Imaging in secondary tumors of the ovary

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.

The Peritoneum: Anatomy, Pathologic Findings, and Patterns of Disease Spread

Disease spread in the abdomen and pelvis generally occurs in a predictable pattern in relation to anatomic landmarks and fascial planes. Anatomically, the abdominopelvic cavity is subdivided into several smaller spaces or compartments by key ligaments and fascial planes. The abdominal cavity has been traditionally divided into peritoneal, retroperitoneal, and pelvic extraperitoneal spaces. Recently, more clinically relevant classifications have evolved. Many pathologic conditions affect the abdominal cavity, including traumatic, inflammatory, infectious, and neoplastic processes. These abnormalities can extend beyond their sites of origin through various pathways. Identifying the origin of a disease process is the first step in formulating a differential diagnosis and ultimately reaching a final diagnosis. Pathologic conditions differ in terms of pathways of disease spread. For example, simple fluid tracks along fascial planes, respecting anatomic boundaries, while fluid from acute necrotizing pancreatitis can destroy fascial planes, resulting in transfascial spread without regard for anatomic landmarks. Furthermore, neoplastic processes can spread through multiple pathways, with a propensity for spread to noncontiguous sites. When the origin of a disease process is not readily apparent, recognizing the spread pattern can allow the radiologist to work backward and ultimately arrive at the site or source of pathogenesis. As such, a cohesive understanding of the peritoneal anatomy, the typical organ or site of origin for a disease process, and the corresponding pattern of disease spread is critical not only for initial diagnosis but also for establishing a road map for staging, anticipating further disease spread, guiding search patterns and report checklists, determining prognosis, and tailoring appropriate follow-up imaging studies. ©RSNA, 2024 Supplemental material is available for this article.

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.

Krukenberg Tumors in Young Women: Computed Tomography and Magnetic Resonance Imaging Diagnosis

INTRODUCTION

The purpose of this report was to present the computed tomography (CT) and magnetic resonance imaging (MRI) features of Krukenberg tumors and to review the pertinent clinical data about the rising incidence of this malignancy among young women.

MATERIAL AND METHODS

This series included 8 women who ranged in age from 24 to 44 years (mean, 36.3 years). They were diagnosed to have Krukenberg tumors during a 5-year period (2011-2016). All patients were evaluated by abdominal CT and pelvic or transvaginal sonography. Five of them also had MRI of the abdomen, and 3 had positron emission tomography scans.

RESULTS

The primary cancer was located in the stomach of 7 patients and in the colon in 1. The initial presentation was due to large pelvic mass and abdominal distention by ascites in 3 patients, gastrointestinal symptoms in 4, and small bowel obstruction by carcinoma of the ascending colon in 1 woman. Ovarian metastases were demonstrated on the initial imaging examination of 5 patients and developed as metachronous lesion during follow-up in 3 cases. Six women died 3 to 23 months (mean, 11 months) after the diagnosis. One has survived for 6 years after extensive surgery, and 1 was lost to follow-up.

CONCLUSIONS

Krukenberg tumors are being diagnosed with an increasing frequency because of well-documented rising incidence of gastric and colorectal carcinomas among young women. Therefore, those presenting with gastrointestinal cancer should have careful imaging of their ovaries for possible metastases, and conversely, the clinical or sonographic detection of bilateral ovarian masses would require evaluation by CT or MRI of the abdomen in search for a potential primary gastrointestinal cancer. This report highlights the CT and MRI features of this neoplastic process and reviews the current concepts about its development and management.

