Cargando…
Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions
RESULTS: The 34 nodules comprised 14 benign nodules and 20 malignant nodules. Iodine content and Hounsfield unit curve slopes did not differ significantly between benign and malignant thyroid nodules (P = 0.480–0.670). However, significant differences in the texture features of monochromatic images...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104273/ https://www.ncbi.nlm.nih.gov/pubmed/32256574 http://dx.doi.org/10.1155/2020/5484671 |
_version_ | 1783512204407996416 |
---|---|
author | Tomita, Hayato Kuno, Hirofumi Sekiya, Kotaro Otani, Katharina Sakai, Osamu Li, Baojun Hiyama, Takashi Nomura, Keiichi Mimura, Hidefumi Kobayashi, Tatsushi |
author_facet | Tomita, Hayato Kuno, Hirofumi Sekiya, Kotaro Otani, Katharina Sakai, Osamu Li, Baojun Hiyama, Takashi Nomura, Keiichi Mimura, Hidefumi Kobayashi, Tatsushi |
author_sort | Tomita, Hayato |
collection | PubMed |
description | RESULTS: The 34 nodules comprised 14 benign nodules and 20 malignant nodules. Iodine content and Hounsfield unit curve slopes did not differ significantly between benign and malignant thyroid nodules (P = 0.480–0.670). However, significant differences in the texture features of monochromatic images were observed between benign and malignant nodules: histogram mean and median, co-occurrence matrix contrast, gray-level gradient matrix (GLGM) skewness, and mean gradients and variance of gradients for GLGM at 80 keV (P = 0.014–0.044). The highest AUC was 0.77, for the histogram mean and median of images acquired at 80 keV. CONCLUSIONS: Texture features extracted from monochromatic images using DECT, specifically acquired at high keV, may be a promising diagnostic approach for thyroid nodules. A further large study for incidental thyroid nodules using DECT texture analysis is required to validate our results. |
format | Online Article Text |
id | pubmed-7104273 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-71042732020-04-03 Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions Tomita, Hayato Kuno, Hirofumi Sekiya, Kotaro Otani, Katharina Sakai, Osamu Li, Baojun Hiyama, Takashi Nomura, Keiichi Mimura, Hidefumi Kobayashi, Tatsushi Int J Endocrinol Research Article RESULTS: The 34 nodules comprised 14 benign nodules and 20 malignant nodules. Iodine content and Hounsfield unit curve slopes did not differ significantly between benign and malignant thyroid nodules (P = 0.480–0.670). However, significant differences in the texture features of monochromatic images were observed between benign and malignant nodules: histogram mean and median, co-occurrence matrix contrast, gray-level gradient matrix (GLGM) skewness, and mean gradients and variance of gradients for GLGM at 80 keV (P = 0.014–0.044). The highest AUC was 0.77, for the histogram mean and median of images acquired at 80 keV. CONCLUSIONS: Texture features extracted from monochromatic images using DECT, specifically acquired at high keV, may be a promising diagnostic approach for thyroid nodules. A further large study for incidental thyroid nodules using DECT texture analysis is required to validate our results. Hindawi 2020-03-18 /pmc/articles/PMC7104273/ /pubmed/32256574 http://dx.doi.org/10.1155/2020/5484671 Text en Copyright © 2020 Hayato Tomita et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Tomita, Hayato Kuno, Hirofumi Sekiya, Kotaro Otani, Katharina Sakai, Osamu Li, Baojun Hiyama, Takashi Nomura, Keiichi Mimura, Hidefumi Kobayashi, Tatsushi Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions |
title | Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions |
title_full | Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions |
title_fullStr | Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions |
title_full_unstemmed | Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions |
title_short | Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions |
title_sort | quantitative assessment of thyroid nodules using dual-energy computed tomography: iodine concentration measurement and multiparametric texture analysis for differentiating between malignant and benign lesions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104273/ https://www.ncbi.nlm.nih.gov/pubmed/32256574 http://dx.doi.org/10.1155/2020/5484671 |
work_keys_str_mv | AT tomitahayato quantitativeassessmentofthyroidnodulesusingdualenergycomputedtomographyiodineconcentrationmeasurementandmultiparametrictextureanalysisfordifferentiatingbetweenmalignantandbenignlesions AT kunohirofumi quantitativeassessmentofthyroidnodulesusingdualenergycomputedtomographyiodineconcentrationmeasurementandmultiparametrictextureanalysisfordifferentiatingbetweenmalignantandbenignlesions AT sekiyakotaro quantitativeassessmentofthyroidnodulesusingdualenergycomputedtomographyiodineconcentrationmeasurementandmultiparametrictextureanalysisfordifferentiatingbetweenmalignantandbenignlesions AT otanikatharina quantitativeassessmentofthyroidnodulesusingdualenergycomputedtomographyiodineconcentrationmeasurementandmultiparametrictextureanalysisfordifferentiatingbetweenmalignantandbenignlesions AT sakaiosamu quantitativeassessmentofthyroidnodulesusingdualenergycomputedtomographyiodineconcentrationmeasurementandmultiparametrictextureanalysisfordifferentiatingbetweenmalignantandbenignlesions AT libaojun quantitativeassessmentofthyroidnodulesusingdualenergycomputedtomographyiodineconcentrationmeasurementandmultiparametrictextureanalysisfordifferentiatingbetweenmalignantandbenignlesions AT hiyamatakashi quantitativeassessmentofthyroidnodulesusingdualenergycomputedtomographyiodineconcentrationmeasurementandmultiparametrictextureanalysisfordifferentiatingbetweenmalignantandbenignlesions AT nomurakeiichi quantitativeassessmentofthyroidnodulesusingdualenergycomputedtomographyiodineconcentrationmeasurementandmultiparametrictextureanalysisfordifferentiatingbetweenmalignantandbenignlesions AT mimurahidefumi quantitativeassessmentofthyroidnodulesusingdualenergycomputedtomographyiodineconcentrationmeasurementandmultiparametrictextureanalysisfordifferentiatingbetweenmalignantandbenignlesions AT kobayashitatsushi quantitativeassessmentofthyroidnodulesusingdualenergycomputedtomographyiodineconcentrationmeasurementandmultiparametrictextureanalysisfordifferentiatingbetweenmalignantandbenignlesions |