Diffusion-weighted MR imaging for differentiating borderline from malignant epithelial tumours of the ovary: pathological correlation

Time-dependent diffusion MRI-based microstructural mapping for differentiating high-grade serous ovarian cancer from serous borderline ovarian tumor

PURPOSE

To investigate the value of microstructural characteristics derived from time-dependent diffusion MRI in distinguishing high-grade serous ovarian cancer (HGSOC) from serous borderline ovarian tumor (SBOT) and the associations of immunohistochemical markers with microstructural features.

METHODS

Totally 34 HGSOC and 12 SBOT cases who received preoperative pelvic MRI were retrospectively included in this study. Two radiologists delineated the tumors to obtain the regions of interest (ROIs). Time-dependent diffusion MRI signals were fitted by the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model, to extract microstructural parameters, including fraction of the intracellular component (fin), cell diameter (d), cellularity and extracellular diffusivity (Dex). Apparent diffusion coefficient (ADC) values were obtained from standard diffusion-weighted imaging (DWI). The parameters of HGSOCs and SBOTs were compared, and the diagnostic performance was evaluated. The associations of microstructural indexes with immunopathological parameters were assessed, including Ki-67, P53, Pax-8, ER and PR.

RESULTS

In this study, fin, cellularity, Dex and ADC had good diagnostic performance levels in differentiating HGSOC from SBOT, with AUCs of 0.936, 0.909, 0.902 and 0.914, respectively. There were no significant differences in diagnostic performance among these parameters. Spearman analysis revealed in the HGSOC group, cellularity had a significant positive correlation with P53 expression (P = 0.028, r = 0.389) and Dex had a significant positive correlation with Pax-8 expression (P = 0.018, r = 0.415). ICC showed excellent agreement for all parameters.

CONCLUSION

Time-dependent diffusion MRI had value in evaluating the microstructures of HGSOC and SBOT and could discriminate between these tumors.

Multimodal diagnostic strategies and precision medicine in mucinous ovarian carcinoma: a comprehensive approach

Mucinous ovarian carcinoma (MOC) represents a distinct entity within ovarian malignancies, characterized by diagnostic challenges due to its rarity and the potential overlap with other tumor types. The determination of tumor origin is important for precise postsurgical treatment. This article highlights the accurate diagnosis and management of MOC, including the use of imaging modalities, serological tumor markers, immunohistochemistry, and genomic analyses. Transabdominal and transvaginal ultrasonography, complemented by MRI and CT, plays a pivotal role in differentiating MOC from other mucinous tumors and in surgical planning, particularly for fertility preservation. Serological markers like CA19-9, CA-125, and CEA, though not definitive, provide valuable preoperative insights. Immunohistochemistry aids in distinguishing primary MOC from metastatic mucinous carcinomas, while genomic profiling offers the potential for precision medicine through the identification of specific molecular signatures and treatment susceptibilities. Despite advancements in diagnostic techniques, no single method conclusively differentiates between primary and metastatic tumors intraoperatively. The paper reviews the origins, diagnosis, and differential diagnosis of primary mucinous ovarian carcinoma highlights the need for a multimodal diagnostic approach and advocates for the inclusion of MOC patients in clinical trials for personalized therapies, recognizing the heterogeneity of the disease at the molecular level.

Estimating pathological prognostic factors in epithelial ovarian cancers using apparent diffusion coefficients of functional tumor volume

PURPOSE

To assess the utility of apparent diffusion coefficients (ADCs) of whole tumor volume (WTV) and functional tumor volume (FTV) in determining the pathologicalprognostic factors in epithelial ovarian cancers (EOCs).

METHODS

A total of 155 consecutive patients who were diagnosed with EOC between January 2017 and August 2022 and underwent both conventional magnetic resonance imaging and diffusion-weighted imaging were assessed in this study. The maximum, minimum, and mean ADC values of the whole tumor (ADCwmax, ADCwmin, and ADCwmean, respectively) and functional tumor (ADCfmax, ADCfmin, and ADCfmean, respectively) as well as the WTV and FTV were derived from the ADC maps. The univariate and multivariate logistic regression analyses and receiver operating characteristic curve (ROC) analysis were used to assess the correlation between these ADC values and the pathological prognostic factors, namely subtypes, lymph node metastasis (LNM), Ki-67 index, and p53 expression.

RESULTS

The ADCfmean value was significantly lower in type II EOC, LNM-positive, and high-Ki-67 index groups compared to the type I EOC, LNM-negative, and low-Ki-67 index groups (p ≤ 0.001). Similarly, the ADCwmean and ADCfmean values were lower in the mutant-p53 group compared to the wild-type-p53 group (p ≤ 0.001). Additionally, the ADCfmean showed the highest area under the ROC curve (AUC) for evaluating type II EOC (0.725), LNM-positive (0.782), and high-Ki-67 index (0.688) samples among the given ROC curves, while both ADCwmean and ADCfmean showed high AUCs for assessing p53 expression (0.694 and 0.678, respectively).

CONCLUSION

The FTV-derived ADC values, especially ADCfmean, can be used to assess preoperative prognostic factors in EOCs.

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.

Comprehensive Review of Serous Tumors of Tubo-Ovarian Origin: Clinical Behavior, Pathological Correlation, Current Molecular Updates, and Imaging Manifestations

Ovarian cancer is the eighth most common women's cancer worldwide, with the highest mortality rate of any gynecologic malignancy. On a global scale, the World Health Organization (WHO) reports that ovarian cancer has approximately 225,000 new cases every year with approximately 145,000 deaths. According to the National Institute of Health, Surveillance Epidemiology and End Results program (SEER) database, 5-year survival for women with ovarian cancer in the United States is 49.1%. High-grade serous ovarian carcinoma typically presents at an advanced stage and accounts for the majority of these cancer deaths. Given their prevalence and the lack of a reliable method for screening, early and reliable diagnosis of serous cancers is of paramount importance. Early differentiation of borderline, low and high-grade lesions can assist in surgical planning and support challenging intraoperative diagnoses. The objective of this article is to provide a review of the pathogenesis, diagnosis, and treatment of serous ovarian tumors, with a specific focus on the imaging characteristics that help to preoperatively differentiate borderline, low-grade, and high-grade serous ovarian lesions.

Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging

To establish a deep learning (DL) model in differentiating borderline ovarian tumor (BOT) from epithelial ovarian cancer (EOC) on conventional MR imaging. We retrospectively enrolled 201 patients of 102 pathologically proven BOTs and 99 EOCs at OB/GYN hospital Fudan University, between January 2015 and December 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MR images were used for lesion area determination. We trained a U-net++ model with deep supervision to segment the lesion area on MR images. Then, the segmented regions were fed into a classification model based on DL network to categorize ovarian masses automatically. For ovarian lesion segmentation, the mean dice similarity coefficient (DSC) of the trained U-net++ model in the testing dataset achieved 0.73 [Formula: see text] 0.25, 0.76 [Formula: see text] 0.18, and 0.60 [Formula: see text] 0.24 in the sagittal T2WI, coronal T2WI, and axial T1WI images, respectively. The DL model by combined T2WI computerized network could differentiate BOT from EOC with a significantly higher AUC of 0.87, an accuracy of 83.7%, a sensitivity of 75.0% and a specificity of 87.5%. In comparison, the AUC yielded by radiologist was only 0.75, with an accuracy of 75.5%, a sensitivity of 96.0% and specificity of 54.2% (P < 0.001).The trained DL network model derived from routine MR imaging could help to distinguish BOT from EOC with a high accuracy, which was superior to radiologists' assessment.

