Literature DB >> 30244368

A computer-aided diagnosis system for the assessment and characterization of low-to-high suspicion thyroid nodules on ultrasound.

Salvatore Gitto1, Giorgia Grassi2, Chiara De Angelis3, Cristian Giuseppe Monaco3, Silvana Sdao4, Francesco Sardanelli5,6, Luca Maria Sconfienza6,7, Giovanni Mauri8.   

Abstract

AIM OF THE STUDY: To compare the diagnostic performance of a commercially available computer-aided diagnosis (CAD) system for thyroid ultrasound (US) with that of a non-computer-aided radiologist in the characterization of low-to-high suspicion thyroid nodules.
METHODS: This retrospective study included a consecutive series of adult patients referred for US-guided fine-needle aspiration biopsy (FNAB) of a thyroid nodule. All patients were eligible for thyroid nodule FNAB according to the current international guidelines. An interventional radiologist experienced in thyroid imaging acquired the US images subsequently used for post-processing, performed FNAB and provided the US features of each nodule. A radiology resident and an endocrinology resident in consensus performed post-processing using the CAD system to assess the same nodule characteristics. The diagnostic performance and agreement of US features between the CAD system and the radiologist were compared.
RESULTS: Sixty-two patients (50 F; age 60 ± 12 years) were enrolled: 77.4% (48/62) of thyroid nodules were benign, 22.6% (14/62) were undetermined to malignant and required follow-up or surgery. Interobserver agreement between the CAD system and the radiologist was substantial for orientation (K = 0.69), fair for composition (K = 0.36), echogenicity (K = 0.36), K-TIRADS (K = 0.29), and slight for margins (K = 0.03). The radiologist demonstrated a significantly higher sensitivity than the CAD system (78.6% vs. 21.4%; P = 0.008), while there was no statistical difference in specificity (66.7% vs. 81.3%; P = 0.065).
CONCLUSION: This CAD system is less sensitive than an experienced radiologist and showed slight-to-substantial agreement with the radiologist for the characterization of thyroid nodules. Although it is an innovative tool with good potential, additional efforts are needed to improve its diagnostic performance.

Entities:  

Keywords:  Computer-aided diagnosis; Nodule; Thyroid; Ultrasound

Mesh:

Year:  2018        PMID: 30244368     DOI: 10.1007/s11547-018-0942-z

Source DB:  PubMed          Journal:  Radiol Med        ISSN: 0033-8362            Impact factor:   3.469


  27 in total

1.  The results of ultrasound-guided fine-needle aspiration biopsy for evaluation of nodular thyroid disease.

Authors:  Elizabeth A Mittendorf; Stephen W Tamarkin; Christopher R McHenry
Journal:  Surgery       Date:  2002-10       Impact factor: 3.982

2.  Characterization of thyroid nodules using the proposed thyroid imaging reporting and data system (TI-RADS).

Authors:  Shih-Ping Cheng; Jie-Jen Lee; Jiun-Lu Lin; Shih-Ming Chuang; Ming-Nan Chien; Chien-Liang Liu
Journal:  Head Neck       Date:  2012-04-19       Impact factor: 3.147

3.  Interobserver and intraobserver variations in ultrasound assessment of thyroid nodules.

Authors:  Seon Hyeong Choi; Eun-Kyung Kim; Jin Young Kwak; Min Jung Kim; Eun Ju Son
Journal:  Thyroid       Date:  2010-02       Impact factor: 6.568

Review 4.  Image-guided thermal ablation of benign thyroid nodules.

Authors:  Anna Pisani Mainini; Cristian Monaco; Lorenzo Carlo Pescatori; Chiara De Angelis; Francesco Sardanelli; Luca Maria Sconfienza; Giovanni Mauri
Journal:  J Ultrasound       Date:  2016-10-21

5.  A computer-aided diagnosis system for the assessment and characterization of low-to-high suspicion thyroid nodules on ultrasound.

Authors:  Salvatore Gitto; Giorgia Grassi; Chiara De Angelis; Cristian Giuseppe Monaco; Silvana Sdao; Francesco Sardanelli; Luca Maria Sconfienza; Giovanni Mauri
Journal:  Radiol Med       Date:  2018-09-22       Impact factor: 3.469

6.  Thyroid Imaging Reporting and Data System Risk Stratification of Thyroid Nodules: Categorization Based on Solidity and Echogenicity.

Authors:  Dong Gyu Na; Jung Hwan Baek; Jin Yong Sung; Ji-Hoon Kim; Jae Kyun Kim; Young Jun Choi; Hyobin Seo
Journal:  Thyroid       Date:  2016-02-09       Impact factor: 6.568

7.  ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform.

Authors:  U Rajendra Acharya; Oliver Faust; S Vinitha Sree; Filippo Molinari; Jasjit S Suri
Journal:  Comput Methods Programs Biomed       Date:  2011-11-04       Impact factor: 5.428

8.  Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan™ systems.

