment cervical nodal staging is performed using computed tomography (CT) as the first-line examination. However, imaging findings focused on morphology are not specific for detecting cervical LN metastasis. In this study, deep learning (DL) analysis of pretreatment contrast-enhanced CT was evaluated and compared with radiologists’ assessments at levels I–II, I, and II using the independent test set. The DL model achieved higher diagnostic performance in discriminating between benign and metastatic cervical LNs at levels I–II, I, and II. Significant difference in the area under the curves of the DL model and the radiologists’ assessments at levels I–II and II were observed. Our findings suggest that this approach can provide additional value to treatment strategies. Abstract We investigated the value of deep learning (DL) in differentiating between benign and metastatic cervical lymph nodes (LNs) using pretreatment contrast-enhanced computed tomography (CT). This retrospective study analyzed 86 metastatic and 234 benign (non-metastatic) cervical LNs at levels I–V in 39 patients with oral squamous cell carcinoma (OSCC) who underwent preoperative CT and neck dissection. LNs were randomly divided into training (70%), validation (10%), and test (20%) sets. For the validation and test sets, cervical LNs at levels I–II were evaluated. Convolutional neural network analysis was performed using Xception architecture. Two radiologists evaluated the possibility of metastasis to cervical LNs using a 4-point scale. The area under the curve of the DL model and the radiologists’ assessments were calculated and compared at levels I–II, I, and II. In the test set, the area under the curves at levels I–II (0.898) and II (0.967) were significantly higher than those of each reader (both, p < 0.05). DL analysis of pretreatment contrast-enhanced CT can help classify cervical LNs in patients with OSCC with better diagnostic performance than radiologists’ assessments alone. DL may be a valuable diagnostic tool for differentiating between benign and metastatic cervical LNs.
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Affiliation(s)
- Hayato Tomita
- Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa 216-8511, Japan;
- Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa 903-0215, Japan; (T.Y.); (J.H.); (S.M.)
- Correspondence: ; Tel.: +81-44-977-8111
| | - Tsuneo Yamashiro
- Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa 903-0215, Japan; (T.Y.); (J.H.); (S.M.)
| | - Joichi Heianna
- Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa 903-0215, Japan; (T.Y.); (J.H.); (S.M.)
| | - Toshiyuki Nakasone
- Department of Oral and Maxillofacial Surgery, Graduate School of Medical Science, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa 903-0215, Japan;
| | - Tatsuaki Kobayashi
- Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa 216-8511, Japan; (T.K.); (Y.K.)
| | - Sono Mishiro
- Department of AI Research Lab, Harada Academy, 2-54-4 Higashitaniyama, Kagoshima, Kagoshima 891-0113, Japan; (S.M.); (D.H.)
| | - Daisuke Hirahara
- Department of AI Research Lab, Harada Academy, 2-54-4 Higashitaniyama, Kagoshima, Kagoshima 891-0113, Japan; (S.M.); (D.H.)
| | - Eichi Takaya
- School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan;
| | - Hidefumi Mimura
- Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa 216-8511, Japan;
| | - Sadayuki Murayama
- Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa 903-0215, Japan; (T.Y.); (J.H.); (S.M.)
| | - Yasuyuki Kobayashi
- Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa 216-8511, Japan; (T.K.); (Y.K.)
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Pilar A, Yu E, Su J, O'Sullivan B, Bartlett E, Waldron JN, Ringash J, Spreafico A, Hansen AR, de Almeida J, Bayley A, Bratman SV, Cho J, Giuliani M, Hope A, Hosni A, Kim J, Tong L, Xu W, Huang SH. Prognostic value of clinical and radiologic extranodal extension and their role in the 8th edition TNM cN classification for HPV-negative oropharyngeal carcinoma. Oral Oncol 2021; 114:105167. [PMID: 33508706 DOI: 10.1016/j.oraloncology.2020.105167] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 11/26/2020] [Accepted: 12/24/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND/OBJECTIVES We evaluate the performance between the TNM-8 versus TNM-7 cN-classification and explore the relative prognostic contribution of radiologic extranodal extension (rENE) for HPV-negative oropharyngeal cancer (HPV-OPC). MATERIALS/METHODS All HPV- OPC treated with IMRT between 2005 and 2016 were included. cENE was defined as unambiguous "fixation" of a neck mass or "skin involvement" on clinical examination. rENE was recorded by re-reviewing pre-treatment CT/MR. Disease-free survival (DFS) stratified by cENE or rENE were compared. Multivariable analyses (MVA) calculated the adjusted hazard ratio (aHR) for the separate cENE and rENE attributes and their combination. A refined cN-category incorporating both cENE and rENE parameters was proposed. The performance of the revision was compared to TNM-8 and TNM-7. RESULTS Of 361 HPV- OPC, 97 were cN0 and 264 were cN+ with 48 cENE+ and 72 rENE+ respectively. Median follow-up was 5.4 years. The 3-year DFS was lower in cENE+ vs cENE-negative (cENE-) (23% vs 45%; aHR = 1.68, p = 0.008) and rENE+ vs rENE-negative (rENE-) patients (29% vs 45%; aHR = 1.44, p = 0.037). The cENE+/rENE+ subset had the worse DFS vs cENE-/rENE+ or cENE-/rENE- (24%/37%/46%, p = 0.005). We propose a refined cN-category wherein any cENE-/rENE+ case is reclassified one N-stratum higher while any cENE+ case remains cN3b. The stage schema with the refined N-categorization outperformed TNM-8, and both outperformed TNM-7. CONCLUSIONS cENE and rENE are both prognostic but the cENE+/rENE+ subset has the worst outcome. The TNM-8 cN-categories improves outcome prediction compared to the TNM-7. Incorporation of rENE into TNM-8 cN-categories may further augment performance.
