MRI features and score for differentiating borderline from malignant epithelial ovarian tumors

Li, Yong Ai; Qiang, Jin Wei; Feng, Hua; Li, Hai Ming; Zhao, Siwen

doi:10.1016/j.ejrad.2017.11.014

Cited by 38 publications

(32 citation statements)

References 19 publications

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“…All MR images were reviewed independently by two radiologists (with 3 and 30 years of experience in abdominal MRI, respectively) who were blinded to the clinical information and the lesion type (either BEOTs or MEOTs). Similar to previous studies (18,19), tumors were evaluated based on the following features: 1) morphology, classified as either lobulated (notches ≥ 2) or round/oval; 2) configuration, which was classified as either purely cystic, predominantly cystic (≤ 1/3 solid component), mixed cystic/ solid (1/3 to 2/3 solid component), or predominantly-solid ) the presence hemorrhagic fluid in the loculi; 8) enhancement degree of the solid component or septa/wall, categorized as either mild, moderate, or significant based on the signal of the iliopsoas and myometrium; 9) the presence of ascites, graded as either mild (limited to the Douglas pouch), moderate (limited to the pelvic cavity), or massive (beyond the pelvic); and 10) the presence of a peritoneal implant.…”

Section: Conventional Mri Features Analysissupporting

confidence: 91%

Texture Analysis of Three-Dimensional MRI Images May Differentiate Borderline and Malignant Epithelial Ovarian Tumors

Weng

et al. 2021

Korean J Radiol

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Objective: To explore the value of magnetic resonance imaging (MRI)-based whole tumor texture analysis in differentiating borderline epithelial ovarian tumors (BEOTs) from FIGO stage I/II malignant epithelial ovarian tumors (MEOTs). Materials and Methods: A total of 88 patients with histopathologically confirmed ovarian epithelial tumors after surgical resection, including 30 BEOT and 58 MEOT patients, were divided into a training group (n = 62) and a test group (n = 26). The clinical and conventional MRI features were retrospectively reviewed. The texture features of tumors, based on T2weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging, were extracted using MaZda software and the three top weighted texture features were selected by using the Random Forest algorithm. A non-texture logistic regression model in the training group was built to include those clinical and conventional MRI variables with p value < 0.10. Subsequently, a combined model integrating non-texture information and texture features was built for the training group. The model, evaluated using patients in the training group, was then applied to patients in the test group. Finally, receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the models. Results: The combined model showed superior performance in categorizing BEOTs and MEOTs (sensitivity, 92.5%; specificity, 86.4%; accuracy, 90.3%; area under the ROC curve [AUC], 0.962) than the non-texture model (sensitivity, 78.3%; specificity, 84.6%; accuracy, 82.3%; AUC, 0.818). The AUCs were statistically different (p value = 0.038). In the test group, the AUCs, sensitivity, specificity, and accuracy were 0.840, 73.3%, 90.1%, and 80.8% when the non-texture model was used and 0.896, 75.0%, 94.0%, and 88.5% when the combined model was used. Conclusion: MRI-based texture features combined with clinical and conventional MRI features may assist in differentitating between BEOT and FIGO stage I/II MEOT patients.

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Section: Conventional Mri Features Analysissupporting

confidence: 91%

Texture Analysis of Three-Dimensional MRI Images May Differentiate Borderline and Malignant Epithelial Ovarian Tumors

Weng

et al. 2021

Korean J Radiol

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“…Morphological features (maximal diameter, mass configuration) were determined on conventional T 1 ‐ and T 2 ‐weighted images. Depending on the proportion of solid components, mass configurations were described as mainly cystic (tumor with less than one‐third solid component); mixed cystic‐solid (tumor with 1/3–2/3 solid component); and mainly solid (tumor with more than 2/3 solid component) . The solid component included solid portions, mural nodules, papillary projections, and irregularly thickened septa .…”

Section: Methodsmentioning

confidence: 99%

“…Magnetic resonance imaging (MRI) plays a crucial role in differentiating BOETs from MOETs and for guiding surgical planning . Both tissue molecular diffusion reflecting cellularity, and microcirculation of capillaries reflecting vascularity of ovarian tumors, are important biomarkers for differentiating borderline from malignant OETs .…”

mentioning

confidence: 99%

Intravoxel Incoherent Motion Imaging in Differentiation Borderline From Malignant Ovarian Epithelial Tumors: Correlation With Histological Cell Proliferation and Vessel Characteristics

