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

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

INTRODUCTION AND IMPORTANCE

Giant ovarian cysts are rare and usually pose significant diagnostic challenges especially in adolescents and young adults. There is limited report of such cases reported in existing literature with hardly any cases published from the Sub-Sharan African region.

CASE PRESENTATION

We present the case of a 24-year-old young woman who reported to our gynaecology clinic on the 23rd of January 2023 with a year's history of a progressively increasing abdominopelvic mass. She was successfully managed surgically and made smooth recovery.

CLINICAL DISCUSSION

Based on the history and examination findings, confirmed the diagnosis clinically with abdomino-pelvic ultrasound scan, removed the tumour surgically and undertook histopathological studies to confirm a benign disease. To the best of our knowledge, our successful management of this patient is the first case of such a huge borderline ovarian tumour reported in Ghana and the Sub-Saharan African region to inform clinicians on safe surgical management in our context.

CONCLUSION

Our successful management of this giant mucinous BOT reiterates the fact that in the absence of precise prognostic marker of malignancy, clinicians should always balance the oncologic safety of the patient against less radical treatment modality.

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

Objectives

This 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).

Methods

Data 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.

Results

In 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.

Conclusions

The nomogram model can be employed as an individualized preoperative non-invasive tool for differentiating early-stage SBOTs from SMOTs.

Role of magnetic resonance imaging to differentiate between borderline and malignant serous epithelial ovarian tumors

PURPOSE

We aimed to differentiate serous borderline ovarian tumors (SBOT) from serous epithelial ovarian carcinomas (SEOC) using morphological and functional MRI findings, to improve the patient management.

METHOD

We retrospectively investigated 24 ovarian lesions diagnosed with SBOT and 64 ovarian lesions diagnosed with SEOC. Additional to the demographic and morphological findings T2W signal intensity ratio, mean apparent diffusion coefficient (ADCmean) and total apparent diffusion coefficient (ADCtotal) values were analyzed and compared between two groups.

RESULTS

Bilaterality, pelvic free fluid presence, serum CA-125 level (U/mL), presence of pelvic peritoneal implant were in favor of SEOC. Lower maximum size of solid component and solid size to maximum size ratio, dominantly cystic and solid-cystic appearance, exophytic growth pattern, presence of papiller projection and papillary architecture and internal branching pattern, higher T2W signal intensity ratio, ADCmean and ADCtotal values were in favor of SBOT.

CONCLUSION

Our study revealed that morphological and functional imaging findings were valuable in differentiating BSOT from SEOC.

Computed Tomography Indicators for Differentiating Stage 1 Borderline Ovarian Tumors from Stage I Malignant Epithelial Ovarian Tumors

Preoperative diagnosis of borderline ovarian tumors (BOTs) is of increasing concern. This study aimed to determine computed tomography (CT) features in differentiating stage 1 BOTs from stage I malignant epithelial ovarian tumors (MEOTs). A total of 170 ovarian masses (97 BOTs and 73 MEOTs) from 141 consecutive patients who underwent preoperative CT imaging were retrospectively analyzed. Two readers independently and retrospectively reviewed quantitative and qualitative CT features. Multivariate logistic analysis demonstrated that a larger tumor size (p = 0.0284 for reader 1, p = 0.0391 for reader 2) and a smaller solid component (p = 0.0007 for reader 1, p = 0.0003 for reader 2) were significantly associated with BOTs compared with MEOTs. In the subanalysis of cases with a solid component, smaller (p = 0.0092 for reader 1, p = 0.0014 for reader 2) and ill-defined (p = 0.0016 for reader 1, p = 0.0414 for reader 2) solid component was significantly associated with BOTs compared with MEOTs. Tumor size and the size and margin of the solid component were useful for differentiating stage 1 BOTs from stage 1 MEOTs on CT images.

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

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.