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Affiliation(s)
- Avinash Pilar
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada
| | - Eugene Yu
- Department of Neuroradiology and Head and Neck Imaging, Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Jie Su
- Department of Biostatistics, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Brian O'Sullivan
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada; Department of Otolaryngology-Head & Neck Surgery, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Eric Bartlett
- Department of Neuroradiology and Head and Neck Imaging, Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - John N Waldron
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada; Department of Otolaryngology-Head & Neck Surgery, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Jolie Ringash
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada; Department of Otolaryngology-Head & Neck Surgery, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Anna Spreafico
- Division of Medical Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Aaron R Hansen
- Division of Medical Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - John de Almeida
- Department of Otolaryngology-Head & Neck Surgery, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Andrew Bayley
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Scott V Bratman
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - John Cho
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Meredith Giuliani
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Andrew Hope
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Ali Hosni
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - John Kim
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Li Tong
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Wei Xu
- Department of Biostatistics, The Princess Margaret Cancer Centre/University of Toronto, Canada.
| | - Shao Hui Huang
- Department of Radiation Oncology, The Princess Margaret Cancer Centre/University of Toronto, Canada; Department of Otolaryngology-Head & Neck Surgery, The Princess Margaret Cancer Centre/University of Toronto, Canada.
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Zheng Q, Yang L, Zeng B, Li J, Guo K, Liang Y, Liao G. Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis. EClinicalMedicine 2021; 31:100669. [PMID: 33392486 PMCID: PMC7773591 DOI: 10.1016/j.eclinm.2020.100669] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 11/14/2020] [Accepted: 11/17/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Early diagnosis of tumor metastasis is crucial for clinical treatment. Artificial intelligence (AI) has shown great promise in the field of medicine. We therefore aimed to evaluate the diagnostic accuracy of AI algorithms in detecting tumor metastasis using medical radiology imaging. METHODS We searched PubMed and Web of Science for studies published from January 1, 1997, to January 30, 2020. Studies evaluating an AI model for the diagnosis of tumor metastasis from medical images were included. We excluded studies that used histopathology images or medical wave-form data and those focused on the region segmentation of interest. Studies providing enough information to construct contingency tables were included in a meta-analysis. FINDINGS We identified 2620 studies, of which 69 were included. Among them, 34 studies were included in a meta-analysis with a pooled sensitivity of 82% (95% CI 79-84%), specificity of 84% (82-87%) and AUC of 0·90 (0·87-0·92). Analysis for different AI algorithms showed a pooled sensitivity of 87% (83-90%) for machine learning and 86% (82-89%) for deep learning, and a pooled specificity of 89% (82-93%) for machine learning, and 87% (82-91%) for deep learning. INTERPRETATION AI algorithms may be used for the diagnosis of tumor metastasis using medical radiology imaging with equivalent or even better performance to health-care professionals, in terms of sensitivity and specificity. At the same time, rigorous reporting standards with external validation and comparison to health-care professionals are urgently needed for AI application in the medical field. FUNDING College students' innovative entrepreneurial training plan program .
