Cargando…
S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules
Computer-aided diagnosis (CAD) and other risk stratification systems may improve ultrasound image interpretation. This prospective study aimed to compare the diagnostic performance of CAD and the European Thyroid Imaging Reporting and Data System (EU-TIRADS) classification applied by physicians with...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7464710/ https://www.ncbi.nlm.nih.gov/pubmed/32756510 http://dx.doi.org/10.3390/jcm9082495 |
_version_ | 1783577426014502912 |
---|---|
author | Szczepanek-Parulska, Ewelina Wolinski, Kosma Dobruch-Sobczak, Katarzyna Antosik, Patrycja Ostalowska, Anna Krauze, Agnieszka Migda, Bartosz Zylka, Agnieszka Lange-Ratajczak, Malgorzata Banasiewicz, Tomasz Dedecjus, Marek Adamczewski, Zbigniew Slapa, Rafal Z. Mlosek, Robert K. Lewinski, Andrzej Ruchala, Marek |
author_facet | Szczepanek-Parulska, Ewelina Wolinski, Kosma Dobruch-Sobczak, Katarzyna Antosik, Patrycja Ostalowska, Anna Krauze, Agnieszka Migda, Bartosz Zylka, Agnieszka Lange-Ratajczak, Malgorzata Banasiewicz, Tomasz Dedecjus, Marek Adamczewski, Zbigniew Slapa, Rafal Z. Mlosek, Robert K. Lewinski, Andrzej Ruchala, Marek |
author_sort | Szczepanek-Parulska, Ewelina |
collection | PubMed |
description | Computer-aided diagnosis (CAD) and other risk stratification systems may improve ultrasound image interpretation. This prospective study aimed to compare the diagnostic performance of CAD and the European Thyroid Imaging Reporting and Data System (EU-TIRADS) classification applied by physicians with S-Detect 2 software CAD based on Korean Thyroid Imaging Reporting and Data System (K-TIRADS) and combinations of both methods (MODELs 1 to 5). In all, 133 nodules from 88 patients referred to thyroidectomy with available histopathology or with unambiguous results of cytology were included. The S-Detect system, EU-TIRADS, and mixed MODELs 1–5 for the diagnosis of thyroid cancer showed a sensitivity of 89.4%, 90.9%, 84.9%, 95.5%, 93.9%, 78.9% and 93.9%; a specificity of 80.6%, 61.2%, 88.1%, 53.7%, 73.1%, 89.6% and 80.6%; a positive predictive value of 81.9%, 69.8%, 87.5%, 67%, 77.5%, 88.1% and 82.7%; a negative predictive value of 88.5%, 87.2%, 85.5%, 92.3%, 92.5%, 81.1% and 93.1%; and an accuracy of 85%, 75.9%, 86.5%, 74.4%, 83.5%, 84.2%, and 87.2%, respectively. Comparison showed superiority of the similar MODELs 1 and 5 over other mixed models as well as EU-TIRADS and S-Detect used alone (p-value < 0.05). S-Detect software is characterized with high sensitivity and good specificity, whereas EU-TIRADS has high sensitivity, but rather low specificity. The best diagnostic performance in malignant thyroid nodule (TN) risk stratification was obtained for the combined model of S-Detect (“possibly malignant” nodule) and simultaneously obtaining 4 or 5 points (MODEL 1) or exactly 5 points (MODEL 5) on the EU-TIRADS scale. |
format | Online Article Text |
id | pubmed-7464710 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74647102020-09-04 S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules Szczepanek-Parulska, Ewelina Wolinski, Kosma Dobruch-Sobczak, Katarzyna Antosik, Patrycja Ostalowska, Anna Krauze, Agnieszka Migda, Bartosz Zylka, Agnieszka Lange-Ratajczak, Malgorzata Banasiewicz, Tomasz Dedecjus, Marek Adamczewski, Zbigniew Slapa, Rafal Z. Mlosek, Robert K. Lewinski, Andrzej Ruchala, Marek J Clin Med Article Computer-aided diagnosis (CAD) and other risk stratification systems may improve ultrasound image interpretation. This prospective study aimed to compare the diagnostic performance of CAD and the European Thyroid Imaging Reporting and Data System (EU-TIRADS) classification applied by physicians with S-Detect 2 software CAD based on Korean Thyroid Imaging Reporting and Data System (K-TIRADS) and combinations of both methods (MODELs 1 to 5). In all, 133 nodules from 88 patients referred to thyroidectomy with available histopathology or with unambiguous results of cytology were included. The S-Detect system, EU-TIRADS, and mixed MODELs 1–5 for the diagnosis of thyroid cancer showed a sensitivity of 89.4%, 90.9%, 84.9%, 95.5%, 93.9%, 78.9% and 93.9%; a specificity of 80.6%, 61.2%, 88.1%, 53.7%, 73.1%, 89.6% and 80.6%; a positive predictive value of 81.9%, 69.8%, 87.5%, 67%, 77.5%, 88.1% and 82.7%; a negative predictive value of 88.5%, 87.2%, 85.5%, 92.3%, 92.5%, 81.1% and 93.1%; and an accuracy of 85%, 75.9%, 86.5%, 74.4%, 83.5%, 84.2%, and 87.2%, respectively. Comparison showed superiority of the similar MODELs 1 and 5 over other mixed models as well as EU-TIRADS and S-Detect used alone (p-value < 0.05). S-Detect software is characterized with high sensitivity and good specificity, whereas EU-TIRADS has high sensitivity, but rather low specificity. The best diagnostic performance in malignant thyroid nodule (TN) risk stratification was obtained for the combined model of S-Detect (“possibly malignant” nodule) and simultaneously obtaining 4 or 5 points (MODEL 1) or exactly 5 points (MODEL 5) on the EU-TIRADS scale. MDPI 2020-08-03 /pmc/articles/PMC7464710/ /pubmed/32756510 http://dx.doi.org/10.3390/jcm9082495 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Szczepanek-Parulska, Ewelina Wolinski, Kosma Dobruch-Sobczak, Katarzyna Antosik, Patrycja Ostalowska, Anna Krauze, Agnieszka Migda, Bartosz Zylka, Agnieszka Lange-Ratajczak, Malgorzata Banasiewicz, Tomasz Dedecjus, Marek Adamczewski, Zbigniew Slapa, Rafal Z. Mlosek, Robert K. Lewinski, Andrzej Ruchala, Marek S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules |
title | S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules |
title_full | S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules |
title_fullStr | S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules |
title_full_unstemmed | S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules |
title_short | S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules |
title_sort | s-detect software vs. eu-tirads classification: a dual-center validation of diagnostic performance in differentiation of thyroid nodules |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7464710/ https://www.ncbi.nlm.nih.gov/pubmed/32756510 http://dx.doi.org/10.3390/jcm9082495 |
work_keys_str_mv | AT szczepanekparulskaewelina sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT wolinskikosma sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT dobruchsobczakkatarzyna sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT antosikpatrycja sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT ostalowskaanna sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT krauzeagnieszka sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT migdabartosz sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT zylkaagnieszka sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT langeratajczakmalgorzata sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT banasiewicztomasz sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT dedecjusmarek sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT adamczewskizbigniew sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT slaparafalz sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT mlosekrobertk sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT lewinskiandrzej sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules AT ruchalamarek sdetectsoftwarevseutiradsclassificationadualcentervalidationofdiagnosticperformanceindifferentiationofthyroidnodules |