MRI for discriminating metastatic ovarian tumors from primary epithelial ovarian cancers

Mimics of primary ovarian cancer and primary peritoneal carcinomatosis - A pictorial review

Numerous conditions can mimic ovarian malignancy. Identifying the origin of a pelvic mass or disseminated peritoneal abnormality on imaging is important to ensure that the patient receives optimal management by the appropriate clinical team. Ovarian cancer mimics include infections and other neoplastic processes, for example, actinomycosis, lymphoma, and sarcoma. We will illustrate intraperitoneal and extraperitoneal ovarian and non-ovarian mimics. Primary peritoneal carcinomatosis mimics include processes such as deep infiltrating endometriosis and rare causes such as gliomatosis peritonei and diffuse peritoneal leiomyomatosis. We aim to illustrate the multimodality key imaging appearances of common and rarer types of mimics.

Multiparametric MRI-based radiomics nomogram for differentiation of primary mucinous ovarian cancer from metastatic ovarian cancer

OBJECTIVE

To develop a multiparametric magnetic resonance imaging (mpMRI)-based radiomics nomogram and evaluate its performance in differentiating primary mucinous ovarian cancer (PMOC) from metastatic ovarian cancer (MOC).

METHODS

A total of 194 patients with PMOC (n = 72) and MOC (n = 122) confirmed by histology were randomly divided into the primary cohort (n = 137) and validation cohort (n = 57). Radiomics features were extracted from axial fat-saturated T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences of each lesion. The effective features were selected by minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression to develop a radiomics model. Combined with clinical features, multivariate logistic regression analysis was employed to develop a radiomics nomogram. The efficiency of nomogram was evaluated using the receiver operating characteristic (ROC) curve analysis and compared using DeLong test. Finally, the goodness of fit and clinical benefit of nomogram were assessed by calibration curves and decision curve analysis, respectively.

RESULTS

The radiomics nomogram, by combining the mpMRI radiomics features with clinical features, yielded area under the curve (AUC) values of 0.931 and 0.934 in the primary and validation cohorts, respectively. The predictive performance of the radiomics nomogram was significantly superior to the radiomics model (0.931 vs. 0.870, P = 0.004; 0.934 vs. 0.844, P = 0.032), the clinical model (0.931 vs. 0.858, P = 0.005; 0.934 vs. 0.847, P = 0.030), and radiologists (all P < 0.05) in the primary and validation cohorts, respectively. The decision curve analysis revealed that the nomogram could provide higher net benefit to patients.

CONCLUSION

The mpMRI-based radiomics nomogram exhibited notable predictive performance in differentiating PMOC from MOC, emerging as a non-invasive preoperative imaging approach.

Unravelling Peritoneal Carcinomatosis Using Cross-Sectional Imaging Modalities

Peritoneal carcinomatosis (PC) refers to malignant epithelial cells that spread to the peritoneum, principally from abdominal malignancies. Until recently, PC prognosis has been considered ill-fated, with palliative therapies serving as the only treatment option. New locoregional treatments are changing the outcome of PC, and imaging modalities have a critical role in early diagnosis and disease staging, determining treatment decision making strategies. The aim of this review is to provide a practical approach for detecting and characterizing peritoneal deposits in cross-sectional imaging modalities, taking into account their appearances, including the secondary complications, the anatomical characteristics of the peritoneal cavity, together with the differential diagnosis with other benign and malignant peritoneal conditions. Among the cross-sectional imaging modalities, computed tomography (CT) is widely available and fast; however, magnetic resonance (MR) performs better in terms of sensitivity (92% vs. 68%), due to its higher contrast resolution. The appearance of peritoneal deposits on CT and MR mainly depends on the primary tumour histology; in case of unknown primary tumour (3-5% of cases), their behaviour at imaging may provide insights into the tumour origin. The timepoint of tumour evolution, previous or ongoing treatments, and the peritoneal spaces in which they occur also play an important role in determining the appearance of peritoneal deposits. Thus, knowledge of peritoneal anatomy and fluid circulation is essential in the detection and characterisation of peritoneal deposits. Several benign and malignant conditions show similar imaging features that overlap those of PC, making differential diagnosis challenging. Knowledge of peritoneal anatomy and primary tumour histology is crucial, but one must also consider clinical history, laboratory findings, and previous imaging examinations to achieve a correct diagnosis. In conclusion, to correctly diagnose PC in cross-sectional imaging modalities, knowledge of peritoneal anatomy and peritoneal fluid flow characteristics are mandatory. Peritoneal deposit features reflect the primary tumour characteristics, and this specificity may be helpful in its identification when it is unknown. Moreover, several benign and malignant peritoneal conditions may mimic PC, which need to be considered even in oncologic patients.

