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Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts
The imaging diagnosis of malignant ovarian cysts relies on their morphological features, which are not always specific to malignancy. The histological analysis of these cysts shows specific fluid characteristics, which cannot be assessed by conventional imaging techniques. This study investigates wh...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7563604/ https://www.ncbi.nlm.nih.gov/pubmed/32937851 http://dx.doi.org/10.3390/jpm10030127 |
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author | Lupean, Roxana-Adelina Ștefan, Paul-Andrei Feier, Diana Sorina Csutak, Csaba Ganeshan, Balaji Lebovici, Andrei Petresc, Bianca Mihu, Carmen Mihaela |
author_facet | Lupean, Roxana-Adelina Ștefan, Paul-Andrei Feier, Diana Sorina Csutak, Csaba Ganeshan, Balaji Lebovici, Andrei Petresc, Bianca Mihu, Carmen Mihaela |
author_sort | Lupean, Roxana-Adelina |
collection | PubMed |
description | The imaging diagnosis of malignant ovarian cysts relies on their morphological features, which are not always specific to malignancy. The histological analysis of these cysts shows specific fluid characteristics, which cannot be assessed by conventional imaging techniques. This study investigates whether the texture-based radiomics analysis (TA) of magnetic resonance (MRI) images of the fluid content within ovarian cysts can function as a noninvasive tool in differentiating between benign and malignant lesions. Twenty-eight patients with benign (n = 15) and malignant (n = 13) ovarian cysts who underwent MRI examinations were retrospectively included. TA of the fluid component was undertaken on an axial T2-weighted sequence. A comparison of resulted parameters between benign and malignant groups was undertaken using univariate, multivariate, multiple regression, and receiver operating characteristics analyses, with the calculation of the area under the curve (AUC). The standard deviation of pixel intensity was identified as an independent predictor of malignant cysts (AUC = 0.738; sensitivity, 61.54%; specificity, 86.67%). The prediction model was able to identify malignant lesions with 84.62% sensitivity and 80% specificity (AUC = 0.841). TA of the fluid contained within the ovarian cysts can differentiate between malignant and benign lesions and potentially act as a noninvasive tool augmenting the imaging diagnosis of ovarian cystic lesions. |
format | Online Article Text |
id | pubmed-7563604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75636042020-10-27 Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts Lupean, Roxana-Adelina Ștefan, Paul-Andrei Feier, Diana Sorina Csutak, Csaba Ganeshan, Balaji Lebovici, Andrei Petresc, Bianca Mihu, Carmen Mihaela J Pers Med Article The imaging diagnosis of malignant ovarian cysts relies on their morphological features, which are not always specific to malignancy. The histological analysis of these cysts shows specific fluid characteristics, which cannot be assessed by conventional imaging techniques. This study investigates whether the texture-based radiomics analysis (TA) of magnetic resonance (MRI) images of the fluid content within ovarian cysts can function as a noninvasive tool in differentiating between benign and malignant lesions. Twenty-eight patients with benign (n = 15) and malignant (n = 13) ovarian cysts who underwent MRI examinations were retrospectively included. TA of the fluid component was undertaken on an axial T2-weighted sequence. A comparison of resulted parameters between benign and malignant groups was undertaken using univariate, multivariate, multiple regression, and receiver operating characteristics analyses, with the calculation of the area under the curve (AUC). The standard deviation of pixel intensity was identified as an independent predictor of malignant cysts (AUC = 0.738; sensitivity, 61.54%; specificity, 86.67%). The prediction model was able to identify malignant lesions with 84.62% sensitivity and 80% specificity (AUC = 0.841). TA of the fluid contained within the ovarian cysts can differentiate between malignant and benign lesions and potentially act as a noninvasive tool augmenting the imaging diagnosis of ovarian cystic lesions. MDPI 2020-09-14 /pmc/articles/PMC7563604/ /pubmed/32937851 http://dx.doi.org/10.3390/jpm10030127 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 Lupean, Roxana-Adelina Ștefan, Paul-Andrei Feier, Diana Sorina Csutak, Csaba Ganeshan, Balaji Lebovici, Andrei Petresc, Bianca Mihu, Carmen Mihaela Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts |
title | Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts |
title_full | Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts |
title_fullStr | Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts |
title_full_unstemmed | Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts |
title_short | Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts |
title_sort | radiomic analysis of mri images is instrumental to the stratification of ovarian cysts |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7563604/ https://www.ncbi.nlm.nih.gov/pubmed/32937851 http://dx.doi.org/10.3390/jpm10030127 |
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