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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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Zhu Y, Meng Z, Wu H, Fan X, Lv W, Tian J, Wang K, Nie F. Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2024; 45:305-315. [PMID: 38052240 DOI: 10.1055/a-2161-9369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
PURPOSE To investigate the feasibility of deep learning radiomics (DLR) based on multimodal ultrasound to differentiate the primary cancer sites of metastatic cervical lymphadenopathy (CLA). MATERIALS AND METHODS This study analyzed 280 biopsy-confirmed metastatic CLAs from 280 cancer patients, including 54 from head and neck squamous cell carcinoma (HNSCC), 58 from thyroid cancer (TC), 92 from lung cancer (LC), and 76 from gastrointestinal cancer (GIC). Before biopsy, patients underwent conventional ultrasound (CUS), ultrasound elastography (UE), and contrast-enhanced ultrasound (CEUS). Based on CUS, DLR models using CUS, CUS+UE, CUS+CEUS, and CUS+UE+CEUS data were developed and compared. The best model was integrated with key clinical indicators selected by univariate analysis to achieve the best classification performance. RESULTS All DLR models achieved similar performance with respect to classifying four primary tumor sites of metastatic CLA (AUC:0.708~0.755). After integrating key clinical indicators (age, sex, and neck level), the US+UE+CEUS+clinical model yielded the best performance with an overall AUC of 0.822 in the validation cohort, but there was no significance compared with the basal CUS+clinical model (P>0.05), both of which identified metastasis from HNSCC, TC, LC, and GIC with 0.869 and 0.911, 0.838 and 0.916, 0.750 and 0.610, and 0.829 and 0.769, respectively. CONCLUSION The ultrasound-based DLR model can be used to classify the primary cancer sites of metastatic CLA, and the CUS combined with clinical indicators is adequate to provide a high discriminatory performance. The addition of the combination of UE and CEUS data is expected to further improve performance.
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Affiliation(s)
- Yangyang Zhu
- Medical Center of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zheling Meng
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of the Chinese Academy of Sciences School, Beijing, China
| | - Hao Wu
- Medical Center of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
| | - Xiao Fan
- Medical Center of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
| | - Wenhao Lv
- Medical Center of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of the Chinese Academy of Sciences School, Beijing, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of the Chinese Academy of Sciences School, Beijing, China
| | - Fang Nie
- Medical Center of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China
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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: 0] [Impact Index Per Article: 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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Vaughn JA. Imaging of Pediatric Cervical Lymphadenopathy. Neuroimaging Clin N Am 2023; 33:581-590. [PMID: 37741659 DOI: 10.1016/j.nic.2023.05.008] [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: 09/25/2023]
Abstract
There is a wide variety of disease entities in children, which can present with cervical adenopathy. The spectrum of pathology and imaging appearance differs in many cases from that seen in adults. This review aims to compare the strengths and limitations of the various imaging modalities available to image pediatric patients presenting with cervical adenopathy, provide guidance on when to image, and highlight the imaging appearance of both common and uncommon disorders affecting the cervical nodes in children to aid the radiologist in their clinical practice.
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Affiliation(s)
- Jennifer A Vaughn
- Department of Radiology, Phoenix Children's Hospital, Phoenix, AZ, USA; Radiology, University of Arizona College of Medicine, Phoenix, AZ, USA; Radiology, Creighton University School of Medicine, Phoenix, AZ, USA; Barrows Neurological Institute, Phoenix, AZ, USA.
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Abbasian Ardakani A, Mohammadi A, Mirza-Aghazadeh-Attari M, Faeghi F, Vogl TJ, Acharya UR. Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:1211-1221. [PMID: 36437513 DOI: 10.1002/jum.16131] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/01/2022] [Accepted: 11/06/2022] [Indexed: 05/18/2023]
Abstract
OBJECTIVES Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients with papillary thyroid cancer. METHODS An in-house deep learning-based model called "ClymphNet" was developed and tested using two datasets containing ultrasound images of 195 malignant and 178 benign lymph nodes. An expert radiologist also viewed these ultrasound images and extracted qualitative imaging features used in routine clinical practice. These signs were used to train three different machine learning algorithms. Then the deep learning model was compared with the machine learning models on internal and external validation datasets containing 22 and 82 malignant and 20 and 76 benign lymph nodes, respectively. RESULTS Among the three machine learning algorithms, the support vector machine model (SVM) outperformed the best, reaching a sensitivity of 91.35%, specificity of 88.54%, accuracy of 90.00%, and an area under the curve (AUC) of 0.925 in all cohorts. The ClymphNet performed better than the SVM protocol in internal and external validation, achieving a sensitivity of 93.27%, specificity of 92.71%, and an accuracy of 93.00%, and an AUC of 0.948 in all cohorts. CONCLUSION A deep learning model trained with ultrasound images outperformed three conventional machine learning algorithms fed with qualitative imaging features interpreted by radiologists. Our study provides evidence regarding the utility of ClymphNet in the early and accurate differentiation of benign and malignant lymphadenopathy.
