CT and MRI findings of type I and type II epithelial ovarian cancer

Dong, Lei; Zhang, Lin; Indima, Nekitsing; Peng, Kun; Li, Qianyu; Ting, Hua; Tang, Guangyu

doi:10.1016/j.ejrad.2017.02.017

Cited by 25 publications

(12 citation statements)

References 27 publications

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“…Both Ki-67 expression and CA-125 level were higher in Type II than in the Type I cancer group, because the patients with Type II cancer were hospitalized with a more advanced FIGO stage than those in the Type I group. Similar findings have also been reported in recently published literature [20].…”

Section: Discussionsupporting

confidence: 93%

“…Type I and Type II OEC resemble two distinct ovarian cancer subtypes based on molecular characteristics [3]. The significance of distinguishing the two OEC subtypes is that Type I has different biological behaviors, treatment responses, and gene mutations from Type II [5], and Type II ovarian cancer (most being high-grade serous ovarian cancer) account for more than 70% of all ovarian cancer deaths [5, 18–20]. Considering the recent treatment strategy advancements [21–23], a minimally invasive approach and fertility-sparing approaches are necessary for patients with BOT tumors and patients of reproductive age with gynecological cancers [24, 25].…”

Section: Discussionmentioning

confidence: 99%

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Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level

et al. 2019

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Background To determine whether magnetic resonance (MR) imaging features combined with apparent diffusion coefficient (ADC ) values could be used as a tool for categorizing ovarian epithelial cancer (OEC) and predicting survival, as well as correlating with laboratory tests (serum cancer antigen 125, serum CA-125) and tumor proliferative index (Ki-67 expression). Methods and materials MRI examination was undertaken before invasive procedures. MRI features were interpreted and recorded on the picture archive communication system (PACS). ADC measurements were manually performed on post-process workstation. Clinical characteristics were individually retrieved and recorded through the hospital information system (HIS). Cox hazard model was used to estimate the effects of both clinical and MRI features on overall survival. Results Both clinical and MRI features differed significantly between Type I and Type II cancer groups ( p < 0.05). The mean ADC value was inversely correlated with Ki-67 expression in Type I cancer ( ρ = − 0.14, p < 0.05). A higher mean ADC value was more likely to suggest Type I ovarian cancer (Odds Ratio (OR) = 16.80, p < 0.01). Old age and an advanced International Federation of Gynecology and Obstetrics (FIGO) stage were significantly related to Type II ovarian cancer (OR = 0.22/0.02, p < 0.05). An advanced FIGO stage, solid components, and old age were significantly associated with poor survival (Hazard Ratio (HR) = 23.54/3.69/2.46, p < 0.05). Clear cell cancer type had a poorer survival than any other pathological subtypes of ovarian cancer (HR = 13.6, p < 0.01). Conclusions MR imaging features combined with ADC value are helpful in categorizing OEC. ADC values can reflect tumor proliferative ability. A solid mass may predict poor prognosis for OEC patients. Electronic supplementary material The online version of this article (10.1186/s13048-019-0534-0) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level

et al. 2019

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“…20,21 Mukhtar and colleagues 22 reported that MDCT can differentiate benign and malignant ovarian lesions with 95.6% sensitivity, 97.3% specificity, 93.5% NPV, 97.3% PPV, and 96.8% overall diagnostic accuracy. In addition, Liu and colleagues 23 reported that the combination of tumour size, morphology, mural nodule, and enhancement degrees can help differentiate type I (low-grade) and type II (highgrade) epithelial ovarian cancer with 61.36% sensitivity, 87.5% specificity, 55.3% negative predictive value, 90% positive predictive value, and 0.808 AUC (p<0.05); however, the inability to detect small volume extra-ovarian disease on bowel serosa, mesentery, and peritoneum along with the hazards of ionising irradiation are some of the limitations of MDCT. 24,25 DECT can be used in patients with pelvic masses to differentiate malignant OTs from benign OTs as suggested by Benveniste et al from their preliminary single-institution experience.…”

Section: Discussionmentioning

confidence: 99%

Does dual-energy CT differentiate benign and malignant ovarian tumours?

et al. 2020

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“…Ovarian BOT is a type of low-potential epithelial tumor with a relatively good prognosis after treatment. Sometimes, it is di cult to discriminate BOTs from ovarian malignancies solely on imaging information due to some overlapping imaging ndings between the two (22). Our current results showed that the 3D MR-based radiomics signatures derived from sagittal fs-T2WI yielded an ACC of 100% in differentiating ovarian malignancies from BOTs and may help clinicians make a correct diagnosis before surgery.…”

Section: Discussionmentioning

confidence: 64%

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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CT and MRI findings of type I and type II epithelial ovarian cancer

Cited by 25 publications

References 27 publications

Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level

Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level

Does dual-energy CT differentiate benign and malignant ovarian tumours?

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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