Development and Validation of Multiparametric MRI–based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer

Abstract

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Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features

“…Endometrial cancer is the sixth most common malignant disorder worldwide with an estimated 382,000 new cases diagnosed annually [ 1 ]. Magnetic resonance imaging (MRI) has been extensively applied in endometrial cancer staging [ 2 , 3 , 4 , 5 ] and is usually the imaging modality of choice for assessment of deep myometrial invasion (DMI) [ 6 , 7 , 8 , 9 ]. Studies have shown that using MRI alone, endometrial cancer DMI prediction [ 10 ] accuracies were estimated to be 74% [ 11 ], 76% [ 12 ], and 89% [ 13 ].…”

“…However, these accuracies are subjective as it is dependent on the radiologist’s ability to detect subtle imaging textures on MR images. To overcome these limitations, radiomics [ 14 , 15 ], which extracts quantitative image features from medical imaging, has been proposed to predict DMI [ 8 , 9 , 16 ]. Nevertheless, relatively few studies to date have focused on identifying clinical prognostic factors other than stage, using radiomic features from MRI [ 17 ].…”

“…Thus far, studies using radiomics for DMI classification prediction [ 8 , 10 , 20 ] have applied only one machine learning method, which may not represent the optimal predictive ability of machine learning. For example, a recent study [ 8 ] adopted a random forest to assess DMI, whilst a second study [ 21 ] applied a logistic regression method. Other studies used a support vector machine (SVM) method [ 20 ] and logistic regression [ 22 ] for risk stratification.…”

Utility of the Diffusion Weighted Sequence in Gynecological Imaging: Review Article

“…For proper measurement of ADC values, the ROI should be placed to avoid any areas of necrosis and cystic components of the lesion to avoid false elevation of values [ 44 ]. Three dimensional radiomics using DWI can help identification of endometrial cancer patients with high-risk histopathological factors such as advanced stage and deep myometrial invasion [ 45 ]. However, the study by Bereby-Kahane et al, reported limited value of radiomics analysis based on T2-weighted and DWI MRI [ 46 ].…”

Reproducibility of 2D versus 3D radiomics for quantitative assessment of fetal lung development: a retrospective fetal MRI study

“…The use of radiomics, a quantitative image analysis method that is used to extract a large number of features from medical image data, has been proposed to increase the diagnostic and prognostic imaging yield in a variety of conditions [5][6][7]. Recently, studies have highlighted the potential of radiomics for the identification of fetuses with abnormal lung development and texture-based neonatal respiratory distress prediction [8][9][10].…”