MRI of Borderline Epithelial Ovarian Tumors: Pathologic Correlation and Diagnostic Challenges

Borderline epithelial ovarian tumors are a distinct pathologic entity characterized by increased epithelial proliferation and nuclear atypia, but without frank stromal invasion. Borderline tumor (BT) is now considered to represent an intermediate phase in the stepwise progression from benign to malignant ovarian epithelial tumor. Since BTs commonly manifest at early stages in women of reproductive age and are associated with a good prognosis, making the correct diagnosis is important in determining whether a patient is a candidate for fertility-sparing surgery. There are six histologic BT subtypes (serous, mucinous, seromucinous, endometrioid, clear cell, and Brenner), and each has different MRI features, reflecting their unique histologic architectures. Radiologists should be aware of the MRI features that can suggest BTs. These features include a hyperintense papillary architecture with hypointense internal branching, which can be observed with serous and seromucinous BTs on T2-weighted images; aggregates of microcysts that have hypointensity on T2-weighted images and reticular enhancement on contrast-enhanced T2-weighted images, which can be seen with mucinous BTs; and moderately high signal intensity on diffusion-weighted images along with relatively high apparent diffusion coefficient values, which can be observed regardless of the histologic subtype. Nevertheless, because the imaging features of BTs overlap with those of many benign lesions (eg, cystadenoma and cystadenofibroma, decidualized endometriosis, and polypoid endometriosis) and malignant tumors (ovarian cancers and metastases), histologic confirmation is required for the final diagnosis. Special emphasis is placed on the MRI features of BTs, pathologic correlation, and the challenges related to diagnosis. ^©RSNA, 2022.

Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors

BACKGROUND

Ovarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery.

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.

CONCLUSIONS

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 ACC.

Quantitative analysis of the MRI features in the differentiation of benign, borderline, and malignant epithelial ovarian tumors

Objective

This study aims to investigate the value of the quantitative indicators of MRI in the differential diagnoses of benign, borderline, and malignant epithelial ovarian tumors (EOTs).

Materials and methods

The study population comprised 477 women with 513 masses who underwent MRI and operation, including benign EOTs (BeEOTs), borderline EOTs (BEOTs), and malignant EOTs (MEOTs). The clinical information and MRI findings of the three groups were compared. Then, multivariate logistic regression analysis was performed to find the independent diagnostic factors. The receiver operating characteristic (ROC) curves were also used to evaluate the diagnostic performance of the quantitative indicators of MRI and clinical information in differentiating BeEOTs from BEOTs or differentiating BEOTs from MEOTs.

Results

The MEOTs likely involved postmenopausal women and showed higher CA-125, HE4 levels, ROMA indices, peritoneal carcinomatosis and bilateral involvement than BeEOTs and BEOTs. Compared with BEOTs, BeEOTs and MEOTs appeared to be more frequently oligocystic (P < 0.001). BeEOTs were more likely to show mild enhancement (P < 0.001) and less ascites (P = 0.003) than BEOTs and MEOTs. In the quantitative indicators of MRI, BeEOTs usually showed thin-walled cysts and no solid component. BEOTs displayed irregular thickened wall and less solid portion. MEOTs were more frequently characterized as solid or predominantly solid mass (P < 0.001) than BeEOTs and BEOTs. The multivariate logistic regression analysis showed that volume of the solid portion (P = 0.006), maximum diameter of the solid portion (P = 0.038), enhancement degrees (P < 0.001), and peritoneal carcinomatosis (P = 0.011) were significant indicators for the differential diagnosis of the three groups. The area under the curves (AUCs) of above indicators and combination of four image features except peritoneal carcinomatosis for the differential diagnosis of BeEOTs and BEOTs, BEOTs and MEOTs ranged from 0.74 to 0.85, 0.58 to 0.79, respectively.

Conclusion

In this study, the characteristics of MRI can provide objective quantitative indicators for the accurate imaging diagnosis of three categories of EOTs and are helpful for clinical decision-making. Among these MRI characteristics, the volume, diameter, and enhancement degrees of the solid portion showed good diagnostic performance.

MRI characteristics for differentiating mucinous borderline ovarian tumours from mucinous ovarian cancers

AIM

To investigate the magnetic resonance imaging (MRI) characteristics that could differentiate mucinous borderline ovarian tumours (MBOTs) from mucinous ovarian cancers (MOCs).

MATERIALS AND METHODS

MRI data from 75 patients with MBOTs and 38 patients with MOCs were reviewed retrospectively. The clinicopathological and MRI features, including age, bilaterality, maximum diameter (MD), shape, margin, configuration, cystic-solid interface, papillae, MD of the cyst walls and septa, MD of the solid components, number of cysts, honeycomb loculi, signal of the cystic and solid components, apparent diffusion coefficient (ADC) value and enhancement ratio of the solid components, peritoneal implants and ascites, were compared using univariable analysis and multivariable logistic regression analysis.

RESULTS

There were 76 MBOTs and 39 MOCs, and median patient age was 41 years (range 16-77 years) and 51 years (range 15-90 years), respectively (p=0.004). There were significant differences between MBOTs and MOCs regarding the presence of papillae (p=0.013), MD of the solid components (p=0.001), enhancement ratio of the solid components (p=0.003), ADC value (p<0.001), and ascites (p<0.001). The optimal cut-off ADC value was 1.16 × 10^-3 mm²/s, with a sensitivity of 87.1%, a specificity of 83.3%, and an area under the curve (AUC) of 0.917.

CONCLUSION

Compared with MOCs, MBOTs had fewer papillae or solid components, lower enhancement ratio, higher ADC values, and were less likely to have moderate or massive ascites.

Multiple instance convolutional neural network with modality-based attention and contextual multi-instance learning pooling layer for effective differentiation between borderline and malignant epithelial ovarian tumors

Malignant epithelial ovarian tumors (MEOTs) are the most lethal gynecologic malignancies, accounting for 90% of ovarian cancer cases. By contrast, borderline epithelial ovarian tumors (BEOTs) have low malignant potential and are generally associated with a good prognosis. Accurate preoperative differentiation between BEOTs and MEOTs is crucial for determining the appropriate surgical strategies and improving the postoperative quality of life. Multimodal magnetic resonance imaging (MRI) is an essential diagnostic tool. Although state-of-the-art artificial intelligence technologies such as convolutional neural networks can be used for automated diagnoses, their application have been limited owing to their high demand for graphics processing unit memory and hardware resources when dealing with large 3D volumetric data. In this study, we used multimodal MRI with a multiple instance learning (MIL) method to differentiate between BEOT and MEOT. We proposed the use of MAC-Net, a multiple instance convolutional neural network (MICNN) with modality-based attention (MA) and contextual MIL pooling layer (C-MPL). The MA module can learn from the decision-making patterns of clinicians to automatically perceive the importance of different MRI modalities and achieve multimodal MRI feature fusion based on their importance. The C-MPL module uses strong prior knowledge of tumor distribution as an important reference and assesses contextual information between adjacent images, thus achieving a more accurate prediction. The performance of MAC-Net is superior, with an area under the receiver operating characteristic curve of 0.878, surpassing that of several known MICNN approaches. Therefore, it can be used to assist clinical differentiation between BEOTs and MEOTs.

Clues to the diagnosis of borderline ovarian tumours: An imaging guide

Borderline Ovarian Tumours (BOTs) are an interesting subset of epithelial neoplasms defined histologically by atypical epithelial proliferation without stromal invasion. These tumours typically affect young women in the reproductive age group and have a good prognosis. Although ultrasonography is the primary screening imaging technique in the evaluation of any suspected adnexal mass, grey-scale and colour Doppler have limited value in characterizing BOTs. Thus, a pelvic magnetic resonance imaging (MRI) is recommended for further characterization on account of its multiplanar capabilities, excellent soft-tissue contrast and high spatial resolution. BOTs histological subtypes display specific features on MRI that are useful in differential diagnosis. However, the final diagnosis and staging of BOTs require pathologic evaluation after surgical excision. Therefore, the purpose of this review is to describe, illustrate and compare the imaging characteristics of the different subtypes of BOTs - serous, mucinous and seromucinous - focusing on MRI, as well as to correlate with pathology findings considering the recent 2020 World Health Organization (WHO) classification, in order to improve the accuracy of preoperative diagnosis and facilitate optimal patient management.