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Affiliation(s)
- Qiuhan Zheng
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Le Yang
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Bin Zeng
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Jiahao Li
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Kaixin Guo
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Yujie Liang
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Guiqing Liao
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
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Watanabe H, Ariji Y, Fukuda M, Kuwada C, Kise Y, Nozawa M, Sugita Y, Ariji E. Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study. Oral Radiol 2020; 37:487-493. [PMID: 32948938 DOI: 10.1007/s11282-020-00485-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 09/05/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVES This study aimed to examine the performance of deep learning object detection technology for detecting and identifying maxillary cyst-like lesions on panoramic radiography. METHODS Altogether, 412 patients with maxillary cyst-like lesions (including several benign tumors) were enrolled. All panoramic radiographs were arbitrarily assigned to the training, testing 1, and testing 2 datasets of the study. The deep learning process of the training images and labels was performed for 1000 epochs using the DetectNet neural network. The testing 1 and testing 2 images were applied to the created learning model, and the detection performance was evaluated. For lesions that could be detected, the classification performance (sensitivity) for identifying radicular cysts or other lesions were examined. RESULTS The recall, precision, and F-1 score for detecting maxillary cysts were 74.6%/77.1%, 89.8%/90.0%, and 81.5%/83.1% for the testing 1/testing 2 datasets, respectively. The recall was higher in the anterior regions and for radicular cysts. The sensitivity was higher for identifying radicular cysts than for other lesions. CONCLUSIONS Using deep learning object detection technology, maxillary cyst-like lesions could be detected in approximately 75-77%.
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Affiliation(s)
- Hirofumi Watanabe
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan
| | - Yoshiko Ariji
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
| | - Motoki Fukuda
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan
| | - Chiaki Kuwada
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan
| | - Yoshitaka Kise
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan
| | - Michihito Nozawa
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan
| | - Yoshihiko Sugita
- Department of Oral Pathology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan
| | - Eiichiro Ariji
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan
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Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, Sarode SC, Bhandi S. Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci 2020; 16:508-522. [PMID: 33384840 PMCID: PMC7770297 DOI: 10.1016/j.jds.2020.06.019] [Citation(s) in RCA: 238] [Impact Index Per Article: 47.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/19/2020] [Indexed: 01/24/2023] Open
Abstract
Background/purpose Artificial intelligence (AI) has made deep inroads into dentistry in the last few years. The aim of this systematic review was to identify the development of AI applications that are widely employed in dentistry and evaluate their performance in terms of diagnosis, clinical decision-making, and predicting the prognosis of the treatment. Materials and methods The literature for this paper was identified and selected by performing a thorough search in the electronic data bases like PubMed, Medline, Embase, Cochrane, Google scholar, Scopus, Web of science, and Saudi digital library published over the past two decades (January 2000–March 15, 2020).After applying inclusion and exclusion criteria, 43 articles were read in full and critically analyzed. Quality analysis was performed using QUADAS-2. Results AI technologies are widely implemented in a wide range of dentistry specialties. Most of the documented work is focused on AI models that rely on convolutional neural networks (CNNs) and artificial neural networks (ANNs). These AI models have been used in detection and diagnosis of dental caries, vertical root fractures, apical lesions, salivary gland diseases, maxillary sinusitis, maxillofacial cysts, cervical lymph nodes metastasis, osteoporosis, cancerous lesions, alveolar bone loss, predicting orthodontic extractions, need for orthodontic treatments, cephalometric analysis, age and gender determination. Conclusion These studies indicate that the performance of an AI based automated system is excellent. They mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy.
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Affiliation(s)
- Sanjeev B Khanagar
- Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Ali Al-Ehaideb
- Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,Dental Services, King Abdulaziz Medical City- Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Prabhadevi C Maganur
- Department of Preventive Dental Sciences, Division of Pedodontics, College of Dentistry, Jazan University, Jazan, Saudi Arabia
| | - Satish Vishwanathaiah
- Department of Preventive Dental Sciences, Division of Pedodontics, College of Dentistry, Jazan University, Jazan, Saudi Arabia
| | - Shankargouda Patil
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan, Saudi Arabia
| | - Hosam A Baeshen
- Consultant in Orthodontics, Department of Orthodontics, College of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sachin C Sarode
- Department of Oral and Maxillofacial Pathology, Dr. D.Y.Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune 411018, Maharashtra, India
| | - Shilpa Bhandi
- Department of Restorative Dental Sciences, Division of Operative Dentistry, College of Dentistry, Jazan University, Saudi Arabia
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Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study. Oral Radiol 2020; 37:290-296. [PMID: 32506212 DOI: 10.1007/s11282-020-00449-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 05/16/2020] [Indexed: 01/24/2023]
Abstract
OBJECTIVE To apply a deep learning object detection technique to CT images for detecting cervical lymph nodes metastasis in patients with oral cancers, and to clarify the detection performance. METHODS One hundred and fifty-nine metastatic and 517 non-metastatic lymph nodes on 365 CT images in 56 patients with oral squamous cell carcinoma were examined. The images were arbitrarily assigned to training, validation, and testing datasets. Using the neural network, 'DetectNet' for object detection, the training procedure was conducted for 1000 epochs. Testing image datasets were applied to the learning model, and the detection performance was calculated. RESULTS The learning curve indicated that the recall (sensitivity) for detecting metastatic and non-metastatic lymph nodes reached 90% and 80%, respectively, while the model performance recall by applying the test dataset was 73.0% and 52.5%, respectively. The recall for detecting level IB and Level II metastatic lymph nodes was relatively high. CONCLUSIONS A system that has the potential to automatically detect cervical lymph nodes was constructed.
