MR Imaging-Pathologic Correlation in Ovarian Cancer

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.

The Application of Nanoparticle-Based Imaging and Phototherapy for Female Reproductive Organs Diseases

Various female reproductive disorders affect millions of women worldwide and bring many troubles to women's daily life. Let alone, gynecological cancer (such as ovarian cancer and cervical cancer) is a severe threat to most women's lives. Endometriosis, pelvic inflammatory disease, and other chronic diseases-induced pain have significantly harmed women's physical and mental health. Despite recent advances in the female reproductive field, the existing challenges are still enormous such as personalization of disease, difficulty in diagnosing early cancers, antibiotic resistance in infectious diseases, etc. To confront such challenges, nanoparticle-based imaging tools and phototherapies that offer minimally invasive detection and treatment of reproductive tract-associated pathologies are indispensable and innovative. Of late, several clinical trials have also been conducted using nanoparticles for the early detection of female reproductive tract infections and cancers, targeted drug delivery, and cellular therapeutics. However, these nanoparticle trials are still nascent due to the body's delicate and complex female reproductive system. The present review comprehensively focuses on emerging nanoparticle-based imaging and phototherapies applications, which hold enormous promise for improved early diagnosis and effective treatments of various female reproductive organ diseases.

MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols

Background

Epithelial ovarian cancer (EOC) is the most malignant gynecological tumor in women. This study aimed to construct and compare radiomics-clinical nomograms based on MR images in EOC prognosis prediction.

Methods

A total of 186 patients with pathologically proven EOC were enrolled and randomly divided into a training cohort (n = 130) and a validation cohort (n = 56). Clinical characteristics of each patient were retrieved from the hospital information system. A total of 1116 radiomics features were extracted from tumor body on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Paired sequence signatures were constructed, selected and trained to build a prognosis prediction model. Radiomic-clinical nomogram was constructed based on multivariate logistic regression analysis with radiomics score and clinical features. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and calibration curve.

Results

The T2WI radiomic-clinical nomogram achieved a favorable prediction performance in the training and validation cohort with an area under ROC curve (AUC) of 0.866 and 0.818, respectively. The DCA showed that the T2WI radiomic-clinical nomogram was better than other models with a greater clinical net benefit.

Conclusion

MR-based radiomics analysis showed the high accuracy in prognostic estimation of EOC patients and could help to predict therapeutic outcome before treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00941-7.

Current update on malignant epithelial ovarian tumors

Epithelial ovarian cancer (EOC) represents the most frequently occurring gynecological malignancy, accounting for more than 70% of ovarian cancer deaths. Preoperative imaging plays an important role in assessing the extent of disease and guides the next step in surgical decision-making and operative planning. In this article, we will review the multimodality imaging features of various subtypes of EOC. We will also discuss the role of imaging in the staging, management, and surveillance of EOC.

Low-grade serous epithelial ovarian cancer: a comprehensive review and update for radiologists

Low-grade serous carcinoma (LGSC) is an infrequent subtype of ovarian cancer, corresponding to 5% of epithelial neoplasms. This subtype of ovarian carcinoma characteristically has molecular features, pathogenesis, clinical behaviour, sensitivity to chemotherapy, and prognosis distinct to high-grade serous carcinoma (HGSC). Knowing the difference between LGSC and other ovarian serous tumours is vital to guide clinical management, which currently is only possible histologically. However, imaging can provide several clues that allow differentiating LGSC from other tumours and enable precise staging and follow-up of ovarian cancer treatment. Characteristically, LGSC appears as mixed lesions with variable papillary projections and solid components, usually in different proportions from those detected in serous borderline tumour and HGSC. Calcified extracellular bodies, known as psammoma bodies, are also a common feature of LGSC, frequently detectable within lymphadenopathies and metastases associated with this type of tumour. In addition, the characterisation of magnetic resonance imaging enhancement also plays an essential role in calculating the probability of malignancy of these lesions. As such, in this review, we discuss and update the distinct radiological modalities features and the clinicopathologic characteristics of LGSC to allow radiologists to be familiarised with them and to narrow the differential diagnosis when facing this type of tumour.

Usefulness of dynamic contrast-enhanced magnetic resonance imaging in characterizing ovarian tumors classified as indeterminate at ultrasonography

OBJECTIVES

To determine whether there is a significant relationship between the shape of the time-intensity curve on dynamic gadolinium-enhanced magnetic resonance imaging (MRI) of ovarian tumors classified as indeterminate at ultrasonography and the type of lesion (benign, borderline, or malignant) to enable an accurate presurgical diagnosis.

MATERIAL AND METHODS

We used dynamic contrast-enhanced MRI to study 68 ovarian tumors that were classified as indeterminate at ultrasonography. We included only cases for which a definitive diagnosis (histologic diagnosis or ≥1 year stability on imaging tests) was available. Each case was classified as benign, borderline, or malignant. To analyze the MRI studies, we marked regions of interest in the lesion and in the myometrium (as a reference). We obtained a curve defined by the relation between the intensity of enhancement and time and classified each tumor according to four predefined curve types. We also analyzed semiquantitative parameters. Finally, we compared the results for each of the three groups of tumors.