Serous borderline ovarian tumours: an extensive review on MR imaging features

Serous borderline ovarian tumours (SBOTs) are an intermediate group of neoplasms, which have features between benign and malignant ovarian tumours and for which, fertility-sparing surgery can be offered. MRI in imaging of SBOTs is, therefore, crucial in raising the possibility of the diagnosis, in order to present the patient with the most appropriate treatment options. There are characteristic MRI features that SBOTs demonstrate. In addition, recent advanced techniques, and further classification into subtypes within the borderline group have been developed. The aim of this article is to review the MRI features of SBOT and provide the reporter with an awareness of the imaging tips and tricks in the differential diagnosis of SBOT.

Are CT and MRI useful tools to distinguish between micropapillary type and typical type of ovarian serous borderline tumors?

PURPOSE

To investigate the computed tomography (CT) and magnetic resonance imaging (MRI) characteristics of ovarian serous borderline tumors (SBTs), and evaluate whether CT and MRI can be used to distinguish micropapillary from typical subtypes.

MATERIALS AND METHODS

We retrospectively reviewed the clinical features and CT and MR imaging findings of 47 patients with SBTs encountered at our institute from September 2013 to December 2019. 30 patients with 58 histologically proven typical SBT and 17 patients with 26 micropapillary SBT were reviewed. Preoperative CT and MR images were evaluated, by two observers in consensus for the laterality, maximum diameter (MD), morphology patterns, internal architecture, attenuation or signal intensity, ADC value, enhancement patterns of solid portions (SP), and extra-ovarian imaging features.

RESULTS

The median age were similar between typical SBT and SBT-MP (32.5 years, 36 years, respectively, P>0.05). Morphology patterns between two subtypes were significantly different on CT and MR images (P < 0.001). Irregular solid tumor (21/37, 56.76%) was the major morphology pattern of typical SBT tumor, while unilocular cyst with mural nodules (14/20, 70%) was the major morphology pattern of SBT-MP on CT images. Similarly, papillary architecture with internal branching (PA&IB) (17/21, 80.95%) was the major morphology pattern of typical SBT tumor, while unilocular cyst with mural nodules (4/6, 66.67%) was the major pattern of SBT-MP on MR images. PA&IB all showed slightly hyperintense papillary architecture with hypointense internal branching on T2-weighted MRI. More calcifications were found in typical SBT (24/37, 64.86%) than SBT-MP mass lesion (6/20, 30%) (P < 0.05). Hemorrhage was less frequently visible in (20/37, 54.05%) typical SBT lessons than SBT-MP mass lesion (18/20, 90%) (P < 0.05). The ovarian preservation is more seen in typical SBT (38/58, 65.52%) than SBT-MP (12/28, 42.86%) in our series (P < 0.05). Mean ADC value of solid portions (papillary architecture and mural nodules) was 1.68 (range from 1.44 to 1.85) × 10^-3 mm²/s for typical SBT and 1.62 (range from 1.45 to 1.7) × 10^-3 mm²/s for that of SBT-MP. The solid components of the two SBT subtypes showed wash-in appearance enhancements after contrast injection both in CT and MR images except 2 of SBT-MP with no enhancement as complete focal hemorrhage on MR images.

CONCLUSION

Morphology and internal architecture are two major imaging features that can help to distinguish between SBT-MP and typical SBT.

Prediction of Platinum-based Chemotherapy Response in Advanced High-grade Serous Ovarian Cancer: ADC Histogram Analysis of Primary Tumors

RATIONALE AND OBJECTIVES

To investigate the feasibility of apparent diffusion coefficient (ADC) histogram analysis of primary advanced high-grade serous ovarian cancer (HGSOC) to predict patient response to platinum-based chemotherapy.

MATERIALS AND METHODS

A total of 70 patients with 102 advanced stage HGSOCs (International Federation of Gynecology and Obstetrics (FIGO) stages III-IV) who received standard treatment of primary debulking surgery followed by the first line of platinum-based chemotherapy were retrospectively enrolled. Patients were grouped as platinum-resistant and platinum-sensitive according to whether relapse occurred within 6 months. Clinical characteristics, including age, pretherapy CA125 level, International Federation of Gynecology and Obstetrics stage, residual tumor, and histogram parameters derived from whole tumor and solid component such as ADC_mean; 10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th percentiles; skewness and kurtosis, were compared between platinum-resistant and platinum-sensitive groups.

RESULTS

No significantly different clinical characteristics were observed between platinum-sensitive and platinum-resistant patients. There were no significant differences in any whole-tumor histogram-derived parameters between the two groups. Significantly higher ADC_mean and percentiles and significantly lower skewness and kurtosis from the solid-component histogram parameters were observed in the platinum-sensitive group when compared with the platinum-resistant group. ADC_mean, skewness and kurtosis showed moderate prediction performances, with areas under the curve of 0.667, 0.733 and 0.616, respectively. Skewness was an independent risk factor for platinum resistance.

CONCLUSION

Pretreatment ADC histogram analysis of primary tumors has the potential to allow prediction of response to platinum-based chemotherapy in patients with advanced HGSOC.

MDCT-Based Radiomics Features for the Differentiation of Serous Borderline Ovarian Tumors and Serous Malignant Ovarian Tumors

Objective

To investigate whether multidetector computed tomography (MDCT)-based radiomics features can discriminate between serous borderline ovarian tumors (SBOTs) and serous malignant ovarian tumors (SMOTs).

Patients and Methods

Eighty patients with SBOTs and 102 patients with SMOTs, confirmed by pathology (training set: n = 127; validation set: n = 55) from December 2017 to June 2020, were enrolled in this study. The interclass correlation coefficient (ICC) and least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics parameters derived from MDCT images on the arterial phase (AP), venous phase (VP), and equilibrium phase (EP). Receiver operating characteristic (ROC) analysis of each selected parameter was carried out. Heat maps were created to illustrate the distribution of the radiomics parameters. Three models incorporating selected radiomics parameters generated by support vector machine (SVM) classifiers in each phase were analyzed by ROC and compared using the DeLong test.

Results

The most predictive features selected by ICC and LASSO regression between SBOTs and SMOTs included 9 radiomics parameters on AP, VP, and EP each. Three models on AP, VP, and EP incorporating the selected features generated by SVM classifiers produced AUCs of 0.80 (accuracy, 0.75; sensitivity, 0.74; specificity, 0.75), 0.86 (accuracy, 0.78; sensitivity, 0.80; specificity, 0.75), and 0.73 (accuracy, 0.69; sensitivity, 0.71; specificity, 0.67), respectively. There were no significant differences in the AUCs among the three models (AP vs. VP, P = 0.199; AP vs. EP, P = 0.260; VP vs. EP, P = 0.793).

Conclusion

MDCT-based radiomics features could be used as biomarkers for the differentiation of SBOTs and SMOTs.

Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging

OBJECTIVES

There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging.

METHODS

Five hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set.

RESULTS

Compared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64, p < 0.001) and specificity (0.92 vs 0.64, p < 0.001) with comparable sensitivity (0.75 vs 0.63, p = 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74, p = 0.033) and specificity (0.92 vs 0.70, p < 0.001) with comparable sensitivity (0.75 vs 0.83, p = 0.557). Assisted by the model's probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13, p < 0.001) and specificity (0.81 vs 0.64, Δ = 0.17, p < 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06, p = 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11, p = 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03, p = 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146, p = 0.097) when compared with the senior radiologists.

CONCLUSIONS

These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance.

KEY POINTS

• Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists. • Assisted by the deep learning model's probabilities, junior radiologists achieved better performance that matched those of senior radiologists.

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

PURPOSE

To investigate the value of CT and MR imaging features in differentiating borderline ovarian tumor (BOT) from type I ovarian epithelial cancer (OEC), which could be significant for suitable clinical treatment and assessment of the prognosis of the patient.

METHODS

Thirty-three patients with BOTs and 35 patients with type I OECs proven by pathology were retrospectively evaluated. The clinico-pathological information (age, premenopausal status, CA-125, and Ki-67) and imaging characteristics were compared between two groups of ovarian tumors. The diagnostic performance of the imaging features was evaluated using receiver operating characteristic analysis. The best predictor variables for type I EOCs were recognized via multivariate analyses.

