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Roberto Negro Division of Endocrinology, V. Fazzi Hospital, Lecce, Italy

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Matteo Rucco United Technology Research Center, Trento, Italy

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Annalisa Creanza Division of Endocrinology and Metabolism, Mauriziano Hospital Umberto I, Turin, Italy

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Alberto Mormile Division of Endocrinology and Metabolism, Mauriziano Hospital Umberto I, Turin, Italy

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Paolo Piero Limone Division of Endocrinology and Metabolism, Mauriziano Hospital Umberto I, Turin, Italy

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Roberto Garberoglio Division of Endocrinology and Metabolism, Molinette Hospital, Turin, Italy

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Stefano Spiezia Endocrine Surgery, Ospedale del Mare, ASL NA1 Centro, Naples, Italy

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Salvatore Monti Endocrinology Unit, Azienda Ospedaliera Universitaria Sant’Andrea, Rome, Italy

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Christian Cugini Radiology Department, Villa Salus Hospital, Venice, Italy

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Ghassan El Dalati Radiology Department, Policlinico G.B. Rossi, Verona, Italy

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Maurilio Deandrea Division of Endocrinology and Metabolism, Mauriziano Hospital Umberto I, Turin, Italy

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additional treatment session. For this purpose, we trained a machine learning model and tested its ability to predict the 12-month VRR of nodules treated with RF. Methods The machine learning model was trained with a dataset consisting of 402 Italian

Article Type:
Research Article
DOI:
https://doi.org/10.1159/000504882
Volume/Issue:
Volume 9: Issue 4
Free access
Giulia Brigante Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria, Modena, Italy

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Clara Lazzaretti Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Elia Paradiso Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Federico Nuzzo Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Martina Sitti Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Frank Tüttelmann Institute of Reproductive Genetics, University of Münster, Münster, Germany

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Gabriele Moretti Department of Biology, University of Pisa, Pisa, Italy

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Roberto Silvestri Department of Biology, University of Pisa, Pisa, Italy

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Federica Gemignani Department of Biology, University of Pisa, Pisa, Italy

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Asta Försti Hopp Children’s Cancer Center (KiTZ), Heidelberg, Germany
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany

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Kari Hemminki Biomedical Center, Faculty of Medicine and Biomedical Center in Pilsen, Charles University in Prague, Pilsen, Czech Republic
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

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Rossella Elisei Department of Endocrinology, University Hospital, Pisa, Italy

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Cristina Romei Department of Endocrinology, University Hospital, Pisa, Italy

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Eric Adriano Zizzi PolitoBIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy

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Marco Agostino Deriu PolitoBIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy

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Manuela Simoni Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria, Modena, Italy
Center for Genomic Research, University of Modena and Reggio Emilia, Modena, Italy

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Stefano Landi Department of Biology, University of Pisa, Pisa, Italy

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Livio Casarini Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
Center for Genomic Research, University of Modena and Reggio Emilia, Modena, Italy

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, gene–gene interactions are likely too complex to be explained by simple additive or weighted models and alternative methods are under exploration. Machine learning (ML) is increasingly used for predicting individuals’ inherited genomic susceptibility

Article Type:
Research Article
Collections:
Genetics and omics in thyroidology
DOI:
https://doi.org/10.1530/ETJ-22-0058
Volume/Issue:
Volume 11: Issue 5
Open access
Heleen I Jansen Department of Laboratory Medicine, Endocrine Laboratory, Amsterdam UMC location Vrije Universiteit Amsterdam, Boelelaan, Amsterdam, The Netherlands
Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Laboratory Medicine, Endocrine Laboratory, Amsterdam UMC location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands

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Marije van Haeringen Department of Laboratory Medicine, Endocrine Laboratory, Amsterdam UMC location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands
Department of Computer Science, Vrije Universiteit, Boelelaan, Amsterdam, The Netherlands

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Marelle J Bouva Reference Laboratory Neonatal Screening, Center for Health protection, National Institute for Public Health and the Environment, Bilthoven, The Netherlands

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Wendy P J den Elzen Department of Laboratory Medicine, Laboratory Specialized Diagnostics & Research, Amsterdam UMC, University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands
Amsterdam Public Health, Amsterdam, The Netherlands

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Eveline Bruinstroop Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Endocrinology and Metabolism, Amsterdam UMC location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands

