Role of proton MR spectroscopy in the differentiation of borderline from malignant epithelial ovarian tumors: A preliminary study

Feng, Hua; Li, Yong Ai; Liu, Jia; Li, Hai Ming; Zhang, Guo Fu; Qiang, Jin Wei

doi:10.1002/jmri.26541

Cited by 17 publications

(18 citation statements)

References 28 publications

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“…In a traditional MRI reading session, the imaging signs always overlap with each other to some extent (for example, large size, solid components, irregular and thick septa) and lead to an inaccurate diagnosis (27,(30)(31)(32). A recent study with proton MRS reported that the SEN and SPE were 91% and 100% for solid components, respectively; additionally, the SEN and SPE were 84% and 82% for cystic components, respectively (12). However, MRS scans are highly unit-dependent and time-consuming examinations and require operators with more experience than conventional methods do.…”

Section: Discussionmentioning

confidence: 99%

“…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 58-100% and a speci city of 61-100%, respectively (7,(10)(11)(12)(13). Functional MRI scans (dynamic contrast-enhanced MRI, diffusion-weighted imaging(DWI), MR spectroscopy(MRS), etc.)…”

Section: Introductionmentioning

confidence: 99%

“…Functional MRI scans (dynamic contrast-enhanced MRI, diffusion-weighted imaging(DWI), MR spectroscopy(MRS), etc.) showed a higher ability to distinguish a BOT from ovarian epithelial cancer than conventional MRI, such as T1-weighted imaging (T1WI) and T2WI, as shown in recently published studies (11,12). However, given that functional MRI acquisition is not routinely used in clinical scenarios, the scanning parameters are not presently standardized universally and may change across MRI machines or institutions.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Wang

Zhang

et al. 2021

Preprint

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Background: Accurate discrimination between ovarian borderline tumors (BOTs) and malignancies with imaging play an important role in management.Purpose: To evaluate the ability of T2-weighted imaging (T2WI)-based radiomics to discriminate ovarian borderline tumors (BOTs) from malignancies based on two-dimensional (2D) and three-dimensional (3D) lesion segmentation methods.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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Liu

Wang

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

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

View full text Add to dashboard Cite

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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“…This 'data-driven' approach which enables the extraction of innumerable quantitative features from tomographic images has already shown promising results in the classification of ovarian epithelial cancer, as well as in predicting several outcome measures [56,57]. MR spectroscopy has also been reported to play a role in differentiating between borderline and malignant epithelial ovarian tumours [58]. However, epithelial tumours are rare in the paediatric population.…”

Section: Discussionmentioning

confidence: 99%

MR imaging in discriminating between benign and malignant paediatric ovarian masses: a systematic review

et al. 2019

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Objectives The use of magnetic resonance (MR) imaging in differentiation between benign and malignant adnexal masses in children and adolescents might be of great value in the diagnostic workup of sonographically indeterminate masses, since preserving fertility is of particular importance in this population. This systematic review evaluates the diagnostic value of MR imaging in children with an ovarian mass. Methods The review was made according to the PRISMA Statement. PubMed and EMBASE were systematically searched for studies on the use of MR imaging in differential diagnosis of ovarian masses in both adult women and children from 2008 to 2018.Results Sixteen paediatric and 18 adult studies were included. In the included studies, MR imaging has shown good diagnostic performance in differentiating between benign and malignant ovarian masses. MR imaging techniques including diffusionweighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging seem to further improve the diagnostic performance. Conclusion The addition of DWI with apparent diffusion coefficient (ADC) values measured in enhancing components of solid lesions and DCE imaging may further increase the good diagnostic performance of MR imaging in the pre-operative differentiation between benign and malignant ovarian masses by increasing specificity. Prospective age-specific studies are needed to confirm the high diagnostic performance of MR imaging in children and adolescents with a sonographically indeterminate ovarian mass. Key Points • MR imaging, based on several morphological features, is of good diagnostic performance in differentiating between benign and malignant ovarian masses. Sensitivity and specificity varied between 84.8 to 100% and 20.0 to 98.4%, respectively. • MR imaging techniques like diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging seem to improve the diagnostic performance. • Specific studies in children and adolescents with ovarian masses are required to confirm the suggested increased diagnostic performance of DWI and DCE in this population.

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Role of proton MR spectroscopy in the differentiation of borderline from malignant epithelial ovarian tumors: A preliminary study

Cited by 17 publications

References 28 publications

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

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

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

MR imaging in discriminating between benign and malignant paediatric ovarian masses: a systematic review

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