Differentiation of borderline tumors from type I ovarian epithelial cancers on CT and MR imaging

Yang, Sihua; Tang, Huan; Xiao, Feipeng; Zhu, Jinxia; Ting, Hua; Tang, Guangyu

doi:10.1007/s00261-020-02467-w

Cited by 10 publications

(7 citation statements)

References 26 publications

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“…The absence of typical morphological features in BOTs, on ultrasound makes it difficult to distinguish them from benign or malignant ovarian tumors [ 18 ]. Similarly, the value of CT and MRI features in differentiating BOTs from malignant tumors appear limited because of the tumour's solid components and thickness oof their septations [ 19 ]. Our patient's preoperative evaluation involved both abdomeno-pelvic ultrasound scan and MRI.…”

Section: Discussionmentioning

confidence: 99%

A giant 25 litre volume ovarian cystic mucinous borderline ovarian tumour with intraepithelial carcinoma in a 24-year-old nulliparous woman: Case report

Onuzo,

Gordon,

Amoatwo

et al. 2024

International Journal of Surgery Case Reports

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Section: Discussionmentioning

confidence: 99%

A giant 25 litre volume ovarian cystic mucinous borderline ovarian tumour with intraepithelial carcinoma in a 24-year-old nulliparous woman: Case report

Onuzo,

Gordon,

Amoatwo

et al. 2024

International Journal of Surgery Case Reports

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“…In routine clinical practice, MDCT is the most commonly used imaging method for preoperative assessment and postoperative surveillance of patients with ovarian tumors. Previous studies have indicated that MDCT and MRI are promising imaging tools for differentiating BOTs from MOTs ( 23 , 24 ). Due to the focus on variables such as tumor shape, tumor volume, and tumor vascular changes in early studies ( 6 , 17 ), it is difficult to distinguish BOTs from MOTs depending on imaging information alone, despite the medical imaging methods used to distinguish them, which could result from some morphological imaging findings overlapping between BOTs and MOTs, such as irregularly thickened walls or septa, predominantly solid masses, and vascular abnormalities.…”

Section: Discussionmentioning

confidence: 99%

Radiomics nomogram for preoperative differentiation of early-stage serous borderline ovarian tumors and serous malignant ovarian tumors

Yu,

Zou,

Wang

et al. 2024

Front. Oncol.

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ObjectivesThis study aimed to construct a radiomics nomogram and validate its performance in the preoperative differentiation between early-stage (I and II) serous borderline ovarian tumors (SBOTs) and serous malignant ovarian tumors (SMOTs).MethodsData were collected from 80 patients with early-stage SBOTs and 102 with early-stage SMOTs (training set: n = 127; validation set: n = 55). Univariate and multivariate analyses were performed to identify the independent clinicoradiological factors. A radiomics signature model was constructed using radiomics features extracted from multidetector computed tomography images of the venous phase, in which the least absolute shrinkage and selection operator regression was employed to lessen the dimensionality of the data and choose the radiomics features. A nomogram model was established by combining independent clinicoradiological factors with the radiomics signature. The performance of nomogram calibration, discrimination, and clinical usefulness was evaluated using training and validation sets.ResultsIn terms of clinicoradiological characteristics, age (p = 0.001), the diameter of the solid component (p = 0.009), and human epididymis protein 4 level (p < 0.001) were identified as the independent risk factors of SMOT, for which the area under the curves (AUCs) were calculated to be 0.850 and 0.836 in the training and validation sets, respectively. Nine features were finally selected to construct the radiomics signature model, which exhibited AUCs of 0.879 and 0.826 for the training and validation sets, respectively. The nomogram model demonstrated considerable calibration and discrimination with AUCs of 0.940 and 0.909 for the training and validation sets, respectively. The nomogram model displayed more prominent clinical usefulness than the clinicoradiological and radiomics signature models according to the decision curve analysis.ConclusionsThe nomogram model can be employed as an individualized preoperative non-invasive tool for differentiating early-stage SBOTs from SMOTs.