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Catharina P B van der Ploeg TNO - Child Health, Sylviusweg, Leiden, The Netherlands

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A S Paul van Trotsenburg Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Paediatric Endocrinology, Emma Children’s Hospital, Amsterdam UMC, University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands

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Nitash Zwaveling-Soonawala Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Paediatric Endocrinology, Emma Children’s Hospital, Amsterdam UMC, University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands

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Annemieke C Heijboer Department of Laboratory Medicine, Endocrine Laboratory, Amsterdam UMC location Vrije Universiteit Amsterdam, Boelelaan, Amsterdam, The Netherlands
Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Laboratory Medicine, Endocrine Laboratory, Amsterdam UMC location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands
Amsterdam Reproduction & Development Research Institute, Amsterdam, The Netherlands

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Annet M Bosch Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Pediatrics, Division of Metabolic Disorders, Emma Children’s Hospital, Amsterdam UMC, University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands

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Robert de Jonge Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Laboratory Medicine, Amsterdam UMC, Vrije Universiteit, Boelelaan, Amsterdam, The Netherlands
Department of Laboratory Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands

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Mark Hoogendoorn Department of Computer Science, Vrije Universiteit, Boelelaan, Amsterdam, The Netherlands

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Anita Boelen Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
Department of Laboratory Medicine, Endocrine Laboratory, Amsterdam UMC location University of Amsterdam, Meibergdreef, Amsterdam, The Netherlands
Amsterdam Reproduction & Development Research Institute, Amsterdam, The Netherlands

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4 and TBG and thereby refining the cutoff value for a (partial) TBG deficiency ( 3 ) and by using machine learning ( 8 ). The latter study used a dataset containing almost all children with a referral in the historical CH screening from 2007 to 2017

Article Type:
Research Article
Collections:
Pediatric thyroid diseases
DOI:
https://doi.org/10.1530/ETJ-23-0141
Volume/Issue:
Volume 12: Issue 6
Open access
Lei Xu Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
Xi’an Hospital of Traditional Chinese Medicine, Xi’an, China

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Junling Gao Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China

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Quan Wang Laboratory of Surgical Oncology, Peking University People’s Hospital, Peking University, Beijing, China

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Jichao Yin Xi’an Hospital of Traditional Chinese Medicine, Xi’an, China

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Pengfei Yu Xijing Hospital, Fourth Military Medical University, Xi’an, China

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Bin Bai Xijing Hospital, Fourth Military Medical University, Xi’an, China

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Ruixia Pei Xi’an Hospital of Traditional Chinese Medicine, Xi’an, China

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Dingzhang Chen Xijing Hospital, Fourth Military Medical University, Xi’an, China

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Guochun Yang Xi’an Hospital of Traditional Chinese Medicine, Xi’an, China

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Shiqi Wang Xijing Hospital, Fourth Military Medical University, Xi’an, China

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Mingxi Wan Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China

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diagnostic accuracy and efficiency, machine learning-based computer-aided diagnosis (CAD) systems are being introduced in the diagnosis process. Currently, two types of machine learning method are adopted: (1) the classic machine learning method, which is

Article Type:
Systematic Review
DOI:
https://doi.org/10.1159/000504390
Volume/Issue:
Volume 9: Issue 4
Free access
Tomohiro Kikuchi Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan
Department of Radiology, Jichi Medical University, School of Medicine, Shimotsuke, Tochigi, Japan

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Shouhei Hanaoka Department of Radiology, The University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan

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Takahiro Nakao Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan

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Yukihiro Nomura Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan
Center for Frontier Medical Engineering, Chiba University, Yayoicho, Inage–ku, Chiba, Japan

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Takeharu Yoshikawa Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan

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Ashraful Alam Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan

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Harushi Mori Department of Radiology, Jichi Medical University, School of Medicine, Shimotsuke, Tochigi, Japan

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Naoto Hayashi Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, Hongo, Bunkyo–ku, Tokyo, Japan

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imaging data from our large database by constructing a deep learning-based dedicated automatic segmentation method for the thyroid gland. Our implementation was done using two U-nets. The U-net is a neural network with a symmetric U-shaped architecture

Article Type:
Research Article
Collections:
Goiter and thyroid nodules
DOI:
https://doi.org/10.1530/ETJ-22-0165
Volume/Issue:
Volume 12: Issue 1
Open access
Kristine Z Swan Department of ORL, Head- and Neck Surgery, Aarhus University Hospital, Aarhus, Denmark

