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Yu Z, Liu S, Liu P, Liu Y. Automatic detection and diagnosis of thyroid ultrasound images based on attention mechanism. Comput Biol Med 2023; 155:106468. [PMID: 36841057 DOI: 10.1016/j.compbiomed.2022.106468] [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: 08/10/2022] [Revised: 11/21/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Incidents of thyroid cancer have dramatically increased in recent years; however, early ultrasound diagnosis can reduce morbidity and mortality. The work in clinical situations relies heavily on the subjective experience of the sonographer. Numerous computer-aided diagnostic techniques exist, but most consider how good the results are, ignoring the pre-image collecting and its usefulness in post-clinical practise. To address these issues, this study proposes a computer-aided diagnosis method based on an attentional mechanism. Due to its lightweight properties, the model can rapidly identify nodules and distinguish between benign and malignant ones without using much hardware. The model uses a bounding box to locate the thyroid nodule and determines whether it is benign or cancerous, and outputs the diagnostic result of the thyroid nodule ultrasound images. The latest attention mechanisms are used to get better results at a fraction of the cost. Additionally, ultrasound images with different features of benign and malignant thyroid nodules were collected following the Thyroid Imaging Reporting and Data System standards. The experimental results showed that the approach identifies and classifies thyroid nodules rapidly and effectively; the mAP value of the results reached 0.89, and the mAP value of malignant nodules reached 0.94, with detection rate of single image reached 7 ms. Young physicians and small hospitals with limited resources can benefit from using this method to assist with thyroid ultrasound examination diagnosis.
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Affiliation(s)
- Zhenggang Yu
- College of Medicine, Huaqiao University, Quanzhou, Fujian Province, China
| | - Shunlan Liu
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian Province, China
| | - Peizhong Liu
- College of Medicine, Huaqiao University, Quanzhou, Fujian Province, China; College of Engineering, Huaqiao University, Quanzhou, Fujian Province, China.
| | - Yao Liu
- College of Science and Engineering, National Quemoy University, Kinmen, 89250, Taiwan
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López F, Al Ghuzlan A, Zafereo M, Vander Poorten V, Robbins KT, Hamoir M, Nixon IJ, Tufano RP, Randolph G, Pace-Asciak P, Angelos P, Coca-Pelaz A, Khafif A, Ronen O, Rodrigo JP, Sanabria Á, Palme CE, Mäkitie AA, Kowalski LP, Rinaldo A, Ferlito A. Neck Surgery for Non-Well Differentiated Thyroid Malignancies: Variations in Strategy According to Histopathology. Cancers (Basel) 2023; 15:cancers15041255. [PMID: 36831604 PMCID: PMC9954150 DOI: 10.3390/cancers15041255] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/04/2023] [Accepted: 02/13/2023] [Indexed: 02/18/2023] Open
Abstract
Lymph node metastases in non-well differentiated thyroid cancer (non-WDTC) are common, both in the central compartment (levels VI and VII) and in the lateral neck (Levels II to V). Nodal metastases negatively affect prognosis and should be treated to maximize locoregional control while minimizing morbidity. In non-WDTC, the rate of nodal involvement is variable and depends on the histology of the tumor. For medullary thyroid carcinomas, poorly differentiated thyroid carcinomas, and anaplastic thyroid carcinomas, the high frequency of lymph node metastases makes central compartment dissection generally necessary. In mucoepidermoid carcinomas, malignant peripheral nerve sheath tumors, sarcomas, and malignant thyroid teratomas or thyroblastomas, central compartment dissection is less often necessary, as clinical lymphnode involvement is less common. We aim to summarize the medical literature and the opinions of several experts from different parts of the world on the current philosophy for managing the neck in less common types of thyroid cancer.