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Affiliation(s)
- Ali Abbasian Ardakani
- Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Afshin Mohammadi
- Department of Radiology, Faculty of Medicine, Urmia University of Medical Science, Urmia, Iran
| | - Mohammad Mirza-Aghazadeh-Attari
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Fariborz Faeghi
- Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Thomas J Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Omobolaji Alabi R, Sjöblom A, Carpén T, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma: A validation of ProgTOOL. Int J Med Inform 2023; 175:105064. [PMID: 37094545 DOI: 10.1016/j.ijmedinf.2023.105064] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND In recent years, there has been a surge in machine learning-based models for diagnosis and prognostication of outcomes in oncology. However, there are concerns relating to the model's reproducibility and generalizability to a separate patient cohort (i.e., external validation). OBJECTIVES This study primarily provides a validation study for a recently introduced and publicly available machine learning (ML) web-based prognostic tool (ProgTOOL) for overall survival risk stratification of oropharyngeal squamous cell carcinoma (OPSCC). Additionally, we reviewed the published studies that have utilized ML for outcome prognostication in OPSCC to examine how many of these models were externally validated, type of external validation, characteristics of the external dataset, and diagnostic performance characteristics on the internal validation (IV) and external validation (EV) datasets were extracted and compared. METHODS We used a total of 163 OPSCC patients obtained from the Helsinki University Hospital to externally validate the ProgTOOL for generalizability. In addition, PubMed, OvidMedline, Scopus, and Web of Science databases were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS The ProgTOOL produced a predictive performance of 86.5% balanced accuracy, Mathew's correlation coefficient of 0.78, Net Benefit (0.7) and Brier score (0.06) for overall survival stratification of OPSCC patients as either low-chance or high-chance. In addition, out of a total of 31 studies found to have used ML for the prognostication of outcomes in OPSCC, only seven (22.6%) reported a form of EV. Three studies (42.9%) each used either temporal EV or geographical EV while only one study (14.2%) used expert as a form of EV. Most of the studies reported a reduction in performance when externally validated. CONCLUSION The performance of the model in this validation study indicates that it may be generalized, therefore, bringing recommendations of the model for clinical evaluation closer to reality. However, the number of externally validated ML-based models for OPSCC is still relatively small. This significantly limits the transfer of these models for clinical evaluation and subsequently reduces the likelihood of the use of these models in daily clinical practice. As a gold standard, we recommend the use of geographical EV and validation studies to reveal biases and overfitting of these models. These recommendations are poised to facilitate the implementation of these models in clinical practice.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Anni Sjöblom
- Department of Pathology, University of Helsinki, Helsinki, Finland
| | - Timo Carpén
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Kong D, Shan W, Zhu Y, Xu Q, Duan S, Guo L. Preliminary study on CT contrast-enhanced radiomics for predicting central cervical lymph node status in patients with thyroid nodules. Front Oncol 2023; 13:1060674. [PMID: 36816945 PMCID: PMC9935823 DOI: 10.3389/fonc.2023.1060674] [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: 10/03/2022] [Accepted: 01/20/2023] [Indexed: 02/05/2023] Open
Abstract
Objective To explore the feasibility of using a contrast-enhanced CT image-based radiomics model to predict central cervical lymph node status in patients with thyroid nodules. Methods Pretreatment clinical and CT imaging data from 271 patients with surgically diagnosed and treated thyroid nodules were retrospectively analyzed. According to the pathological features of the thyroid nodules and central lymph nodes, the patients were divided into three groups: group 1: papillary thyroid carcinoma (PTC) metastatic lymph node group; group 2: PTC nonmetastatic lymph node group; and group 3: benign thyroid nodule reactive lymph node group. Radiomics models were constructed to compare the three groups by pairwise classification (model 1: group 1 vs group 3; model 2: group 1 vs group 2; model 3: group 2 vs group 3; and model 4: group 1 vs groups (2 + 3)). The feature parameters with good generalizability and clinical risk factors were screened. A nomogram was constructed by combining the radiomics features and clinical risk factors. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were performed to assess the diagnostic and clinical value of the nomogram. Results For radiomics models 1, 2, and 3, the areas under the curve (AUCs) in the training group were 0.97, 0.96, and 0.93, respectively. The following independent clinical risk factors were identified: model 1, arterial phase CT values; model 2, sex and arterial phase CT values; model 3: none. The AUCs for the nomograms of models 1 and 2 in the training group were 0.98 and 0.97, respectively, and those in the test group were 0.95 and 0.87, respectively. The AUCs of the model 4 nomogram in the training and test groups were 0.96 and 0.94, respectively. Calibration curve analysis and DCA revealed the high clinical value of the nomograms of models 1, 2 and 4. Conclusion The nomograms based on contrast-enhanced CT images had good predictive efficacy in classifying benign and malignant central cervical lymph nodes of thyroid nodule patients.