Manifestations of Ovarian Cancer in Relation to Other Pelvic Diseases by MRI

Imaging plays a pivotal role in the diagnostic approach of women with suspected ovarian cancer. MRI is widely used for preoperative characterization and risk stratification of adnexal masses. While epithelial ovarian cancer (EOC) has typical findings on MRI; there are several benign and malignant pelvic conditions that may mimic its appearance on imaging. Knowledge of the origin and imaging characteristics of a pelvic mass will help radiologists diagnose ovarian cancer promptly and accurately. Finally, in special subgroups, including adolescents and gravid population, the prevalence of various ovarian tumors differs from that of the general population and there are conditions which uniquely manifest during these periods of life.

Application of enhanced computed tomography-based radiomics nomogram analysis to differentiate metastatic ovarian tumors from epithelial ovarian tumors

OBJECTIVE

To investigate the value of nomogram analysis based on conventional features and radiomics features of computed tomography (CT) venous phase to differentiate metastatic ovarian tumors (MOTs) from epithelial ovarian tumors (EOTs).

METHODS

A dataset involving 286 patients pathologically confirmed with EOTs (training cohort: 133 cases, validation cohort: 68 cases) and MOTs (training cohort: 54 cases, validation cohort: 31 cases) is assembled in this study. Radiomics features are extracted from the venous phase of CT images. Logistic regression is employed to build models based on conventional features (model 1), radiomics features (model 2), and the combination of model 1 and model 2 (model 3). Diagnostic performance is assessed and compared. Additionally, a nomogram is plotted for model 3, and decision curve analysis is applied for clinical use.

RESULTS

Age, abdominal metastasis, para-aortic lymph node metastasis, location, and septation are chosen to build Model 1. Ten optimal radiomics features are ultimately selected and radiomics score (rad-score) is calculated to build Model 2. Nomogram score is calculated to build model 3 that shows optimal diagnostic performance in both the training (AUC = 0.952) and validation cohorts (AUC = 0.720), followed by model 1 (AUC = 0.872 for training cohort and AUC = 0.709 for validation cohort) and model 2 (AUC = 0.833 for training cohort and AUC = 0.620 for validation cohort). Additionally, Model 3 achieves accuracy, sensitivity, and specificity of 0.893, 0.880, and 0.926 in the training cohort and 0.737, 0.853, and 0.613 in the validation cohort.

CONCLUSION

Model 3 demonstrates the best diagnostic performance for preoperative differentiation of MOTs from EOTs. Thus, nomogram analysis based on Model 3 may be used as a biomarker to differentiate MOTs from EOTs.

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.