Song

Wang

Ren

et al. 2019

Magnetic Resonance Imaging

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Background The differentiation of borderline from malignant ovarian epithelial tumors (OETs) is difficult based on morphologic characteristics. Diffusion and perfusion information from intravoxel incoherent motion (IVIM) diffusion‐weighted imaging (DWI) might be useful for this distinction. Purpose To investigate the potential of IVIM‐DWI in discriminating borderline from malignant OETs by correlating with cell proliferation and microvessel density (MVD). Study Type Prospective. Subjects Sixty‐six patients with OETs (22 borderline, BOETs; 44 malignant, MOETs) underwent IVIM‐DWI before surgery. Field Strength 3.0T IVIM‐DWI sequence with 12 b‐values (0–1000 sec/mm2). Assessment Apparent diffusion coefficient (ADC) and IVIM‐DWI parameters (diffusion coefficient [D], microvascular volume fraction [f], and pseudodiffusion coefficient [D*]) were measured. Cell proliferation and MVD was determined by immunohistochemical staining of Ki‐67 and CD‐31, respectively. Statistical Tests Mann–Whitney U‐test; two‐sample t‐test; intraclass correlation coefficient; Bland–Altman analysis; receiver operating characteristics (ROC) curves; and Spearman correlation. Results ADC and D in BOETs was significantly higher than those in MOETs (P < 0.001), while f in BOETs was significantly lower than those in MOETs (P < 0.001). No significant difference was found in D* between borderline and malignancies (P = 0.324). In the differential diagnosis of borderline from malignant OETs; D demonstrated the highest area under the curve (AUC) of 0.951, while ADC and f had a lower AUC of 0.921 and 0.847, respectively. The ADC and D was negatively correlated with cell proliferation (r = –0.682, r = –0.694, respectively, P < 0.001), while f was positively correlated with MVD of the OETs (r = 0.558, P < 0.001). Data Conclusion IVIM‐DWI may be a useful tool for differentiating BOETs from MOETs. D and f could be image biomarkers to reflect histopathological characteristics of cell proliferation and MVD in OETs. Level of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:928–935.

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“…Magnetic resonance imaging (MRI) has many advantages in determining the etiology of ovarian masses and is widely used in clinical centers (4). MRI has high diagnostic performance in differentiating between ovarian benign tumors and malignant tumors (5)(6)(7)(8)(9). Considering the ability to discriminate BOTs from malignant epithelial ovarian tumors, conventional MRI varies with a sensitivity of 58% to 100% and a speci city of 61% to 100%, respectively (7, 10-13).…”

Section: Introductionmentioning

confidence: 99%

The Two-dimensional and Three-dimensional T2 Weighted Imaging-based Radiomic Signatures for the Preoperative Discrimination of Ovarian Borderline Tumors and Epithelial Cancer.

Liu

Wang

Zhang

et al. 2020

Preprint

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Background: Accurate discrimination between ovarian borderline tumors (BOTs) and malignancies with imaging play an important role in management.Methods: A total of 95 patients with pathologically proven ovarian BOTs and 101 patients with malignancies were retrospectively included in this study. We evaluated the diagnostic performance of the signatures derived from T2WI-based radiomics in their ability to differentiate between BOTs and malignancies and compared the performance differences in the 2D and 3D segmentation models. The least absolute shrinkage and selection operator method (LASSO) was used for radiomics feature selection and machine learning processing.Results: The radiomics score between BOTs and malignancies in four types of selected T2WI-based radiomics models differed significantly at the statistical level (p < 0.0001). For the classification between BOTs and malignant masses, the 2D and 3D coronal T2WI-based radiomics models yielded accuracy values of 0.79 and 0.83 in the testing group, respectively; the 2D and 3D sagittal fat-suppressed (fs) T2WI-based radiomics models yielded an accuracy of 0.78 and 0.99, respectively.Conclusion: Our results suggest that T2WI-based radiomic features were highly correlated with ovarian tumor subtype classification. 3D-sagittal MRI radiomics features may help clinicians differentiate ovarian BOTs from malignancies with high accuracy (ACC).

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MRI features and score for differentiating borderline from malignant epithelial ovarian tumors

Cited by 38 publications

References 19 publications

Texture Analysis of Three-Dimensional MRI Images May Differentiate Borderline and Malignant Epithelial Ovarian Tumors

Texture Analysis of Three-Dimensional MRI Images May Differentiate Borderline and Malignant Epithelial Ovarian Tumors

Intravoxel Incoherent Motion Imaging in Differentiation Borderline From Malignant Ovarian Epithelial Tumors: Correlation With Histological Cell Proliferation and Vessel Characteristics

The Two-dimensional and Three-dimensional T2 Weighted Imaging-based Radiomic Signatures for the Preoperative Discrimination of Ovarian Borderline Tumors and Epithelial Cancer.

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