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Affiliation(s)
- Dan Kong
- Department of Imaging, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Wenli Shan
- Department of Imaging, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Yan Zhu
- Department of Imaging, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Qingqing Xu
- Department of Imaging, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Shaofeng Duan
- Institute of precision medicine, GE Healthcare, Shanghai, China
| | - Lili Guo
- Department of Imaging, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, China,*Correspondence: Lili Guo,
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Santer M, Kloppenburg M, Gottfried TM, Runge A, Schmutzhard J, Vorbach SM, Mangesius J, Riedl D, Mangesius S, Widmann G, Riechelmann H, Dejaco D, Freysinger W. Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma-A Systematic Review. Cancers (Basel) 2022; 14:5397. [PMID: 36358815 PMCID: PMC9654953 DOI: 10.3390/cancers14215397] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 10/28/2022] [Accepted: 10/29/2022] [Indexed: 07/22/2023] Open
Abstract
Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10-258) and of LNs was 340 (SD ± 268; range 21-791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43-99%) and for testing sets 86% (SD ± 5%; range 76-92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI's role in LN-classification in locally-advanced HNSCC.
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Affiliation(s)
- Matthias Santer
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Marcel Kloppenburg
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Timo Maria Gottfried
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Annette Runge
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Joachim Schmutzhard
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Samuel Moritz Vorbach
- Department of Radiation-Oncology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Julian Mangesius
- Department of Radiation-Oncology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - David Riedl
- University Hospital of Psychiatry II, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Ludwig-Boltzmann Institute for Rehabilitation Research, 1100 Vienna, Austria
| | - Stephanie Mangesius
- Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Gerlig Widmann
- Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Herbert Riechelmann
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Daniel Dejaco
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Wolfgang Freysinger
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
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Kim BH, Lee C, Lee JY, Tae K. Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto's disease from tuberculous lymphadenitis on neck CECT. Sci Rep 2022; 12:14184. [PMID: 35986073 PMCID: PMC9391448 DOI: 10.1038/s41598-022-18535-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/16/2022] [Indexed: 11/14/2022] Open
Abstract
Neck contrast-enhanced CT (CECT) is a routine tool used to evaluate patients with cervical lymphadenopathy. This study aimed to evaluate the ability of convolutional neural networks (CNNs) to classify Kikuchi-Fujimoto's disease (KD) and cervical tuberculous lymphadenitis (CTL) on neck CECT in patients with benign cervical lymphadenopathy. A retrospective analysis of consecutive patients with biopsy-confirmed KD and CTL in a single center, from January 2012 to June 2020 was performed. This study included 198 patients of whom 125 patients (mean age, 25.1 years ± 8.7, 31 men) had KD and 73 patients (mean age, 41.0 years ± 16.8, 34 men) had CTL. A neuroradiologist manually labelled the enlarged lymph nodes on the CECT images. Using these labels as the reference standard, a CNNs was developed to classify the findings as KD or CTL. The CT images were divided into training (70%), validation (10%), and test (20%) subsets. As a supervised augmentation method, the Cut&Remain method was applied to improve performance. The best area under the receiver operating characteristic curve for classifying KD from CTL for the test set was 0.91. This study shows that the differentiation of KD from CTL on neck CECT using a CNNs is feasible with high diagnostic performance.