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“…This study discovered that patients with BOTs had a lower proportion of elevated preoperative serum CA125 concentrations than EOC patients. However, Yang et al [ 4 ] found no apparent difference in preoperative serum CA125 Levels between BOTs and EOC. The probable reason for the difference is that their study population was BOT patients and stage I epithelial ovarian cancer patients, and the serum CA125 concentration was reported to have only 50% sensitivity for women with stage I ovarian cancer or with BOTs[ 23 ].…”

Section: Discussionmentioning

confidence: 99%

“…The major subtypes of BOTs are mucinous and serous following histological examination, accounting for approximately 30% and 67% of BOTs, respectively[ 2 , 3 ]. BOTs are characterized by cytological evidence of malignancy in which atypical epithelial cell proliferation and nuclear atypia are observed but with no obvious invasion to the stroma, which differs from epithelial ovarian cancer (EOC)[ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Develop a nomogram to predict overall survival of patients with borderline ovarian tumors

Gong¹,

Zhang²

2022

WJCC

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BACKGROUND The prognosis of borderline ovarian tumors (BOTs) has been the concern of clinicians and patients. It is urgent to develop a model to predict the survival of patients with BOTs. AIM To construct a nomogram to predict the likelihood of overall survival (OS) in patients with BOTs. METHODS A total of 192 patients with histologically verified BOTs and 374 patients with epithelial ovarian cancer (EOC) were retrospectively investigated for clinical characteristics and survival outcomes. A 1:1 propensity score matching (PSM) analysis was performed to eliminate selection bias. Survival was analyzed by using the log-rank test and the restricted mean survival time (RMST). Next, univariate and multivariate Cox regression analyses were used to identify meaningful independent prognostic factors. In addition, a nomogram model was developed to predict the 1-, 3-, and 5-year overall survival of patients with BOTs. The predictive performance of the model was assessed by using the concordance index (C-index), calibration curves, and decision curve analysis (DCA). RESULTS For clinical data, there was no significant difference in body mass index, preoperative CA199 concentration, or tumor localization between the BOTs group and EOC group. Women with BOTs were significantly younger than those with EOC. There was a significant difference in menopausal status, parity, preoperative serum CA125 concentration, Federation International of gynecology and obstetrics (FIGO) stage, and whether patients accepted postoperative adjuvant therapy between the BOT and EOC group. After PSM, patients with BOTs had better overall survival than patients with EOC ( P value = 0.0067); more importantly, the 5-year RMST of BOTs was longer than that of EOC ( P value = 0.0002, 95%CI -1.137 to -0.263). Multivariate Cox regression analysis showed that diagnosed age and surgical type were independent risk factors for BOT patient OS ( P value < 0.05). A nomogram was developed based on diagnosed age, preoperative serum CA125 and CA199 Levels, surgical type, FIGO stage, and tumor size. Moreover, the c-index (0.959, 95% confidence interval 0.8708–1.0472), calibration plot of 1-, 3-, and 5-year OS, and decision curve analysis indicated the accurate predictive ability of this model. CONCLUSION Patients with BOTs had a better prognosis than patients with EOC. The nomogram we constructed might be helpful for clinicians in personalized treatment planning and patient counseling.

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Differentiation of borderline tumors from type I ovarian epithelial cancers on CT and MR imaging

Cited by 10 publications

References 26 publications

A giant 25 litre volume ovarian cystic mucinous borderline ovarian tumour with intraepithelial carcinoma in a 24-year-old nulliparous woman: Case report

A giant 25 litre volume ovarian cystic mucinous borderline ovarian tumour with intraepithelial carcinoma in a 24-year-old nulliparous woman: Case report

Radiomics nomogram for preoperative differentiation of early-stage serous borderline ovarian tumors and serous malignant ovarian tumors

Develop a nomogram to predict overall survival of patients with borderline ovarian tumors

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