RESULTS

BOTs are more likely to involve younger patients and frequently show lower CA-125 values and lower proliferation indices (Ki-67 < 15%) than type I OECs. Compared with type I OECs, BOTs were more often purely cystic (15/33, 45.45% vs. 1/35, 2.86%; p < 0.001) and displayed less frequent mural nodules (16/33, 48.48% vs. 28/35, 80.00%; p = 0.007), less frequently unclear margin (3/33, 9.09% vs. 11/35, 31.43%; p = 0.023), smaller solid portion (0.56 ± 2.66 vs. 4.51 ± 3.88; p < 0.001), and thinner walls (0.3 ± 0.17 vs. 0.55 ± 0.24; p < 0.001). The maximum wall thickness presented the largest area under the curve (AUC, 0.848). Multivariate analysis revealed that the solid portion size (OR 10.822, p = 0.002) and maximum wall thickness (OR 9.130, p = 0.001) were independent indicators for the differential diagnosis between the two groups of lesions.

CONCLUSION

The solid portion size and maximum wall thickness significantly influenced the classification of the two groups of ovarian tumors.

Texture Analysis of Three-Dimensional MRI Images May Differentiate Borderline and Malignant Epithelial Ovarian Tumors

Objective

To explore the value of magnetic resonance imaging (MRI)-based whole tumor texture analysis in differentiating borderline epithelial ovarian tumors (BEOTs) from FIGO stage I/II malignant epithelial ovarian tumors (MEOTs).

Materials and Methods

A total of 88 patients with histopathologically confirmed ovarian epithelial tumors after surgical resection, including 30 BEOT and 58 MEOT patients, were divided into a training group (n = 62) and a test group (n = 26). The clinical and conventional MRI features were retrospectively reviewed. The texture features of tumors, based on T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging, were extracted using MaZda software and the three top weighted texture features were selected by using the Random Forest algorithm. A non-texture logistic regression model in the training group was built to include those clinical and conventional MRI variables with p value < 0.10. Subsequently, a combined model integrating non-texture information and texture features was built for the training group. The model, evaluated using patients in the training group, was then applied to patients in the test group. Finally, receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the models.

Results

The combined model showed superior performance in categorizing BEOTs and MEOTs (sensitivity, 92.5%; specificity, 86.4%; accuracy, 90.3%; area under the ROC curve [AUC], 0.962) than the non-texture model (sensitivity, 78.3%; specificity, 84.6%; accuracy, 82.3%; AUC, 0.818). The AUCs were statistically different (p value = 0.038). In the test group, the AUCs, sensitivity, specificity, and accuracy were 0.840, 73.3%, 90.1%, and 80.8% when the non-texture model was used and 0.896, 75.0%, 94.0%, and 88.5% when the combined model was used.

Conclusion

MRI-based texture features combined with clinical and conventional MRI features may assist in differentitating between BEOT and FIGO stage I/II MEOT patients.

Apparent diffusion coefficient (ADC) values of serous, endometrioid, and clear cell carcinoma of the ovary: pathological correlation

BACKGROUND

Primary epithelial ovarian cancer is divided into several subtypes. The relationships between apparent diffusion coefficient (ADC) values and their subtypes have not yet been established.

PURPOSE

To investigate whether ADC values of epithelial ovarian cancer vary according to histologic tumor cellularity and evaluate the difference of clear cell carcinoma (CCC), high-grade serous carcinoma (HGSC), and endometrioid carcinoma (EC).

MATERIAL AND METHODS

This retrospective study included 51 cases of epithelial ovarian cancer (17 CCC, 20 HGSC, and 14 EC) identified by magnetic resonance imaging with pathological confirmation. All patients underwent diffusion-weighted imaging and the ADC values of lesions were measured. We counted the tumor cells in three high-power fields and calculated the average for each case. The Spearman's correlation coefficient test was used to analyze correlation between ADC values and tumor cellularity. The ADC values of HGSC, EC, and CCC were compared using the Steel-Dwass test.

RESULTS

The ADC values of all cases were significantly inversely correlated with tumor cellularity (r_s = -0.761; P < 0.001). The mean ± SD ADC values (×10^-3 mm²/s) of CCC, HGSC, and EC were 1.24 ± 0.17 (range 0.98--1.65), 0.84 ± 0.10 (range 0.67--1.06), and 0.84 ± 0.10 (range 0.67--1.07). The mean ± SD tumor cellularity of CCC, HGSC, and EC was 162.88 ± 63.28 (range 90.33--305.67), 440.60 ± 119.86 (range 204.67--655.67), and 461.02 ± 81.86 (range 333.33--602.33).

CONCLUSION

There is a significant inverse correlation between ADC values and tumor cellularity in epithelial ovarian cancer. The mean ADC value of CCC was higher than those of HGSC and EC, seemingly due to the low cellularity of CCC.

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.

[Borderline Ovarian Tumours: CNGOF Guidelines for Clinical Practice - Imaging]

OBJECTIVE

To determine the place of imaging and the performance of different imaging techniques (transvaginal ultrasound with or without Doppler, scoring, CT, MRI) to differentiate benign tumour, borderline ovarian tumour (BOT) and malignant ovarian tumor. Differentiate the histological subtypes of BOT (serous, sero-mucinous, mucinous) and prediction in imaging of the possibility of conservative treatment.

METHODS

The research was carried out over the last 16 years using the terms "MeSH" based on the query of the Medline® database and supplemented by the review of references contained in the meta-analyzes, systematic reviews and original articles included.

RESULTS

Endo-vaginal and suprapubic ultrasonography is recommended for analysis of an ovarian mass (grade A). In the case of ultrasound by a referent, subjective analysis is the recommended technique (grade A). In case of echography by a non-referent, the use of "Simple Rules" is recommended (grade A) and should be best combined with subjective analysis to rejoin the performance of a sonographer refer (grade A). In cases of undetermined ovarian lesions in endovaginal ultrasound and suprapubic ultrasound, it is recommended to perform a pelvic MRI (grade A). The MRI protocol should include T2, T1, T1 sequences with fat saturation, diffusion, injected dynamics, and after gadolinium injection (grade B). To characterize an MRI-adnexal image, it is recommended to include a risk score for malignancy (ADNEX-MR/O-RADS) (grade C) in the report and to formulate an anatomopathological hypothesis (Grade C). The predictive signs of benignity in front of a cyst with endocystic vegetations are the low number, the small size, the presence of calcifications and the absence of Doppler flow in case of size greater than 10mm in echography (LP 4) and a curve of type 1 MRI (LP4). MRI is recommended for suspicious lesions of BOT in ultrasound (grade B) or indeterminate lesions in ultrasound (grade A). There is no data to support the usefulness of CT or PET-CT for BOT. Morphological criteria in ultrasound and MRI exist to differentiate BOT from invasive tumors regardless of grade (NP 2). Pelvic MRI is recommended to characterize a tumor suggestive of ultrasound BOT (grade C). No recommendations can be made about the use of combined ultrasound, biological, and menopausal status scores for the diagnosis of BOT. The diagnostic performance of imaging to detect peritoneal implants of BOT is not known. The assessment of the invasiveness of peritoneal implants of imaging BOT has not been evaluated. The association of macroscopic signs in MRI makes it possible to differentiate the different subtypes - serous, sero-mucinous and mucinous (intestinal type) - of BOT, despite the overlap of certain presentations (LP3). The analysis of macroscopic MRI signs must be performed to differentiate the different subtypes of TFO (grade C). No recommendation can be made on imaging prediction of the possibility of conservative BOT treatment.

Functional MRI in the pre-operative assessment of GI-RADS 3, 4, and 5 ovarian masses

Background

Characterization of an ovarian lesion is a diagnostic challenge. A correct preoperative assessment is of great importance so as to arrange adequate therapeutic procedures. The aim of the current study is to evaluate the diagnostic performance of functional MRI in differentiation between malignant, borderline, and benign ovarian masses.