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Affiliation(s)
- Byung Hun Kim
- Department of Otolaryngology-Head and Neck Surgery, Hanyang University Hospital, College of Medicine, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Changhwan Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Ji Young Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpodaero, Seocho-gu, Seoul, 06591, Republic of Korea.
| | - Kyung Tae
- Department of Otolaryngology-Head and Neck Surgery, Hanyang University Hospital, College of Medicine, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
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Siravegna G, O'Boyle CJ, Varmeh S, Queenan N, Michel A, Stein J, Thierauf J, Sadow PM, Faquin WC, Perry SK, Bard AZ, Wang W, Deschler DG, Emerick KS, Varvares MA, Park JC, Clark JR, Chan AW, Andreu Arasa VC, Sakai O, Lennerz J, Corcoran RB, Wirth LJ, Lin DT, Iafrate AJ, Richmon JD, Faden DL. Cell free HPV DNA provides an accurate and rapid diagnosis of HPV-associated head and neck cancer. Clin Cancer Res 2021; 28:719-727. [PMID: 34857594 DOI: 10.1158/1078-0432.ccr-21-3151] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 10/15/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE HPV-associated Head and Neck Squamous Cell Carcinoma(HPV+HNSCC) is the most common HPV-associated malignancy in the United States and continues to increase in incidence. Current diagnostic approaches for HPV+HNSCC rely on tissue biopsy followed by histomorphologic assessment and detection of HPV indirectly by p16 immunohistochemistry. Such approaches are invasive and have variable sensitivity. EXPERIMENTAL DESIGN We conducted a prospective observational study in 140 subjects (70 cases and 70 controls) to test the hypothesis that a non-invasive diagnostic approach for HPV+HNSCC would have improved diagnostic accuracy, lower cost, and shorter Diagnostic Interval compared to standard approaches. Blood was collected, processed for circulating tumor HPV DNA(ctHPVDNA) and analyzed with custom ddPCR assays for HPV genotypes 16,18, 33, 35 and 45. Diagnostic performance, cost and Diagnostic Interval were calculated for standard clinical work up and compared to a non-invasive approach using ctHPVDNA combined with cross-sectional imaging and physical exam findings. RESULTS Sensitivity and specificity of ctHPVDNA for detecting HPV+HNSCC was 98.4% and 98.6%. Sensitivity and specificity of a composite non-invasive diagnostic using ctHPVDNA and imaging/physical exam were 95.1% and 98.6%. Diagnostic accuracy of this non-invasive approach was significantly higher than standard of care (Youden index 0.937 vs 0.707, p=0.0006). Costs of non-invasive diagnostic were 36-38% less than standard clinical work up and the median Diagnostic Interval was 26 days less. CONCLUSIONS A non-invasive diagnostic approach for HPV+HNSCC demonstrated improved accuracy, reduced cost and a shorter time to diagnosis compared to standard clinical workup and could be a viable alternative in the future.
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Affiliation(s)
| | - Connor J O'Boyle
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear
| | | | - Natalia Queenan
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear
| | | | - Jarrod Stein
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear, Massachusetts General Hospital, Harvard Medical School, Broad Institute
| | - Julia Thierauf
- Department of Otolaryngology, Head and Neck Surgery, 1985
| | | | | | - Simon K Perry
- Department of Pathology, Massachusetts General Hospital
| | - Adam Z Bard
- Department of Pathology, Massachusetts General Hospital
| | - Wei Wang
- 6. Departments of Medicine and Neurology, Brigham and Women's Hospital
| | - Daniel G Deschler
- Otology and Laryngology, Massachusetts Eye and Ear Infirmary and Harvard Medical School
| | - Kevin S Emerick
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear, Massachusetts General Hospital, Harvard Medical School, Broad Institute
| | - Mark A Varvares
- Department of Otolaryngology, Massachusetts Eye and Ear Infirmary,, Harvard Medical School
| | - Jong C Park
- Hematology and Oncology, Massachusetts General Hospital
| | - John R Clark
- Hematology and Oncology, Massachusetts General Hospital/Harvard Medical School
| | - Annie W Chan
- Radiation Oncology, Massachusetts General Hospital
| | | | - Osamu Sakai
- Department or Radiology, Boston Medical Center
| | | | | | - Lori J Wirth
- Department of Medicine, Massachusetts General Hospital
| | | | | | - Jeremy D Richmon
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear, Massachusetts General Hospital, Harvard Medical School, Broad Institute
| | - Daniel L Faden
- Otolaryngology-Head and Neck Suirgery, Massachusetts Eye and Ear, Massachusetts General Hospital, Harvard Medical School, Broad Institute
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