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Affiliation(s)
- Fernando López
- Head and Neck Surgery, Hospital Universitario Central de Asturias (HUCA), 33011 Oviedo, Spain
- Instituto Universitario de Oncología del Principado de Asturias, Instituto de Investigación Sanitaria del Principado de Asturias, University of Oviedo, CIBERONC, 330011 Oviedo, Spain
- Correspondence: ; Tel.: +34-985108000
| | - Abir Al Ghuzlan
- Department of Biology and Pathology, Gustave Roussy Cancer Campus, University Paris-Saclay, 91190 Villejuif, France
| | - Mark Zafereo
- Department of Head and Neck Surgery, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Vincent Vander Poorten
- Otorhinolaryngology Head and Neck Surgery and Department of Oncology, Section Head and Neck Oncology, University Hospitals Leuven, 3000 Leuven, Belgium
- European Reference Network for Rare Adult Solid Cancers (EURACAN), 69008 Lyon, France
| | - K. Thomas Robbins
- Department of Otolaryngology-Head and Neck Surgery, Southern Illinois University School of Medicine, Springfield, IL 62702, USA
| | - Marc Hamoir
- Department of Head and Neck Surgery, UC Louvain, St Luc University Hospital and King Albert II Cancer Institute, 1200 Brussels, Belgium
| | - Iain J. Nixon
- Department of Otorhinolaryngology Head and Neck Surgery, NHS Lothian, Edinburgh EH1 3EG, UK
| | - Ralph P. Tufano
- FPG Thyroid and Parathyroid Center, Division of Head and Neck Endocrine Surgery, The Sarasota Memorial Health Care System, Sarasota, FL 34239, USA
| | - Gregory Randolph
- Division of Otolaryngology-Endocrine Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Harvard University, Boston, MA 02138, USA
| | - Pia Pace-Asciak
- Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Toronto, ON M5S, Canada
| | - Peter Angelos
- Department of Surgery and MacLean Center for Clinical Medical Ethics, The University of Chicago, Chicago, IL 60637, USA
| | - Andrés Coca-Pelaz
- Head and Neck Surgery, Hospital Universitario Central de Asturias (HUCA), 33011 Oviedo, Spain
- Instituto Universitario de Oncología del Principado de Asturias, Instituto de Investigación Sanitaria del Principado de Asturias, University of Oviedo, CIBERONC, 330011 Oviedo, Spain
| | - Avi Khafif
- A.R.M. Center of Otolaryngology-Head and Neck Surgery, Assuta Medical Center, Affiliated with Ben-Gurion University of the Negev, Tel Aviv 8410501, Israel
| | - Ohad Ronen
- Department of Otolaryngology—Head and Neck Surgery, Galilee Medical Center, Affiliated with Azrieli Faculty of Medicine, Bar Ilan University, Safed 5290002, Israel
| | - Juan Pablo Rodrigo
- Head and Neck Surgery, Hospital Universitario Central de Asturias (HUCA), 33011 Oviedo, Spain
- Instituto Universitario de Oncología del Principado de Asturias, Instituto de Investigación Sanitaria del Principado de Asturias, University of Oviedo, CIBERONC, 330011 Oviedo, Spain
| | - Álvaro Sanabria
- Department of Surgery, Universidad de Antioquia, CEXCA Centro de Excelencia en Enfermedades de Cabeza y Cuello, Medellín 050021, Colombia
| | - Carsten E. Palme
- Department of Head and Neck Surgery, Sydney Head and Neck Cancer Institute, Chris O’Brien Lifehouse, Sydney, NSW 2050, Australia
- Faculty of Medicine and Health Sciences, Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
- Royal Prince Alfred Institute of Academic Surgery, Sydney Local Health District, Sydney, NSW 2050, Australia
| | - Antti A. Mäkitie
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, FI-00029 Helsinki, Finland
| | - Luiz P. Kowalski
- Department of Head and Neck Surgery, University of Sao Paulo Medical School, Sao Paulo 01246-903, Brazil
- Department of Head and Neck Surgery and Otorhinolaryngology, AC Camargo Cancer Center, Sao Paulo 01509-001, Brazil
| | | | - Alfio Ferlito
- Coordinator of International Head and Neck Scientific Group, 35125 Padua, Italy
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Li J, Zhang YR, Ren JY, Li QL, Zhu PS, Du TT, Ge XY, Chen M, Cui XW. Association between diagnostic efficacy of acoustic radiation force impulse for benign and malignant thyroid nodules and the presence or absence of non-papillary thyroid cancer: A meta-analysis. Front Oncol 2023; 13:1007464. [PMID: 36776305 PMCID: PMC9915625 DOI: 10.3389/fonc.2023.1007464] [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: 07/30/2022] [Accepted: 01/09/2023] [Indexed: 01/30/2023] Open
Abstract