Results

This study included 56 adnexal lesions. Bilateral synchronous ovarian lesions are detected in 16 cases. Postoperative histologically proved to be benign in 17 (30%), borderline (low potential malignancy) in 12 (22%), and malignant in 27 (48%). The overall diagnostic performance of conventional MRI in the diagnosis of adenexal lesion was a sensitivity of 74%, specificity of 47%, positive predictive value (PPV) of 76%, negative predictive value (NPV) of 44%, and an accuracy of 66%. Functional pelvic MRI examination showed an increase in overall diagnostic performance compared to conventional values with the highest sensitivity of 90% and NPV of 67% using DWI, and the highest specificity of 88%, PPV of 94%, and an accuracy of 82% using DCE MRI.

Conclusion

Functional MRI in conjugation with conventional MRI plays a key role in the ovarian lesion detection, characterization, and staging. Functional MRI is currently being evaluated as possible predictive and prognostic biomarkers in ovarian lesions.

Multiparametric MRI Evaluation of Complex Ovarian Masses

OBJECTIVE

To assess the role of diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in the categorization of complex ovarian masses into benign and malignant.

MATERIALS AND METHODS

This prospective study was done on 33 complex ovarian masses. T1 and T2-weighted sequences, diffusion-weighted imaging, apparent diffusion coefficient, and dynamic contrast-enhanced magnetic resonance imaging were performed on 1.5 T MRI. Time-intensity curves, tissue signal intensity on unenhanced T1 images (SI₀), maximum absolute contrast enhancement (SI_max), time to reach SI_max (T_max), maximum relative SI (SI_rel = [SI_max - SI₀]/SI₀ ×100), maximum Slope (Slope_max = SI_rel/T_max ×100), and wash in rate (WIR = [SI_max - SI₀]/T_max) were calculated. Histopathological diagnosis was taken as gold standard.

RESULTS

A total of 20/33 masses were benign, 2/33 were borderline tumors, and 11/33 were malignant. Diffusion restriction was seen in all malignant masses and 13/20 benign masses. The mean apparent diffusion coefficient values showed a significant difference between malignant and benign, with 81.8% sensitivity and 63.6% specificity. Type III curve showed 100% specificity for malignant lesions. T_max and Slope_max were useful in differentiating benign and malignant masses; with T_max cut-off at 73.5 seconds having a high specificity (81.8%) and Slope_max cut-off at 0.83%/s having high sensitivity (91%) and negative predictive value (94.4%).

CONCLUSION

Multiparametric MRI confers high diagnostic accuracy in stratifying complex ovarian masses.

Apparent diffusion coefficients measured using different regions of interest in differentiating borderline from malignant ovarian tumors

Background

Apparent diffusion coefficients (ADCs) measured using different regions of interest (ROIs) are widely used in differentiating ovarian tumors.

Purpose

To evaluate the diagnostic performance of ADCs with different ROIs in differentiating borderline ovarian tumors (BOTs) from malignant ovarian tumors (MOTs).

Material and Methods

Thirty-five BOTs and 54 MOTs who underwent diffusion-weighted magnetic resonance imaging (MRI) were evaluated retrospectively. ADC values were independently measured using five ROI methods: round; rectangle; hot-spot; edge drawing; and five sample ROIs. The inter- and intraclass correlation coefficients (ICCs), one-way analysis of variance, receiver operating characteristic curve analysis, and unpaired t-tests were used to perform the statistical analyses.

Results

The measurement reproducibility of the minimum ADC and mean ADC values were good or excellent for BOTs and MOTs (ICC = 0.70–0.95). The minimum and mean ADC value by the edge drawing ROI were significantly higher than those of the other ROI methods (both P < 0.05). The area under the curve (AUC) of the minimum ADC value was less than that of the mean ADC value from the five ROI methods, whereas the AUCs of the mean ADC values from the round ROI and five sample ROIs were significantly larger than those of the other ROI methods ( P < 0.05). The minimum and mean ADC values from the five ROI methods showed significant differences between BOTs and MOTs (all P < 0.05).

Conclusion

The ROI shape influences the diagnostic performance of ADC value for differentiating BOTs from MOTs. The mean ADC values from the round ROI and five sample ROIs have better diagnostic efficiency.

Intravoxel Incoherent Motion Imaging in Differentiation Borderline From Malignant Ovarian Epithelial Tumors: Correlation With Histological Cell Proliferation and Vessel Characteristics

BACKGROUND

The differentiation of borderline from malignant ovarian epithelial tumors (OETs) is difficult based on morphologic characteristics. Diffusion and perfusion information from intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) might be useful for this distinction.

PURPOSE

To investigate the potential of IVIM-DWI in discriminating borderline from malignant OETs by correlating with cell proliferation and microvessel density (MVD).

STUDY TYPE

Prospective.

SUBJECTS

Sixty-six patients with OETs (22 borderline, BOETs; 44 malignant, MOETs) underwent IVIM-DWI before surgery.

FIELD STRENGTH

3.0T IVIM-DWI sequence with 12 b-values (0-1000 sec/mm² ).

ASSESSMENT

Apparent diffusion coefficient (ADC) and IVIM-DWI parameters (diffusion coefficient [D], microvascular volume fraction [f], and pseudodiffusion coefficient [D*]) were measured. Cell proliferation and MVD was determined by immunohistochemical staining of Ki-67 and CD-31, respectively.

STATISTICAL TESTS

Mann-Whitney U-test; two-sample t-test; intraclass correlation coefficient; Bland-Altman analysis; receiver operating characteristics (ROC) curves; and Spearman correlation.

RESULTS

ADC and D in BOETs was significantly higher than those in MOETs (P < 0.001), while f in BOETs was significantly lower than those in MOETs (P < 0.001). No significant difference was found in D* between borderline and malignancies (P = 0.324). In the differential diagnosis of borderline from malignant OETs; D demonstrated the highest area under the curve (AUC) of 0.951, while ADC and f had a lower AUC of 0.921 and 0.847, respectively. The ADC and D was negatively correlated with cell proliferation (r = -0.682, r = -0.694, respectively, P < 0.001), while f was positively correlated with MVD of the OETs (r = 0.558, P < 0.001).

DATA CONCLUSION

IVIM-DWI may be a useful tool for differentiating BOETs from MOETs. D and f could be image biomarkers to reflect histopathological characteristics of cell proliferation and MVD in OETs.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:928-935.

Value of normalized apparent diffusion coefficients in differentiating between borderline and malignant epithelial ovarian tumors

PURPOSE

To compare the diagnostic performance of normalized apparent diffusion coefficients (nADCs) of different references with that of ADCs at differentb factors in differentiating borderline epithelial ovarian tumors (BEOTs) from malignant epithelial ovarian tumors (MEOTs).

METHOD

This retrospective study included 53 BEOTs and 148 MEOTs. Conventional magnetic resonance and diffusion-weighted imaging withb factors of 800 and 1000s/mm² were performed. ADC was measured three times at solid components of tumors, gluteus maximus muscle (GMM), iliopsoas muscle (IM) and urine and then averaged. ADC_tumor, nADCs were then obtained. Differences and the diagnostic performance of ADC_tumor and nADCs between BEOTs and MEOTs with different b factors were compared.

RESULTS

ADC_tumor, nADCs regardless of b factors were significantly higher in BEOTs than MEOTs. The diagnostic performance of nADC_urine regardless of b factors was significantly larger than that of nADC_GMM and nADC_IM. There was no significant difference in the diagnostic performance between ADC_tumor and nADC_urine regardless of b factors. A significantly lower ADC_tumor and a larger diagnostic performance for ADC_tumor was found with a b factor of 1000s/mm² than 800 s/mm². There were no significant differences in nADC_urine of BEOTs or MEOTs or in the diagnostic performance of nADC_urine with b factors between 800 and 1000s/mm².

CONCLUSIONS

ADC_tumor and nADCs were both capable of differentiating BEOTs from MEOTs. nADC_urine was the best of all nADCs and was superior to ADC_tumor because of its stable performance in differentiating BEOTs from MEOTs, regardless of b factors.