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Johnson Thomas Department of Endocrinology, Mercy Hospital, Springfield, Missouri, USA

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Viveque E Nielsen Department of ORL, Head- and Neck Surgery, Odense University Hospital, Odense, Denmark

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Marie Louise Jespersen Department of Pathology, Aarhus University Hospital, Aarhus, Denmark

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Steen J Bonnema Department of Endocrinology, Odense University Hospital, Odense, Denmark

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Fairness, Accountability and Transparency , pp. 77 – 91 . Eds Sorelle AF Christo W . Proceedings of Machine Learning Research: PMLR , 2018 . 21 Thomas J Ledger GA Mamillapalli CK . Use of artificial intelligence and machine learning for

Article Type:
Research Article
Collections:
Goiter and thyroid nodules
DOI:
https://doi.org/10.1530/ETJ-21-0129
Volume/Issue:
Volume 11: Issue 2
Open access
Adile Begüm Bahçecioğlu Department of Endocrinology and Metabolism, Ankara University, School of Medicine, Ankara, Turkey

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Alptekin Gürsoy Department of Endocrinology and Metabolism, Ankara Guven Hospital, Ankara, Turkey

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Serpil Dizbay Sak Department of Pathology, Ankara University, School of Medicine Ankara, Turkey

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Seyfettin Ilgan Department of Nuclear Medicine, Ankara Guven Hospital, Ankara, Turkey

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Banu Bilezikçi Department of Pathology, Ankara Guven Hospital, Ankara, Turkey

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Murat Faik Erdoğan Department of Endocrinology and Metabolism, Ankara University, School of Medicine, Ankara, Turkey

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) Unclassified. US was performed with two different machines: General Electric® Acuson Antares machine equipped with a 13–10 MHz linear transducer and General Electric® Logiq S7 Expert machine equipped with 15–8 MHz linear transducer

Article Type:
Research Article
Collections:
Thyroid cancer - clinical
DOI:
https://doi.org/10.1530/ETJ-23-0108
Volume/Issue:
Volume 12: Issue 6
Open access
Haiyang Zhang Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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Shuo Wu Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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Shuyu Hu Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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Xianqun Fan Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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Xuefei Song Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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Tienan Feng Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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Huifang Zhou Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

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heterogeneity profiling through high-dimensional quantitative data extracted from radiological images ( 27 , 36 ). Furthermore, the application of advanced modeling techniques like machine learning and artificial intelligence ( 37 ) empowers the construction of

Article Type:
Systematic Review
Collections:
Hyperthyroidism and thyrotoxicosis
DOI:
https://doi.org/10.1530/ETJ-24-0122
Volume/Issue:
Volume 13: Issue 4
Open access
Roberto Negro Division of Endocrinology, “V. Fazzi” Hospital, Lecce, Italy

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Pierpaolo Trimboli Clinic for Nuclear Medicine and Competence Center for Thyroid Diseases, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland

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percutaneous laser ablation: a pilot study . Med Ultrason . 2017 Apr ; 19 ( 2 ): 172 – 8 . 10.11152/mu-1039 28440351 1844-4172 20 Negro R , Rucco M , Creanza A , Mormile A , Limone PP , Garberoglio R , et al. Machine learning

Article Type:
Editorial
DOI:
https://doi.org/10.1159/000509310
Volume/Issue:
Volume 9: Issue 4
Free access
Massimiliano Andrioli Division of Endocrine and Metabolic Diseases, San Luca Hospital, Istituto Auxologico Italiano

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Chiara Carzaniga Division of Endocrine and Metabolic Diseases, San Luca Hospital, Istituto Auxologico Italiano

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Luca Persani Division of Endocrine and Metabolic Diseases, San Luca Hospital, Istituto Auxologico Italiano
Dipartimento di Scienze Cliniche e di Comunità, Università degli Studi di Milano, Milan, Italy

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reports is nowadays even greater due to the large number of physicians who perform thyroid US. In fact, with increased accessibility of high-resolution, portable US machines, thyroid US is being performed more commonly by non-radiologist physicians, e

Article Type:
Research Article
DOI:
https://doi.org/10.1159/000347144
Volume/Issue:
Volume 2: Issue 1
Free access