Purpose The aim of this study was to investigate the diagnostic efficacy of Acoustic Radiation Force Impulse (ARFI) for benign and malignant thyroid nodules in the presence and absence of non-papillary thyroid cancer (NPTC) and to determine the cut-off values of Shear Wave Velocity (SWV) for the highest diagnostic efficacy of Virtual Touch Quantification (VTQ) and Virtual Touch Tissue Imaging and Quantification (VTIQ). Methods The diagnostic accuracy of ARFI for benign and malignant thyroid nodules was assessed by pooling sensitivity, specificity and area under the curve (AUC) in each group in the presence and absence of both non-papillary thyroid glands, using histology and cytology as the gold standard. All included studies were divided into two groups according to VTQ and VTIQ, and each group was ranked according to the magnitude of the SWV cutoff value to determine the SWV cutoff interval with the highest diagnostic efficacy for VTQ and VTIQ. Results A total of 57 studies were collected on the evaluation of ARFI for the diagnosis of benign and malignant thyroid nodules. The results showed that the presence of non-papillary thyroid carcinoma led to differences in the specificity of VTIQ for the identification of benign and malignant thyroid nodules, and the differences were statistically significant. In addition, the diagnostic efficacy of VTQ was best when the cutoff value of SWV was in the interval of 2.48-2.55 m/s, and the diagnostic efficacy of VTIQ was best when the cutoff value of SWV was in the interval of 3.01-3.15 m/s. Conclusion VTQ and VTIQ have a high diagnostic value for benign and malignant thyroid nodules; however, when the malignant nodules in the study contain non-papillary thyroid carcinoma occupying the thyroid gland, the findings should be viewed in a comprehensive manner.
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Affiliation(s)
- Jun Li
- Department of Ultrasound, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China,*Correspondence: Jun Li, ; Xin Wu Cui,
| | - Yu-Rui Zhang
- Department of Ultrasound, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Jia-Yu Ren
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiao-Li Li
- Department of Ultrasound, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Pei-Shan Zhu
- Department of Ultrasound, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Ting-Ting Du
- Department of Ultrasound, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Xiao-Yan Ge
- Department of Ultrasound, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Ming Chen
- Department of Ultrasound, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Xin Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Jun Li, ; Xin Wu Cui,
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Zhang R, Yi G, Pu S, Wang Q, Sun C, Wang Q, Feng L, Liu X, Li Z, Niu L. Deep learning based on ultrasound to differentiate pathologically proven atypical and typical medullary thyroid carcinoma from follicular thyroid adenoma. Eur J Radiol 2022; 156:110547. [PMID: 36201930 DOI: 10.1016/j.ejrad.2022.110547] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 08/22/2022] [Accepted: 09/26/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVES To investigate the feasibility and value of deep learning based on grayscale ultrasonography in the differentiation of pathologically proven atypical and typical medullary thyroid carcinoma (MTC) from follicular thyroid adenoma (FTA). METHODS The preoperative 770 ultrasound images consisted of 354 MTCs (66% were typical MTCs with a high suspicion sonographic pattern, 34% were atypical MTCs with a suspicion pattern of intermediate or less) and 416 FTAs. All images were delineated manually by a senior sonographer to achieve the regions of interest. Two deep neural networks of ResNet-34 and ResNet-18 were performed on the training set (n = 690). The test data set (n = 80) was subsequently evaluated by the two models and two sonographers, their diagnostic performances and misdiagnosis lesions were compared and analyzed. RESULTS The ResNet-34 model shows higher diagnostic ability than the junior sonographer with an area under the receiver operating curve of 0.992 (95% CI: 0.840-0.970)versus 0.754 (95% CI:0.645-0.843). Moreover, 12 of 16 atypical MTCs were successfully identified by the ResNet-34, which is significantly better than the senior and junior sonographer, suggesting that these patients could benefit from timely serological examination and surgical strategy at an earlier stage. CONCLUSION Deep learning to differentiate MTC from FTA on grayscale ultrasound may be a useful diagnostic support tool, especially in atypical MTC and FTA. Moreover, the computing time of deep learning is short, which will help to incorporate it into real-time ultrasound diagnosis.
