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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...

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Autores principales: Lupean, Roxana-Adelina, Ștefan, Paul-Andrei, Feier, Diana Sorina, Csutak, Csaba, Ganeshan, Balaji, Lebovici, Andrei, Petresc, Bianca, Mihu, Carmen Mihaela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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.
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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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