Imaging findings for mucinous tumors of the abdomen and pelvis

This article aims to describe the imaging findings for mucinous tumors of the abdomen and pelvis, which have a similar appearance on imaging tests regardless of the organ in which they develop. Due to the high water content of mucus, the appearance of these tumors is generally similar to that of water on ultrasonography, computed tomography, and magnetic resonance imaging. Another common feature of mucin-producing tumors is that calcifications are often present. The rupture of these lesions and accumulation of mucinous material in the peritoneal cavity gives rise to pseudomyxoma peritonei. It is important to identify mucinous tumors because they have a different prognosis and clinical course than non-mucinous tumors and require different management. Depending on their anatomic location and their imaging characteristics, the treatment approach varies from follow-up to radical surgery together with chemotherapy or radiotherapy or both.

[Diagnostic value of imaging (ultrasonography, doppler, CT, MR, PET-CT) for the diagnosis of a suspicious ovarian mass and staging of ovarian, tubal or primary peritoneal cancer: Article drafted from the French Guidelines in oncology entitled "Initial management of patients with epithelial ovarian cancer" developed by FRANCOGYN, CNGOF, SFOG, GINECO-ARCAGY under the aegis of CNGOF and endorsed by INCa]

Transvaginal ultrasound is the first-line examination allowing characterizing 80 to 90% of adnexal masses (LP1). If performed by an expert, a subjective analysis is optimal. If performed by a non-expert, combining the use of Simple Rules with subjective analysis can achieve the diagnostic performance of an expert (LP1). Whichever the chosen model (subjective analysis by an expert or combination of the Simple Rules with a subjective analysis by a non-expert), a second-line examination will have to be proposed in the complex or indeterminate cases (about 20% of the masses) (grade A). The best-performing second-line test for characterization is pelvic MRI (LP1). If read by an expert, a pathological hypothesis can or should be suggested (grade D). In case of non-expert reading, the use of the ADNEXMR score allows a reliable assessment of the positive predictive value of malignancy to guide the patient towards the best management (gradeC). For preoperative assessment and evaluation of resectability of ovarian, fallopian tube or primary peritoneal cancer, it is recommended to perform a chest abdomen and pelvis CT with contrast agent injection (LP2, grade B). In the event of a contraindication to the injection of iodinated contrast agent (severe renal insufficiency, GFR <30mL/min), an abdomen and pelvis MRI completed with a non-injected chest CT may be proposed (LP3, grade C). By analogy, the same examinations are recommended to evaluate the disease after neo-adjuvant chemotherapy (LP3, Recommendation grade C). Further studies will be required to determine whether PET-CT provides better lymph node assessment before retroperitoneal and pelvic lymphadenectomy. PET-CT may be used to eliminate lymph node involvement in the absence of suspicious lymph nodes on morphological examination (LP3, grade C). The report should specify the localizations leading to a risk of incomplete cytoreductive surgery and lesions outside the field explored during surgery.

Utility of DWI with quantitative ADC values in ovarian tumors: a meta-analysis of diagnostic test performance

Background Diffusion-weighted imaging (DWI) and quantitative apparent diffusion coefficient (ADC) values are widely used in the differential diagnosis of ovarian tumors. Purpose To assess the diagnostic performance of quantitative ADC values in ovarian tumors. Material and Methods PubMed, Embase, the Cochrane Library, and local databases were searched for studies assessing ovarian tumors using quantitative ADC values. We quantitatively analyzed the diagnostic performances for two clinical problems: benign vs. malignant tumors and borderline vs. malignant tumors. We evaluated diagnostic performances by the pooled sensitivity and specificity values and by summary receiver operating characteristic (SROC) curves. Subgroup analyses were used to analyze study heterogeneity. Results From the 742 studies identified in the search results, 16 studies met our inclusion criteria. A total of ten studies evaluated malignant vs. benign ovarian tumors and six studies assessed malignant vs. borderline ovarian tumors. Regarding the diagnostic accuracy of quantitative ADC values for distinguishing between malignant and benign ovarian tumors, the pooled sensitivity and specificity values were 0.91 and 0.91, respectively. The area under the SROC curve (AUC) was 0.96. For differentiating borderline from malignant tumors, the pooled sensitivity and specificity values were 0.89 and 0.79, and the AUC was 0.91. The methodological quality of the included studies was moderate. Conclusion Quantitative ADC values could serve as useful preoperative markers for predicting the nature of ovarian tumors. Nevertheless, prospective trials focused on standardized imaging parameters are needed to evaluate the clinical value of quantitative ADC values in ovarian tumors.

Endometrial Stromal Sarcoma of the Uterus: Magnetic Resonance Imaging Findings Including Apparent Diffusion Coefficient Value and Its Correlation With Ki-67 Expression

OBJECTIVE

This study aimed to investigate the conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) features of endometrial stromal sarcoma (ESS) including a preliminary investigation of the correlation between the apparent diffusion coefficient (ADC) value and Ki-67 expression.

METHODS

The clinical and MRI data of 15 patients with ESS confirmed by surgery and pathology were analyzed retrospectively. The conventional MR morphological features, signal intensity on DWI, ADC value (n = 14), and clinicopathological marker Ki-67 (n = 13) were evaluated.

RESULTS

Of 15 patients with ESS, 13 tumors were low-grade ESS (LGESS), and the remaining 2 were high-grade ESS (HGESS); 9 tumors were located in the myometrium, 5 were located in the endometrium and/or cervical canal, and 1 was located in extrauterine. Thirteen (87%) of 15 tumors showed a homo- or heterogeneous isointensity on T1-weighted imaging and a heterogeneous hyperintensity on T2-weighted imaging. The hypointense bands were observed in 11 tumors (73%) on T2-weighted imaging. The degenerations (cystic/necrosis/hemorrhage) were observed in 7 LGESS tumors and 2 HGESS tumors. The DWI hyperintensity was observed in 13 tumors (93%) and isointensity in remaining 1. The mean ADC value of the solid components in 14 ESSs was (1.05 ± 0.20) × 10mm/s. The contrast-enhanced MRI showed an obvious enhancement in 14 tumors (93%) (heterogeneous in 7 LGESSs and 2 HGESSs; homogeneous in 5 LGESSs). The ADC value was inversely correlated with the Ki-67 expression (r = -0.613, P = 0.026).

CONCLUSIONS

Patients with ESS showed some characteristics on conventional MRI and DWI, and there was an inverse correlation between the ADC value and Ki-67 expression.

MRI appearance of ovarian serous borderline tumors of the micropapillary type compared to that of typical ovarian serous borderline tumors: radiologic-pathologic correlation

Background

Serous borderline tumor (SBT) of the micropapillary type (SBT-MP) became one of the major pathological SBT diagnoses in addition to typical SBT, and was also defined as “non-invasive” low-gradeserous carcinoma according to the World Health Organization (WHO) classification in 2014. In this study, we investigated the MRI appearance of SBT-MP compared to that of typical SBT in order to identify specific imaging features of SBT-MP that correspond to pathological findings.

Methods

MR images of 6 histologically proven ovarian SBT-MP in four patients and 14 typical SBT in ten patients were reviewed retrospectively. Images were evaluated for laterality, size and morphology of the lesion and the solid component (SC) and signal intensity (SI) of the SC. MRI findings were correlated with pathological findings.

Results

The patients with SBT-MP (mean 26.3 years) were younger than those with typical SBT (mean 44.5 years). Postoperative staging in patients with SBT-MP was II in two and III in two cases, while staging for typical SBT was I in seven, II in one and III in two cases. The morphologic patterns of SBT-MP were a unilateral cystic mass with intracystic mural nodules (CwMN) (n = 2), bilateral solid papillary masses (SM), and bilateral SM with CwMN. The pattern of typical SBT was CwMN (n = 13) in all but one lesion (SM with CwMN). All SCs showed inhomogeneous slight hyperintensity on T2 weighted images (WI) and high SI on diffusion-WI (DWI) except for in one typical SBT. Although diffuse proliferation of the tumor cells in micropapillary projections with little stroma seemed to correspond to inhomogeneous slightly hyperintense foci in SC on T2WI and high SI on DWI, similar MR findings were observed in typical SBT in all lesions on T2WI and 11 of 12 lesions on DWI. In typical SBT, inhomogeneous slightly hyperintense foci in SC on T2WI and high SI on DWI corresponded to highly cellular foci with densely branched papillae.