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Affiliation(s)
- Rui Zhang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guanxiu Yi
- Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, China.
| | - Shunfan Pu
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qin Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Sun
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qian Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Li Feng
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiabi Liu
- Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, China.
| | - Zhengjiang Li
- Department of Head and Neck Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Lijuan Niu
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Leimbach RD, Hoang TD, Shakir MKM. Diagnostic Challenges of Medullary Thyroid Carcinoma. Oncology 2021; 99:422-432. [PMID: 33878761 DOI: 10.1159/000515373] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 02/17/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND Medullary thyroid carcinoma (MTC) comprises 1-2% of all thyroid cancers, yet 15% of all thyroid cancer-related deaths. While up to 20% of cases may be predicted due to autosomal dominant germline mutations, 80% of cases are sporadic. However, due to non-specific presenting symptoms and diagnostic imaging, prompt diagnosis and treatment has remained elusive. This article will further investigate the limitations of MTC diagnosis and look into future areas for diagnostic improvement. METHODS Relevant articles were identified using a systematic PubMed and Google Scholar search. RESULTS Prophylactic total thyroidectomy for the 20% of MTC cases that are present in autosomal dominant disorder provides definitive treatment. Serum calcitonin (Ctn) screening has several technical limitations due to population variability and laboratory assay interference, but advances in laboratory technology and combined use with fine needle aspiration increase its sensitivity. Other serum assays such as carcinoembryonic antigen and procalcitonin have limited applicability. Thyroid ultrasound remains the gold standard for the initial diagnostic planning, with limited application for CT, MRI, and PET imaging. CONCLUSION With complete surgical resection the only definitive treatment, early MTC diagnosis has presented an elusive challenge, mainly due to its relative rarity and difficulty in finding an economic screening strategy. Careful family history combined with fine needle aspiration with serum Ctn analysis can improve diagnostic sensitivity and specificity to greater than 95%.
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Affiliation(s)
- Robert D Leimbach
- Division of Endocrinology, Department of Medicine, Walter Reed National Military Medical Center, Bethesda, Maryland, USA.,Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Thanh D Hoang
- Division of Endocrinology, Department of Medicine, Walter Reed National Military Medical Center, Bethesda, Maryland, USA.,Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Mohamed K M Shakir
- Division of Endocrinology, Department of Medicine, Walter Reed National Military Medical Center, Bethesda, Maryland, USA.,Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
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Zhou TH, Zhao LQ, Zhang Y, Wu F, Lu KN, Mao LL, Jiang KC, Luo DC. The Prediction of Metastases of Lateral Cervical Lymph Node in Medullary Thyroid Carcinoma. Front Endocrinol (Lausanne) 2021; 12:741289. [PMID: 34867784 PMCID: PMC8635959 DOI: 10.3389/fendo.2021.741289] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 10/25/2021] [Indexed: 11/26/2022] Open
Abstract
PURPOSE Development and validation of a nomogram for the prediction of lateral lymph node metastasis (LLNM) in medullary thyroid carcinoma (MTC). METHODS We retrospectively reviewed the clinical features of patients with MTC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017 and in our Department of Surgical Oncology, Hangzhou First People's Hospital between 2009 and 2019. The log-rank test was used to compare the difference in the Kaplan-Meier (K-M) curves in recurrence and survival. The nomogram was developed to predict the risk of LLNM in MTC patients. The prediction efficiency of the predictive model was assessed by area under the curve (AUC) and concordance index (C-index) and calibration curves. Decision curve analysis (DCA) was performed to determine the clinic value of the predictive model. RESULT A total of 714 patients in the SEER database and 35 patients in our department were enrolled in our study. Patients with LLNM had worse recurrence rate and cancer-specific survival (CSS) compared with patients without LLNM. Five clinical characteristics including sex, tumor size, multifocality, extrathyroidal extension, and distant metastasis were identified to be associated with LLNM in MTC patients, which were used to develop a nomogram. Our prediction model had satisfied discrimination with a C-index of 0.825, supported by both training set and internal testing set with a C-index of 0.825, and 0.816, respectively. DCA was further made to evaluate the clinical utility of this nomogram for predicting LLNM. CONCLUSIONS Male sex, tumor size >38mm, multifocality, extrathyroidal extension, and distant metastasis in MTC patients were significant risk factors for predicting LLNM.
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Affiliation(s)
- Tian-Han Zhou
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ling-Qian Zhao
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yu Zhang
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Surgical Oncology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fan Wu
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Kai-Ning Lu
- Department of Surgical Oncology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lin-Lin Mao
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Surgical Oncology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ke-Cheng Jiang
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ding-Cun Luo
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Surgical Oncology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Ding-Cun Luo,
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