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Kise Y, Shimizu M, Ikeda H, Fujii T, Kuwada C, Nishiyama M, Funakoshi T, Ariji Y, Fujita H, Katsumata A, Yoshiura K, Ariji E. Usefulness of a deep learning system for diagnosing Sjögren's syndrome using ultrasonography images. Dentomaxillofac Radiol 2020; 49:20190348. [PMID: 31804146 PMCID: PMC7068075 DOI: 10.1259/dmfr.20190348] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 11/28/2019] [Accepted: 11/29/2019] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVES We evaluated the diagnostic performance of a deep learning system for the detection of Sjögren's syndrome (SjS) in ultrasonography (US) images, and compared it with the performance of inexperienced radiologists. METHODS 100 patients with a confirmed diagnosis of SjS according to both the Japanese criteria and American-European Consensus Group criteria and 100 non-SjS patients that had a dry mouth and suspected SjS but were definitively diagnosed as non-SjS were enrolled in this study. All the patients underwent US scans of both the parotid glands (PG) and submandibular glands (SMG). The training group consisted of 80 SjS patients and 80 non-SjS patients, whereas the test group consisted of 20 SjS patients and 20 non-SjS patients for deep learning analysis. The performance of the deep learning system for diagnosing SjS from the US images was compared with the diagnoses made by three inexperienced radiologists. RESULTS The accuracy, sensitivity and specificity of the deep learning system for the PG were 89.5, 90.0 and 89.0%, respectively, and those for the inexperienced radiologists were 76.7, 67.0 and 86.3%, respectively. The deep learning system results for the SMG were 84.0, 81.0 and 87.0%, respectively, and those for the inexperienced radiologists were 72.0, 78.0 and 66.0%, respectively. The AUC for the inexperienced radiologists was significantly different from that of the deep learning system. CONCLUSIONS The deep learning system had a high diagnostic ability for SjS. This suggests that deep learning could be used for diagnostic support when interpreting US images.
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Affiliation(s)
- Yoshitaka Kise
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University, Nagoya, Japan
| | - Mayumi Shimizu
- Department of Oral and Maxillofacial Radiology, Kyushu University Hospital, Fukuoka, Japan
| | - Haruka Ikeda
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University, Nagoya, Japan
| | - Takeshi Fujii
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University, Nagoya, Japan
| | - Chiaki Kuwada
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University, Nagoya, Japan
| | - Masako Nishiyama
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University, Nagoya, Japan
| | - Takuma Funakoshi
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University, Nagoya, Japan
| | - Yoshiko Ariji
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University, Nagoya, Japan
| | - Hiroshi Fujita
- Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan
| | - Akitoshi Katsumata
- Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan
| | - Kazunori Yoshiura
- Department of Oral and Maxillofacial Radiology, Faculty of Dental Science, Kyushu University, Fukuoka, Japan
| | - Eiichiro Ariji
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University, Nagoya, Japan
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Ariji Y, Fukuda M, Kise Y, Nozawa M, Nagao T, Nakayama A, Sugita Y, Katumata A, Ariji E. A preliminary application of intraoral Doppler ultrasound images to deep learning techniques for predicting late cervical lymph node metastasis in early tongue cancers. ACTA ACUST UNITED AC 2019. [DOI: 10.1002/osi2.1039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Yoshiko Ariji
- Department of Oral and Maxillofacial Radiology Aichi‐Gakuin University School of Dentistry Nagoya Japan
| | - Motoki Fukuda
- Department of Oral and Maxillofacial Radiology Aichi‐Gakuin University School of Dentistry Nagoya Japan
| | - Yoshitaka Kise
- Department of Oral and Maxillofacial Radiology Aichi‐Gakuin University School of Dentistry Nagoya Japan
| | - Michihito Nozawa
- Department of Oral and Maxillofacial Radiology Aichi‐Gakuin University School of Dentistry Nagoya Japan
| | - Toru Nagao
- Department of Maxillofacial Surgery Aichi‐Gakuin University School of Dentistry Nagoya Japan
| | - Atsushi Nakayama
- Department of Oral and Maxillofacial Surgery Aichi‐Gakuin University School of Dentistry Nagoya Japan
| | - Yoshihiko Sugita
- Department of Oral Pathology Aichi‐Gakuin University School of Dentistry Nagoya Japan
| | - Akitoshi Katumata
- Department of Oral Radiology Asahi University School of Dentistry Mizuho Japan
| | - Eiichiro Ariji
- Department of Oral and Maxillofacial Radiology Aichi‐Gakuin University School of Dentistry Nagoya Japan
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