Clinicopathologic Features and Risk Factors for Recurrence of Mucinous Borderline Ovarian Tumors: A Retrospective Study With Follow-up of More Than 10 Years

Mucins and mucinous ovarian carcinoma: Development, differential diagnosis, and treatment

Mucinous ovarian carcinoma (MOC) is a rare histological type of epithelial ovarian cancer. It has poor response to conventional platinum-based chemotherapy regimens and PARPi-based maintenance treatment, resulting in short survival and poor prognosis in advanced-disease patients. MOC is characterized by mucus that is mainly composed of mucin in the cystic cavity. Our review discusses in detail the role of mucins in MOC. Mucins are correlated with MOC development. Furthermore, they are valuable in the differential diagnosis of primary and secondary ovarian mucinous tumors. Some types of mucins have been studied in the context of chemoresistance and targeted therapy for ovarian cancer. This review may provide a new direction for the diagnosis and treatment of advanced MOC.

Rapid recurrence of a ruptured mucinous borderline ovarian tumor harboring K-RAS mutation followed by progression into anaplastic carcinoma with TP53 mutation

We describe the case of a young patient with a borderline mucinous ovarian tumor that progressed into ipsilateral ovarian anaplastic carcinoma in only 3 months with metastasis to the contralateral ovary and extensive spread in the pelvic and abdominal regions. The mucinous tumor harbored micro-foci of intraepithelial carcinoma, but no mural nodules, microinvasion, or invasive adenocarcinoma were detected. Notably, a rupture on the ovarian mass and low-grade pseudomyxoma peritonei were present. Next-generation sequencing identified an identical KRAS mutation in the mucinous tumor and anaplastic carcinoma, while the latter had KRAS gene amplification and CDKN2A, MPL and TP53 mutations. These findings indicate the anaplastic carcinoma might have arisen via recurrence, malignant transformation and dedifferentiation of the former low-grade mucinous tumor. We consider that the mass rupture and pseudomyxoma peritonei were high-risk factors for recurrence, while genetic mutations were key drivers of progression. Accordingly, such cases may benefit from active surgical treatment and early chemotherapy.

MRI-Based Multiple Instance Convolutional Neural Network for Increased Accuracy in the Differentiation of Borderline and Malignant Epithelial Ovarian Tumors

BACKGROUND

Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT vs. MEOT) is challenging and can significantly impact surgical management.

PURPOSE

To develop a multiple instance convolutional neural network (MICNN) that can differentiate BEOT from MEOT, and to compare its diagnostic performance with that of radiologists.

STUDY TYPE

Retrospective study of eight clinical centers.

SUBJECTS

Between January 2010 and June 2018, a total of 501 women (mean age, 48.93 ± 14.05 years) with histopathologically confirmed BEOT (N = 165) or MEOT (N = 336) were divided into the training (N = 342) and validation cohorts (N = 159).

FIELD STRENGTH/SEQUENCE

Three axial sequences from 1.5 or 3 T scanner were used: fast spin echo T2-weighted imaging with fat saturation (T2WI FS), echo planar diffusion-weighted imaging, and 2D volumetric interpolated breath-hold examination of contrast-enhanced T1-weighted imaging (CE-T1WI) with FS.

ASSESSMENT

Three monoparametric MICNN models were built based on T2WI FS, apparent diffusion coefficient map, and CE-T1WI. Based on these monoparametric models, we constructed an early multiparametric (EMP) model and a late multiparametric (LMP) model using early and late information fusion methods, respectively. The diagnostic performance of the models was evaluated using the receiver operating characteristic (ROC) curve and compared to the performance of six radiologists with varying levels of experience.

STATISTICAL TESTS

We used DeLong test, chi-square test, Mann-Whitney U-test, and t-test, with significance level of 0.05.

RESULTS

Both EMP and LMP models differentiated BEOT from MEOT, with an area under the ROC curve (AUC) of 0.855 (95% CI, 0.795-0.915) and 0.884 (95% CI, 0.831-0.938), respectively. The AUC of the LMP model was significantly higher than the radiologists' pooled AUC (0.884 vs. 0.797).