Conclusion

Pathological findings and clinical behavior of SBT-MP differed from those of typical SBT, but morphology and SI of SC on MRI were similar, with papillary projections demonstrating inhomogeneous slight hyperintensity on T2WI and high SI on DWI.

Intraoperative Frozen Section of Ovarian Tumors: A 6-Year Review of Performance and Potential Pitfalls in an Australian Tertiary Referral Center

OBJECTIVES

Intraoperative frozen section (IFS) offers a rapid test to guide the extent of surgery, which is essential for optimal treatment of ovarian cancer. This study evaluated the diagnostic performance and influence of IFS in the surgical management of ovarian tumors.

METHODS

A retrospective review was conducted of IFS of adnexal lesions from 2008 to 2013, with diagnoses classified as benign, borderline, or malignant. The diagnostic performance of IFS was calculated, with a focus on primary epithelial tumors. In discordant cases, it was determined whether the results of the IFS influenced the nature of the primary surgery.

RESULTS

There were 277 consecutive cases over the study period. The overall sensitivity for diagnosing malignant disease was 75.9% and the specificity was 100%. With a benign IFS result, there was a 6.25% (9/144) chance that the final diagnosis would be malignant, and a 7.6% (11/144) chance that the final diagnosis would be borderline, resulting in the potential for understaging. The predictive values for benign, borderline, and malignant IFS results were 86.1%, 66.6%, and 100%, respectively. For a borderline IFS result, there was a 33.3% chance that the final diagnosis would be malignant disease, and this was higher in older patients (53.3%). There were no instances of overdiagnosis in this series. Of 37 cases underdiagnosed, 19 received incomplete primary staging surgery guided by the IFS, and most of these were mucinous tumors.

CONCLUSIONS

Intraoperative frozen section is most valuable for its high specificity in diagnosing malignancy. It should be interpreted with caution in borderline tumors, particularly in older patients and in mucinous tumors. Overdiagnosis did not occur in this series; however, in younger patients, the limitations of IFS must be considered before surgery that would result in loss of fertility.

MRI features and score for differentiating borderline from malignant epithelial ovarian tumors

PURPOSE

To identify the MRI features of borderline epithelial ovarian tumors (BEOTs) and to differentiate BEOTs from malignant epithelial ovarian tumors (MEOTs).

MATERIALS AND METHODS

The clinical and MRI data of 89 patients with a BEOT and 109 patients with a MEOT proven by surgery and histopathology were retrospectively reviewed. MRI features, including bilaterality, size, shape, margin, cystic-solid interface, configuration, papillae or nodules, signal intensity, enhancement, presence of an ipsilateral ovary, peritoneal implants and ascites were analyzed and compared. Based on the odds ratio (OR) values, the significant risk features for BEOTs were scored as 3 (OR≈∞), 2 (5≤OR<∞) or 1 (OR<5).

RESULTS

There were 89 BEOT patients with 113 tumors [mean size of (13±6.7)cm], with bilateral ovary involvement in 24 cases. There were 109 MEOT patients with 142 tumors [(9.3±4.2)cm] with bilateral ovary involvement in 33 cases. There were eight significant risk factors for BEOTs, including round or oval shape (OR=2.714), well-defined margins (OR=3.318), clear cystic-solid interfaces (OR=5.593), purely cystic (OR=15.206), predominantly cystic with papillae or nodules (OR=2.579), exophytic papillae or nodules (OR=5.351), branching papilla (OR≈∞) and the presence of an ipsilateral ovary (OR≈∞). Based on the scoring of the eight risk factors, a cut-off score of 3.5 yielded a differential sensitivity, specificity, and accuracy of 82%, 85% and 84%, respectively.

CONCLUSION

In contrast to MEOTs, BEOTs frequently had the following features on MRI: round or oval, with well-defined margins and clear cystic-solid interfaces, purely cystic or predominantly cystic with papillae or nodules, branching or exophytic papillae, with the presence of an ipsilateral ovary. MRI can reveal the distinct morphological features of BEOTs and MEOTs and facilitate their discrimination.

Fertility-sparing for young patients with gynecologic cancer: How MRI can guide patient selection prior to conservative management

Historically, cancer treatment has emphasized measures for the "cure" regardless of the long-term consequences. Advances in cancer detection and treatment have resulted in improved outcomes bringing to the fore various quality of life considerations including future fertility. For many young cancer patients, fertility preservation is now an integral component of clinical decision-making and treatment design. Optimal fertility-sparing options for young patients with gynecologic cancer are influenced by patient age, primary cancer, treatment regimens, and patient preferences. Possible approaches include embryo or oocyte cryopreservation, ovarian transposition, conservative surgery, and conservative medical treatment to delay radical surgery. These may be used alone or in combination to maximize fertility preservation. Awareness of the various fertility-sparing options, eligibility criteria, and the central role of magnetic resonance imaging in the proper selection of patients will enable radiologists to produce complete clinically relevant imaging reports and serve as effective consultants to referring clinicians. Knowledge of the potential imaging pitfalls is essential to avoid misinterpretation and guide appropriate management.

Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis

The purpose of this meta-analysis was to provide clinical evidence regarding relationship between ADC and cellularity in different tumors based on large patient data.

Medline library was screened for associations between ADC and cell count in different tumors up to September 2016. Only publications in English were extracted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) was used for the research.

Overall, 39 publications with 1530 patients were included into the analysis. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients.

The pooled correlation coefficient for all studies was ρ = -0.56 (95 % CI = [−0.62; −0.50]),. Correlation coefficients ranged from ρ =−0.25 (95 % CI = [−0.63; 0.12]) in lymphoma to ρ=−0.66 (95 % CI = [−0.85; −0.47]) in glioma. Other coefficients were as follows: ovarian cancer, ρ = −0.64 (95% CI = [−0.76; −0.52]); lung cancer, ρ = −0.63 (95 % CI = [−0.78; −0.48]); uterine cervical cancer, ρ = −0.57 (95 % CI = [−0.80; −0.34]); prostatic cancer, ρ = −0.56 (95 % CI = [−0.69; −0.42]); renal cell carcinoma, ρ = −0.53 (95 % CI = [−0.93; −0.13]); head and neck squamous cell carcinoma, ρ = −0.53 (95 % CI = [-0.74; −0.32]); breast cancer, ρ = −0.48 (95 % CI = [−0.74; −0.23]); and meningioma, ρ = -0.45 (95 % CI = [−0.73; −0.17]).

Diagnostic accuracy of DWI in patients with ovarian cancer: A meta-analysis

BACKGROUND

Diffusion weighted imaging (DWI) is recently developed for identifying different malignant tumors. In this article the diagnostic accuracy of DWI for ovarian cancer was evaluated by synthesis of published data.

METHODS

A comprehensive literature search was conducted in PubMed/MEDLINE and Embase databases on the diagnostic performance of DWI for ovarian cancer published in English. Methodological quality was evaluated following Quality Assessment for Studies of Diagnostic Accuracy 2 (QUADAS 2) tool. We adopted the summary receiver operating characteristic (SROC) curve to assess the DWI accuracy.

RESULTS

Twelve studies including 1142 lesions were analyzed in this meta-analysis to estimate the pooled Sen (sensitivity), Spe (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), and construct SROC (summary receiver operating characteristics) curve. The pooled Sen and Spe were 0.86 (95% confidence interval [CI], 0.83-0.89) and 0.81 (95%CI, 0.77-0.84), respectively. The pooled PLR and pooled NLR were 5.07 (95%CI, 3.15-8.16) and 0.17 (95%CI, 0.10-0.30), respectively. The pooled diagnostic odds ratio (DOR) was 35.23 (95%CI, 17.21-72.14). The area under the curve (AUC) was 0.9160.

CONCLUSION

DWI had moderately excellent diagnostic ability for ovarian cancer and promised to be a helpful diagnostic tool for patients of ovarian cancer.