Multimodality Imaging Approach to Ovarian Neoplasms with Pathologic Correlation

Ovarian neoplasms can be categorized on the basis of histopathologic features into epithelial surface cell tumors, germ cell tumors, sex cord-stromal tumors, and metastases. While their imaging appearance is often nonspecific, it closely parallels the gross pathologic appearance, and radiologic-pathologic correlation is helpful to aid in a deeper understanding of the subtypes. Epithelial cell neoplasms are the most common category, and they can be benign, borderline, or malignant. Specific subtypes include serous (most common), mucinous, seromucinous, endometrioid, clear cell, Brenner, and undifferentiated. High-grade serous cystadenocarcinoma accounts for the majority of malignant ovarian tumors and the most ovarian cancer deaths. While serous neoplasms are often unilocular and bilateral, mucinous neoplasms are larger, unilateral, and multilocular. Solid components, thickened septa, and papillary projections, particularly with vascularity, indicate borderline or malignant varieties. Endometrioid and clear cell carcinomas can arise within endometriomas. Fibrous tumors (cystadenofibroma, adenofibroma, fibroma or fibrothecoma, and Brenner tumors) demonstrate low T2-weighted signal intensity of their solid components, while teratomas contain lipid. The nonspecific imaging appearance of additional malignant ovarian germ cell tumors can be narrowed with tumor marker profiles. Sex cord-stromal tumors are often solid, and secondary signs from their hormonal secretion can be a clue to their diagnosis. The authors review the anatomy of the ovary and distal fallopian tube, the proposed origins of the histologic subtypes of tumors, the clinical features and epidemiology of ovarian neoplasms, and the applications of US, CT, and MRI in imaging ovarian neoplasms. The main focus is on the radiologic and pathologic features of the multiple ovarian neoplasm subtypes. An algorithmic approach to the diagnosis of ovarian neoplasms is presented. Online supplemental material is available for this article. ^©RSNA, 2020.

Krukenberg Tumors: Update on Imaging and Clinical Features

OBJECTIVE. The purpose of this case-based article is to discuss the pathophysiologic findings, common pathways of spread, and imaging features associated with Krukenberg tumors. CONCLUSION. Not all ovarian metastases are Krukenberg tumors. Krukenberg tumors are the most common subtype of ovarian metastases, and they are histologically characterized by signet ring cell mucinous features. Common primary tumor sites include the stomach or colon. Although often difficult, distinguishing between Krukenberg tumors and primary ovarian malignancy on imaging is important because of management and prognostic implications.

Bilateral ovarian metastasis of clear-cell renal cell carcinoma: A case report

Renal cell carcinoma (RCC) accounts for 2-3% of all adult malignancies. Clear-cell type RCC is the most common type, accounting for approximately 75% of all renal cancer cases. The most common sites of metastasis include the lung, bone, and liver. Ovarian metastasis of RCC is an exceptionally rare occurrence with only 41 cases reported in the literature with only 11 of these cases involving the bilateral ovaries. Here we present a case of clear-cell RCC arising from the left kidney with metastatic involvement of bilateral ovaries in a 48-year-old woman.

Ovarian solid tumors: MR imaging features with radiologic-pathologic correlation

Ovarian solid tumors have variable histological types including benign and malignant tumors. In addition, non-neoplastic lesions sometimes show a tumor-like appearance. It is important to differentiate benign from malignant tumors. In general, low signal intensity (SI) on T2-weighted imaging (T2WI), low SI on diffusion-weighted imaging (DWI), and gradual increased pattern on dynamic contrast-enhanced magnetic resonance (MR) imaging are known to be suggestive of a benign tumor. Conversely, there are some cases in which these rules do not apply. We should, therefore, strive for a greater understanding of these exceptional cases. Several tumors show characteristic findings on MR imaging reflecting pathologic features, which leads to the correct diagnosis. Additionally, MR imaging provides important information other than the nature of tumors, such as secondary uterine changes. Furthermore, clinical findings and laboratory examination data also help in determining the correct diagnosis.

Differentiation between ovarian metastasis from colorectal carcinoma and primary ovarian carcinoma: Evaluation of tumour markers and "mille-feuille sign" on computed tomography/magnetic resonance imaging

PURPOSE

The purpose of this retrospective study was to evaluate the usefulness of serum tumour markers and morphological characteristics in CT/MRI to differentiate between ovarian metastases from colorectal carcinomas (OMCRC) and primary ovarian carcinomas (POC).

METHOD

Preoperative radiological images of 41 OMCRCs from 27 patients (mean age ± SD: 52.2 ± 10.7 years) and 46 POCs from 36 patients (52.1 ± 12.7 years) were included. Three blinded gynecological radiologists classified tumour morphology into 'mille-feuille sign', 'solid and cystic', 'multicystic without nodules', and 'multicystic with nodules' groups and analysed using Fisher's exact test. Serum carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), and carbohydrate antigen 19-9 levels were compared by Wilcoxon rank-sum test.