An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer

This retrospective single-center study included patients diagnosed with epithelial ovarian cancer (EOC) using preoperative pelvic magnetic resonance imaging (MRI). The apparent diffusion coefficient (ADC) of the axial MRI maps that included the largest solid portion of the ovarian mass was analysed. The mean ADC values (ADC_mean) were derived from the regions of interest (ROIs) of each largest solid portion. Logistic regression and three types of machine learning (ML) applications were used to analyse the ADCs and clinical factors. Of the 200 patients, 103 had high-grade serous ovarian cancer (HGSOC), and 97 had non-HGSOC (endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, and low-grade serous ovarian cancer). The median ADC_mean of patients with HGSOC was significantly lower than that of patients without HGSOCs. Low ADC_mean and CA 19-9 levels were independent predictors for HGSOC over non-HGSOC. Compared to stage I disease, stage III disease was associated with HGSOC. Gradient boosting machine and extreme gradient boosting machine showed the highest accuracy in distinguishing between the histological findings of HGSOC versus non-HGSOC and between the five histological types of EOC. In conclusion, ADC_mean, disease stage at diagnosis, and CA 19-9 level were significant factors for differentiating between EOC histological types.

Comparison of pathological, radiological, and prognostic features between cellular schwannoma and non-cellular schwannoma

PURPOSE

To investigate the differences of pathological, radiological, and prognostic features between cellular schwannoma (CS) and non-cellular schwannoma (NCS).

METHODS

CT and MRI images of 24 patients with CSs and 30 patients with NCSs were reviewed retrospectively. Clinico-pathological characteristics of CSs and NCSs and tumor radiological features including location, shape, size, border, cystic-solid components, hemorrhage, calcification, bone remodeling, pattern of CT/MRI precontrast scan, degree of enhancement, target sign, and tumor vessels were recorded. Statistical analyses were performed with Chi square or Fisher's exact test, independent sample t test, and logistic regression analysis to compare the differences between CSs and NCSs.

RESULTS

Four CSs showed mitotic activity, which was not found in the NCS group (P = 0.034). The CS group showed higher MIB-1 index than that in the NCS group (P = 0.002). Two patients with CS presented with tumor recurrence. Compared to NCSs, CSs were often located in spinal area (P = 0.028) and irregular (P = 0.013) with larger size (P = 0.005). Target sign, a common finding in NCSs (7/22, 31.8 %), was not seen in CSs (P = 0.014). The tumor vessels were only seen in CS group (4/22, 18.2 %; P = 0.027). Regression analysis revealed that location (P = 0.048) and size (P = 0.012) were independent indicators in differentiating CSs from NCSs.

CONCLUSIONS

CS is a rare subtype of schwannoma with some significant radiological features including a predilection for the spinal area, irregular shape, large tumor size, absent target sign, tumor vessels, and potential risk of recurrence. Location and size of the schwannomas were the most useful indicators in differentiating CSs from NCSs.

The Diagnosis, Treatment, Prognosis and Molecular Pathology of Borderline Ovarian Tumors: Current Status and Perspectives

Borderline ovarian tumors (BOTs) are a type of low malignant potential tumor that is typically associated with better outcomes than ovarian cancer. Indeed, its 10-year survival rate is as high as 95%. However, there is a small subset of patients who experience relapse and eventually die. It has been shown that the prognosis of BOTs was based on pathological diagnosis, the age at diagnosis, pre-operative carbohydrate antigen 125 level, invasive implants, and micropapillary patterns. Now the molecular-targeted therapy and molecular-genetic diagnosis have developed into a form of precision medicine. Recent studies on extensive molecular characterizations and molecular pathological mechanisms of BOTs have helped us understand the genomic landscapes of BOTs, and therefore BOTs could be reclassified into biologically and clinically more accurate and effective subtypes. The purpose of this review is to summarize current status for the diagnosis and treatment of BOTs and to describe the research progress on molecular pathologies, with a goal of providing a theoretical perspective for the diagnosis and treatment of BOTs.