Multiparametric MRI for differentiation of borderline ovarian tumors from stage I malignant epithelial ovarian tumors using multivariate logistic regression analysis

OBJECTIVE

To assess the value of contrast-enhanced MRI, apparent diffusion coefficient (ADC) measurement, and CA-125 measurement for differentiating borderline ovarian tumors (BOTs) from stage I malignant epithelial ovarian tumors (MEOTs).

MATERIAL AND METHODS

This retrospective study included 43 patients with BOTs and 43 patients with stage I MEOTs who underwent contrast-enhanced MRI with DWI and CA-125 analysis. Two radiologists evaluated the MRI findings in consensus. Univariate and multivariate analyses were performed to detect the best predictor variables for MEOTs.

RESULTS

Mixed cystic/solid and predominantly solid appearances, as well as thickened irregular septa, were more frequent in MEOTs. A papillary architecture and internal branching (PA&IB) pattern was more frequent in BOTs. MEOTs had thicker walls and septa, larger solid components, and higher CA-125 values. The mean ADC value of solid components (ADC_mean) and minimum ADC value of whole lesions (ADC_min) were lower in MEOTs. Multivariate analysis revealed that ADC_min and maximum diameter of the solid components were independent indicators of MEOTs with an AUC, sensitivity, and specificity of 0.86, 81%, and 84%, respectively.

CONCLUSION

ADC_min and maximum diameter of solid components were useful for differentiating BOTs from MEOTs.

The distribution of the apparent diffusion coefficient as an indicator of the response to chemotherapeutics in ovarian tumour xenografts

Diffusion-weighted magnetic resonance imaging (DW-MRI) was used to evaluate the effects of single-agent and combination treatment regimens in a spheroid-based animal model of ovarian cancer. Ovarian tumour xenografts grown in non-obese diabetic/severe-combined-immunodeficiency (NOD/SCID) mice were treated with carboplatin or paclitaxel, or combination carboplatin/paclitaxel chemotherapy regimens. After 4 weeks of treatment, tumours were extracted and underwent DW-MRI, mechanical testing, immunohistochemical and gene expression analyses. The distribution of the apparent diffusion coefficient (ADC) exhibited an upward shift as a result of each treatment regimen. The 99-th percentile of the ADC distribution (“maximum ADC”) exhibited a strong correlation with the tumour size (r² = 0.90) and with the inverse of the elastic modulus (r² = 0.96). Single-agent paclitaxel (n = 5) and combination carboplatin/paclitaxel (n = 2) treatment regimens were more effective in inducing changes in regions of higher cell density than single-agent carboplatin (n = 3) or the no-treatment control (n = 5). The maximum ADC was a good indicator of treatment-induced cell death and changes in the extracellular matrix (ECM). Comparative analysis of the tumours’ ADC distribution, mechanical properties and ECM constituents provides insights into the molecular and cellular response of the ovarian tumour xenografts to chemotherapy. Increased sample sizes are recommended for future studies. We propose experimental approaches to evaluation of the timeline of the tumour’s response to treatment.

Role of Imaging Tools for the Diagnosis of Borderline Ovarian Tumors: A Systematic Review and Meta-Analysis

Borderline ovarian tumors (BOTs) have a low malignant potential and occur most often in young women. Its preoperative diagnosis can improve surgical strategy and reproductive approach. This study systematically reviews the literature for the accuracy of transvaginal ultrasound (TVUS), magnetic resonance (MRI), and computed tomography (CT) in the diagnostic of BOTs. We conducted a search in PubMed/Medline of articles in English from the last 5 years and included 14 studies for systematic review, 9 of them in the meta-analysis. The pooled sensibility and specificity was respectively 77.0% and 83.0% for TVUS (5 studies) and 85% and 74% for MRI (4 studies) in differentiating benign from malignant BOTs. CT and positron emission tomography (PET)-CT techniques were heterogeneous between studies, so a meta-analysis was not performed for the 4 studies on CT and PET-CT. However, these 4 studies have also shown a high accuracy in differentiating BOTs from malignant ovarian cancers.

Diagnostic performance of MR imaging findings and quantitative values in the differentiation of seromucinous borderline tumour from endometriosis-related malignant ovarian tumour

OBJECTIVES

To evaluate the diagnostic performance of quantitative values and MRI findings for differentiating seromucinous borderline tumours (SMBTs) from endometriosis-related malignant ovarian tumours (MT).

METHODS

This retrospective study examined 19 lesions from SMBT and 84 lesions from MT. The following quantitative values were evaluated using receiver-operating characteristic analysis: overall and solid portion sizes, fluid signal intensity (SI), degree of contrast-enhancement, and mean and minimum apparent diffusion coefficient (ADC) values of the solid portion. Two radiologists independently evaluated four MRI findings characteristic of SMBT, fluid SI on the T1-weighted image and SI of the solid portion on diffusion-weighted image. The diagnostic values of these findings and interobserver agreement were assessed.

RESULTS

For diagnosing SMBT, the mean ADC value of the solid portion showed the greatest area under the curve (0.860) (cut-off value: 1.31 × 10^-3 mm²/s, sensitivity: 1.00, specificity: 0.61). The T2-weighted image (T2WI) high SI solid portion was the most useful finding, with high specificity and interobserver agreement (sensitivity, 0.58; specificity, 0.95-0.96, kappa = 0.96), followed by T2WI low SI core (sensitivity, 0.48-0.63; specificity, 0.98, kappa = 0.68).

CONCLUSION

Mean ADC values of the solid portion, T2WI high SI solid portion, and T2WI low SI core were useful for differentiating SMBT from MT.

KEY POINTS

• SMBT is a newly categorised ovarian tumour often associated with endometriosis. • Differentiation of SMBT from endometriosis-related malignant ovarian tumour is clinically important. • Diagnostic performances of quantitative values and MRI findings were evaluated. • Mean ADC value of the solid portion was the most useful value. • "T2WI high SI solid portion" was the most useful MRI finding.

Tubo-Ovarian Abscess (with/without Pseudotumor Area) Mimicking Ovarian Malignancy: Role of Diffusion-Weighted MR Imaging with Apparent Diffusion Coefficient Values

Objective

To assess the added value of diffusion-weighted magnetic resonance imaging (DWI) with apparent diffusion coefficient (ADC) values compared to MRI, for characterizing the tubo-ovarian abscesses (TOA) mimicking ovarian malignancy.

Materials and Methods

Patients with TOA (or ovarian abscess alone; n = 34) or ovarian malignancy (n = 35) who underwent DWI and MRI were retrospectively reviewed. The signal intensity of cystic and solid component of TOAs and ovarian malignant tumors on DWI and the corresponding ADC values were evaluated, as well as clinical characteristics, morphological features, MRI findings were comparatively analyzed. Receiver operating characteristic (ROC) curve analysis based on logistic regression was applied to identify different imaging characteristics between the two patient groups and assess the predictive value of combination diagnosis with area under the curve (AUC) analysis.

Results

The mean ADC value of the cystic component in TOA was significantly lower than in malignant tumors (1.04 ± 0 .41 × 10⁻³ mm²/s vs. 2.42 ± 0.38 × 10⁻³ mm²/s; p < 0.001). The mean ADC value of the enhanced solid component in 26 TOAs was 1.43 ± 0.16×10⁻³mm²/s, and 46.2% (12 TOAs; pseudotumor areas) showed significantly higher signal intensity on DW-MRI than in ovarian malignancy (mean ADC value 1.44 ± 0.20×10⁻³ mm²/s vs.1.18 ± 0.36 × 10⁻³ mm²/s; p = 0.043). The combination diagnosis of ADC value and dilated tubal structure achieved the best AUC of 0.996. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of MRI vs. DWI with ADC values for predicting TOA were 47.1%, 91.4%, 84.2%, 64%, and 69.6% vs. 100%, 97.1%, 97.1%, 100%, and 98.6%, respectively.

Conclusions

DW-MRI is superior to MRI in the assessment of TOA mimicking ovarian malignancy, and the ADC values aid in discriminating the pseudotumor area of TOA from the solid portion of ovarian malignancy.