RESULTS

'Mille-feuille sign' indicated OMCRC (OMCRC: 8/41, POC: 1/46, specificity = 0.98, p = 0.011) and had excellent interobserver agreement (Fleiss's kappa value = 0.96). 'Solid and cystic' indicated POC (18/41 vs 41/45, p < 0.001) and 'multicystic without nodules' indicated OMCRC (8/41 vs 2/46, p = 0.041). There was no significant difference in 'multicystic with nodules'. CA125 levels were higher in POCs (292.5 U/mL vs. 41.0 U/mL, p = 0.003). CEA levels were higher in OMCRCs (24.5 ng/mL vs 2 ng/mL, p < 0.001). CEA (< 6.3 ng/mL) AND (CA125 (≥87.0 U/mL) OR 'solid and cystic') indicated POC with high accuracy (3/41 vs 44/46, accuracy = 0.94, p < 0.001).

CONCLUSIONS

Our new method with morphological classification and tumour markers were useful for differentiating the two tumours. In particular, the 'mille-feuille sign' frequently indicated OMCRC with high specificity and excellent interobserver agreement.

Primary appendiceal mucinous neoplasm: Gynecological manifestations, management, and prognosis

OBJECTIVE

The aim of this study was to investigate the clinicopathological characteristics, management, and survival of patients with appendiceal mucinous neoplasm misdiagnosed as ovarian tumor and initially treated by a gynecological oncologists.

METHODS

We retrospectively reviewed data for 71 patients identified from January 1, 2006 to September 31, 2018 with a final pathological diagnosis of appendiceal mucinous neoplasm. All patients were diagnosed as having ovarian tumor preoperatively and initially treated at Department of Gynecological Oncology.

RESULTS

The median patients' age was 61 years, and the median peritoneal cancer index (PCI) was 16, with a complete cytoreduction score (CCR0/1) achieved in 46.47% of patients. Two patients received hyperthermic intraperitoneal chemotherapy (HIPEC). Estimated 5-year and 10-year survival was 76.7% and 53.4%, respectively. PCI and lactate dehydrogenase (LDH) levels were identified as independent predictors of poor overall survival.

CONCLUSIONS

Because of the high rate of ovarian metastases and the difficulty diagnosing appendiceal mucinous neoplasm preoperatively, this condition should be highlighted in gynecological oncology. Because initial surgical intervention is often performed by gynecological oncologists, updating the management guidelines for appendiceal mucinous tumor is needed because the rates of CCR0/1 resection and HIPEC are much lower in patients who are misdiagnosed.

Bilateral Ovarian Tumors on MRI: How Should We Differentiate the Lesions?

Background

We investigated the distinguishing pathological features of bilateral ovarian tumors using magnetic resonance (MR) imaging.

Methods

Eighty-six patients with bilateral ovarian tumors on MR imaging were evaluated. The pathological diagnosis was investigated, and the results were subjected to statistical analysis using Mann-Whitney U test, Fisher's exact test, Chi-squared test and receiver operating characteristic (ROC) curve to determine the features useful for the differentiation of distinct types of lesions.

Results

The diagnosis of bilateral ovarian tumors was confirmed in eighty-one patients and the majority of the lesions were further classified into serous carcinoma (n = 36), mature teratoma (n = 20) and metastasis (n = 12). We assessed the existence of factors useful for the MR imaging differentiation between metastasis and serous carcinoma or primary malignant ovarian tumors. Cancer antigen (CA) 125 serum level and maximum tumor diameter were significantly different between metastasis and serous carcinoma and similarly, between metastasis and primary malignant ovarian tumors. MR imaging morphology, ascites and peritoneal implants did not show any significant difference between the different types of lesions.

Conclusion

Within our patient cohort, most bilateral ovarian tumor lesions were determined to be serous carcinoma, mature teratoma or metastasis. CA 125 serum level and maximum tumor diameter are useful markers for the differentiation between metastasis and serous carcinoma or primary malignant ovarian tumors.