Authors:  U Rajendra Acharya; S Vinitha Sree; M Muthu Rama Krishnan; Filippo Molinari; Roberto Garberoglio; Jasjit S Suri
Journal:  Ultrasonics       Date:  2011-11-25       Impact factor: 2.890

9.  Current thyroid cancer trends in the United States.

Authors:  Louise Davies; H Gilbert Welch
Journal:  JAMA Otolaryngol Head Neck Surg       Date:  2014-04       Impact factor: 6.223

Review 10.  Comparison of diagnostic yield of core-needle and fine-needle aspiration biopsies of thyroid lesions: Systematic review and meta-analysis.

Authors:  Kosma Wolinski; Adam Stangierski; Marek Ruchala
Journal:  Eur Radiol       Date:  2016-04-18       Impact factor: 5.315

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  20 in total

1.  Clinical practice guidelines on ultrasound-guided fine needle aspiration biopsy of thyroid nodules: a critical appraisal using AGREE II.

Authors:  Salvatore Gitto; Sotirios Bisdas; Ilaria Emili; Luca Nicosia; Lorenzo Carlo Pescatori; Kunwar Bhatia; Ravi K Lingam; Francesco Sardanelli; Luca Maria Sconfienza; Giovanni Mauri
Journal:  Endocrine       Date:  2019-03-22       Impact factor: 3.633

2.  A computer-aided diagnosis system for the assessment and characterization of low-to-high suspicion thyroid nodules on ultrasound.

Authors:  Salvatore Gitto; Giorgia Grassi; Chiara De Angelis; Cristian Giuseppe Monaco; Silvana Sdao; Francesco Sardanelli; Luca Maria Sconfienza; Giovanni Mauri
Journal:  Radiol Med       Date:  2018-09-22       Impact factor: 3.469

3.  Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners.

Authors:  Daniele Fresilli; Giorgio Grani; Maria Luna De Pascali; Gregorio Alagna; Eleonora Tassone; Valeria Ramundo; Valeria Ascoli; Daniela Bosco; Marco Biffoni; Marco Bononi; Vito D'Andrea; Fabrizio Frattaroli; Laura Giacomelli; Yana Solskaya; Giorgia Polti; Patrizia Pacini; Olga Guiban; Raffaele Gallo Curcio; Marcello Caratozzolo; Vito Cantisani
Journal:  J Ultrasound       Date:  2020-04-03

4.  Use of the Thyroid Imaging Reporting and Data System (TIRADS) in clinical practice: an Italian survey.

Authors:  Giovanni Mauri; Salvatore Gitto; Vito Cantisani; Gianfranco Vallone; Cosima Schiavone; Enrico Papini; Luca Maria Sconfienza
Journal:  Endocrine       Date:  2020-01-25       Impact factor: 3.633

5.  Interobserver agreement and efficacy of consensus reading in Kwak-, EU-, and ACR-thyroid imaging recording and data systems and ATA guidelines for the ultrasound risk stratification of thyroid nodules.

Authors:  Philipp Seifert; Rainer Görges; Michael Zimny; Michael C Kreissl; Simone Schenke
Journal:  Endocrine       Date:  2019-11-18       Impact factor: 3.633

6.  [Value of ultrasonic S-Detect technique in diagnosis of breast masses].

Authors:  Y Cheng; Q Xia; J Wang; H Xie; Y Yu; H Liu; Z Yao; J Hu
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2022-07-20

7.  A comparison of artificial intelligence versus radiologists in the diagnosis of thyroid nodules using ultrasonography: a systematic review and meta-analysis.

Authors:  Pimrada Potipimpanon; Natamon Charakorn; Prakobkiat Hirunwiwatkul
Journal:  Eur Arch Otorhinolaryngol       Date:  2022-06-29       Impact factor: 3.236

Review 8.  Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis.

Authors:  Lei Xu; Junling Gao; Quan Wang; Jichao Yin; Pengfei Yu; Bin Bai; Ruixia Pei; Dingzhang Chen; Guochun Yang; Shiqi Wang; Mingxi Wan
Journal:  Eur Thyroid J       Date:  2019-12-04

9.  Clinical validation of S-DetectTM mode in semi-automated ultrasound classification of thyroid lesions in surgical office.

Authors:  Marcin Barczyński; Małgorzata Stopa-Barczyńska; Beata Wojtczak; Agnieszka Czarniecka; Aleksander Konturek
Journal:  Gland Surg       Date:  2020-02

10.  TIRADS, SRE and SWE in INDETERMINATE thyroid nodule characterization: Which has better diagnostic performance?

Authors:  Ilaria Celletti; Daniele Fresilli; Corrado De Vito; Marco Bononi; Sara Cardaccio; Alessia Cozzolino; Cosimo Durante; Giorgio Grani; Gianmarco Grimaldi; Andrea M Isidori; Carlo Catalano; Vito Cantisani
Journal:  Radiol Med       Date:  2021-06-15       Impact factor: 3.469

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