Breast Cancer Cell Type and Biomechanical Properties of Decellularized Mouse Organs Drives Tumor Cell Colonization

Tissue engineering has emerged as an indispensable tool for the reconstruction of organ-specific environments. Organ-derived extracellular matrices (ECM) and, especially, decellularized tissues (DCL) are recognized as the most successful biomaterials in regenerative medicine, as DCL preserves the most essential organ-specific ECM properties such as composition alongside biomechanics characterized by stiffness and porosity. Expansion of the DCL technology to cancer biology research, drug development, and nanomedicine is pending refinement of the existing DCL protocols whose reproducibility remains sub-optimal varying from organ to organ. We introduce a facile decellularization protocol universally applicable to murine organs, including liver, lungs, spleen, kidneys, and ovaries, with demonstrated robustness, reproducibility, high purification from cell debris, and architecture preservation, as confirmed by the histological and SEM analysis. The biomechanical properties of as-produced DCL organs expressed in terms of the local and total stiffness were measured using our facile methodology and were found well preserved in comparison with the intact organs. To demonstrate the utility of the developed DCL model to cancer research, we engineered three-dimensional tissue constructs by recellularization representative decellularized organs and collagenous hydrogel with human breast cancer cells of pronounced mesenchymal (MDA-MB-231) or epithelial (SKBR-3) phenotypes. The biomechanical properties of the DCL organs were found pivotal to determining the cancer cell fate and progression. Our histological and scanning electron microscopy (SEM) study revealed that the larger the ECM mean pore size and the smaller the total stiffness (as in lung and ovary), the more proliferative and invasive the mesenchymal cells became. At the same time, the low local stiffness ECMs (ranged 2.8-3.6 kPa) did support the epithelial-like SKBR-3 cells' viability (as in lung and spleen), while stiff ECMs did not. The total and local stiffness of the collagenous hydrogel was measured too low to sustain the proliferative potential of both cell lines. The observed cell proliferation patterns were easily interpretable in terms of the ECM biomechanical properties, such as binding sites, embedment facilities, and migration space. As such, our three-dimensional tissue engineering model is scalable and adaptable for pharmacological testing and cancer biology research of metastatic and primary tumors, including early metastatic colonization in native organ-specific ECM.

Assessing synchronous ovarian metastasis in gastric cancer patients using a clinical-radiomics nomogram based on baseline abdominal contrast-enhanced CT: a two-center study

BACKGROUND

To build and validate a radiomics nomogram based on preoperative CT scans and clinical data for detecting synchronous ovarian metastasis (SOM) in female gastric cancer (GC) cases.

METHODS

Pathologically confirmed GC cases in 2 cohorts were retrospectively enrolled. All cases had presurgical abdominal contrast-enhanced CT and pelvis contrast-enhanced MRI and pathological examinations for any suspicious ovarian lesions detected by MRI. Cohort 1 cases (n = 101) were included as the training set. Radiomics features were obtained to develop a radscore. A nomogram combining the radscore and clinical factors was built to detect SOM. The bootstrap method was carried out in cohort 1 as internal validation. External validation was carried out in cohort 2 (n = 46). Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and the confusion matrix were utilized to assess the performances of the radscore, nomogram and subjective evaluation model.

RESULTS

The nomogram, which combined age and the radscore, displayed a higher AUC than the radscore and subjective evaluation (0.910 vs 0.827 vs 0.773) in the training cohort. In the external validation cohort, the nomogram also had a higher AUC than the radscore and subjective evaluation (0.850 vs 0.790 vs 0.675). DCA and the confusion matrix confirmed the nomogram was superior to the radscore in both cohorts.

CONCLUSIONS

This pilot study showed that a nomogram model combining the radscore and clinical characteristics is useful in detecting SOM in female GC cases. It may be applied to improve clinical treatment and is superior to subjective evaluation or the radscore alone.

Real-time contrast-enhanced ultrasound-guided percutaneous biopsy in the diagnosis of ovarian metastasis of gallbladder carcinoma: a case report

BACKGROUND

Multiple-organ primary tumors can invade the ovary through lymphatic and hematogenous routes, presenting as ovarian Krukenberg tumors, but these rarely originate from the gallbladder. Krukenberg tumors can present similar to primary ovarian tumors; however, their treatments are completely different.

PATIENT CONCERNS

A 62-year-old Chinese woman presented with abdominal distension for six months and weight loss of five kilograms for two months.