DATA CONCLUSION

The developed MICNN models can effectively differentiate BEOT from MEOT and the diagnostic performances (AUCs) were more superior than that of the radiologists' assessments.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Ovarian Seromucinous Borderline Tumors Are Histologically Different from Mucinous Borderline Tumors

AIM

To examine the clinicopathological features of ovarian seromucinous borderline tumors (SMBTs) and compare them with those of mucinous borderline/atypical proliferative mucinous tumors (MB/APMTs).

PATIENTS AND METHODS

Patients with SMBT between 2014 and 2018 and those with MB/APMT between 1988 and 2018 who underwent surgery at our Institution were identified. Pathological review was conducted using the 2014 World Health Organization criteria. Clinical features were compared retrospectively between SMBT and MB/APMT.

RESULTS

In total, 11 (12.9%) patients with SMBT and 74 (87.1%) patients with MB/APMT were included in our study. The diagnosis of six patients with SMBT and 73 patients with MB/APMT was not revised on review. SMBT was diagnosed at a younger age (p=0.04), was of smaller size (p<0.01) and bilateral (p=0.03), coexisted with endometriosis (p<0.01), and more frequently recurred than MB/APMT (p=0.04).

CONCLUSION

SMBT might be more aggressive than MB/APMT.

Pelvic seromucinous borderline tumor 26 years after ovarian seromucinous borderline tumor managed with post-treatment estrogen replacement therapy

•A 56-year-old woman developed two seromucinous borderline tumors 26 years apart.•The second cyst was diagnosed as a seromucinous borderline tumor associated with pelvic endometriosis.•The first ovarian cancer was re-diagnosed as an ovarian seromucinous borderline tumor after a pathological slide review.•Seromucinous borderline tumors can re-occur several years after post-treatment estrogen replacement therapy.•Post-treatment estrogen replacement therapy for seromucinous borderline tumors should be provided carefully.

Staging procedures fail to benefit women with borderline ovarian tumours who want to preserve fertility: a retrospective analysis of 448 cases

Background

To evaluate the effect of clinicopathologic factors on the prognosis and fertility outcomes of BOT patients.

Methods

We performed a retrospective analysis of BOT patients who underwent surgical procedures in West China Second University Hospital from 2008 to 2015. The DFS outcomes, potential prognostic factors and fertility outcomes were evaluated.

Results

Four hundred forty-eight patients were included; 52 recurrences were observed. Ninety-two patients undergoing FSS achieved pregnancy. No significant differences in fertility outcomes were found between the staging and unstaged surgery groups. Staging surgery was not an independent prognostic factor for DFS. Laparoscopy resulted in better prognosis than laparotomy in patients with stage I tumours and a desire for fertility preservation.

Conclusion

Patients with BOT fail to benefit from surgical staging. Laparoscopy is recommended for patients with stage I disease who desire to preserve fertility. Physicians should pay more attention to risk of recurrence in patients who want to preserve fertility.

MRI-Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study

BACKGROUND

Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results.

PURPOSE

To develop and validate an objective MRI-based machine-learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation.

STUDY TYPE

Retrospective study of eight clinical centers.

POPULATION

In all, 501 women with histopathologically-confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159).

FIELD STRENGTH/SEQUENCE

Preoperative MRI within 2 weeks of surgery. Single- and multiparameter (MP) machine-learning assessment models were built utilizing the following four MRI sequences: T₂ -weighted imaging (T₂ WI), fat saturation (FS), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced (CE)-T₁ WI.

ASSESSMENT

Diagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early-stage MEOT was made. Six radiologists of varying experience also interpreted the MR images.

STATISTICAL TESTS

Mann-Whitney U-test: significance of the clinical characteristics; chi-square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC).

RESULTS

The MP-ST model performed better than the MP-WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early-stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679-0.924) and external (mean AUC = 0.797; range, 0.744-0.867) validation cohorts.

DATA CONCLUSION

Performance of the MRI-based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women.

LEVEL OF EVIDENCE

Level 4.

TECHNICAL EFFICACY

Stage 2. J. Magn. Reson. Imaging 2020;52:897-904.