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Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts

Lupean, R-A; Ștefan, P-A; Feier, DS; Csutak, C; Ganeshan, B; Lebovici, A; Petresc, B; (2020) Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts. Journal of Personalized Medicine , 10 (3) , Article 127. 10.3390/jpm10030127. Green open access

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Abstract

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.

Type: Article
Title: Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/jpm10030127
Publisher version: https://doi.org/10.3390/jpm10030127
Language: English
Additional information: This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: computer-aided diagnosis; disease modeling; magnetic resonance imaging (MRI); ovarian cyst; patient stratification; personalized medicine; prediction; texture analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Department of Imaging
URI: https://discovery.ucl.ac.uk/id/eprint/10110506
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