RESULTS

We found significant associations (p <0.001) between the curves without early enhancement and benign and borderline lesions as well as between the curves with early enhancement and malignant lesions. Malignant lesions were significantly associated with the semiquantitative enhancement parameters: maximum (p=0.002), maximum relative (p=0.006), and relative (p=0.018).

CONCLUSIONS

In ovarian tumors classified as indeterminate at ultrasonography, dynamic contrast-enhanced MRI can be useful for classification as benign, borderline, or malignant because the malignant lesions are significantly associated with early enhancement curves.

MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes

OBJECTIVE

To compare the performance of clinical features, conventional MR image features, ADC value, T2WI, DWI, DCE-MRI radiomics, and a combined multiple features model in predicting the type of epithelial ovarian cancer (EOC).

METHODS

In this retrospective analysis, 61 EOC patients were confirmed by histology. Significant features (p < 0.05) by multivariate logistic regression were retained to establish a clinical model, conventional MRI morphological model, ADC model, and traditional model. The radiomics model included FS-T2WI, DWI, and DCE-MRI, and also, a multisequence model was established. A total of 1070 radiomics features of each sequence were extracted; then, univariate analysis and LASSO were used to select important features. Traditional models were combined with a combined radiomics model to establish a mixed model. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the traditional model in identifying early- and late-stage EOC.

RESULTS

Traditional models showed the highest performance (AUC = 0.96). The performance of the mixed model (AUC = 0.97) was not significantly different from that of the traditional model. The calibration curve showed that the traditional model had the highest reliability. Stratified analysis showed the potential of the combined radiomics model in the early distinction of the two tumor types.

CONCLUSION

The traditional model is an effective tool to distinguish EOC type I/II. Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.

KEY POINTS

• The combined radiomics model resulted in a better predictive model than that from a single sequence model. • The traditional model showed higher classification accuracy than the combined radiomics model. • Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.

Imaging of ovarian cancers using enzyme activatable probes with second near-infrared window emission

We herein develop two β-galactosidase (β-Gal) activatable NIR fluorescent probes for visualizing ovarian cancers. Particularly, probe BOD-M-βGal produced NIR-II emission light at 900-1300 nm upon β-Gal activation. By using our activatable and target specific NIR-II probe for deep-tissue imaging of β-Gal overexpressed ovarian cancer cells, rapid and accurate imaging of ovarian tumors in nude mice was achieved.

Imaging in pediatric ovarian tumors

The spectrum of ovarian tumors in the pediatric population differs significantly from that in adults. Germ cell tumors are the predominant class of ovarian tumors in children, whereas epithelial tumors are the most common in adults. Ultrasonography is the modality of choice for the initial evaluation of pediatric ovarian tumors. Determining the diagnosis based on imaging may prove difficult, and combining the imaging findings with the clinical scenario is very helpful in reaching a differential diagnosis during clinical practice. We will discuss the spectrum of ovarian neoplasms in the pediatric population and describe their clinical, pathologic, and imaging characteristics. A few unique entities related to ovarian tumors, such as growing teratoma syndrome, anti-N-methyl-D-aspartate receptor encephalitis, and hereditary ovarian tumor syndromes, are also discussed. In addition, we will review several entities that may mimic ovarian neoplasms as well as their distinct imaging features.

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

Background

To determine whether magnetic resonance (MR) imaging features combined with apparent diffusion coefficient (ADC) values could be used as a tool for categorizing ovarian epithelial cancer (OEC) and predicting survival, as well as correlating with laboratory tests (serum cancer antigen 125, serum CA-125) and tumor proliferative index (Ki-67 expression).

Methods and materials

MRI examination was undertaken before invasive procedures. MRI features were interpreted and recorded on the picture archive communication system (PACS). ADC measurements were manually performed on post-process workstation. Clinical characteristics were individually retrieved and recorded through the hospital information system (HIS). Cox hazard model was used to estimate the effects of both clinical and MRI features on overall survival.

Results

Both clinical and MRI features differed significantly between Type I and Type II cancer groups (p < 0.05). The mean ADC value was inversely correlated with Ki-67 expression in Type I cancer (ρ = − 0.14, p < 0.05). A higher mean ADC value was more likely to suggest Type I ovarian cancer (Odds Ratio (OR) = 16.80, p < 0.01). Old age and an advanced International Federation of Gynecology and Obstetrics (FIGO) stage were significantly related to Type II ovarian cancer (OR = 0.22/0.02, p < 0.05). An advanced FIGO stage, solid components, and old age were significantly associated with poor survival (Hazard Ratio (HR) = 23.54/3.69/2.46, p < 0.05). Clear cell cancer type had a poorer survival than any other pathological subtypes of ovarian cancer (HR = 13.6, p < 0.01).

Conclusions

MR imaging features combined with ADC value are helpful in categorizing OEC. ADC values can reflect tumor proliferative ability. A solid mass may predict poor prognosis for OEC patients.

Electronic supplementary material

The online version of this article (10.1186/s13048-019-0534-0) contains supplementary material, which is available to authorized users.