DIAGNOSES

Based on multiple imaging examinations, the patient was preliminarily diagnosed with a malignant tumor of unknown origin with multiple metastases (omentum). To identify the origin of the malignancy, the patient underwent real-time contrast-enhanced ultrasound-guided percutaneous biopsy. The results revealed a perihepatic hypoechoic lesion and right adnexal mass that were both metastatic adenocarcinomas from the gallbladder.

INTERVENTIONS

The patient initially received chemotherapy with gemcitabine and cisplatin instead of surgery. However, the tumor increased in size on re-examination after two cycles, so the treatment was shifted to a combination regimen with durvalumab for six cycles.

OUTCOMES

The treatment proceeded smoothly, with no recurrence or obvious progression of the cancer during follow-up.

CONCLUSIONS

Differentiating between primary and metastatic ovarian tumors is important. Early diagnosis and effective treatment options are essential for patient survival. CEUS-guided percutaneous biopsy is a valuable procedure for patients with multiple metastases who cannot tolerate surgery.

Diffusion-weighted magnetic resonance sequence and CA125/CEA ratio can be used as add-on tools to ultrasound for the differentiation of ovarian from non-ovarian pelvic masses

OBJECTIVE

To provide a straightforward approach to the sequential use of ultrasound (US), magnetic resonance (MR) and serum biomarkers in order to differentiate the origin of pelvic masses, making the most efficient use of these diagnostic resources.

STUDY DESIGN

This is a cross-sectional study with 159 patients (133 with ovarian and 26 with non-ovarian tumors) who underwent surgery/biopsy for an adnexal mass. Preoperative CA125 and CEA serum measurements were obtained and a pelvic/abdominal ultrasound was performed. Preoperative pelvic MR studies were performed for all patients. Morphological and advanced MR sequences were obtained. Using a recursive partitioning algorithm to predict tumor origin, we devised a roadmap to determine the probability of non-ovarian origin using only statistically significant US, laboratory and MR parameters.

RESULTS

Upfront US classification as ovarian versus non-ovarian and CA125/CEA ratio were significantly associated with non-ovarian tumors. Signal diffusion (absent/low versus high) was the only MR parameter significantly associated with non-ovarian tumors. When upfront US designated a tumor as being of ovarian origin, further MR signal diffusion and CA125/CEA ratio were corrected nearly all US errors: patients with MR signal diffusion low/absent and those with signal high but CA125/CEA ratio ≥25 had an extremely low chance (<1%) of being of non-ovarian origin. However, for women whose ovarian tumors were incorrectly rendered as non-ovarian by upfront US, neither MR nor CA125/CEA ratio were able to determine tumor origin precisely.

CONCLUSION

MR signal diffusion is an extremely useful MR parameter to help determine adnexal mass origin when US and laboratory findings are inconclusive.

Imaging findings of ovarian metastasis of primary renal cell carcinoma: A case report and literature review

A 62-year-old woman presented with a tumor in the right kidney. A right partial nephrectomy was performed, and the tumor was diagnosed as clear cell renal cell carcinoma (RCC) on histopathological examination. A right ovarian tumor was detected on follow-up computed tomography (CT) 5 years after partial nephrectomy and pathology proved RCC metastasis. RCC rarely metastasizes to the ovaries. There is limited information on the radiological features of ovarian metastasis in RCC. In this case report, we presented the CT and magnetic resonance images of ovarian metastasis of RCC. In addition, we also presented a literature review with special emphasis on the imaging features of ovarian metastasis of RCC.

Diagnostic Accuracy of Whole-Body Computed Tomography for Incidental Ovarian Tumors in Patients with Prior Breast Cancer

Whole-body computed tomography (WBCT) serves as the first-line imaging modality for breast cancer follow-up. To investigate the imaging characteristics and diagnostic accuracy of WBCT for incidental ovarian tumors in patients with prior breast cancer, we retrospectively reviewed a consecutive cohort of 13,845 patients with breast cancer, of whom 149 had pathologically-proven ovarian lesions. We excluded patients with ovarian diagnosis before breast cancer, CT scan not including ovary, CT-pathology interval >30 days, and severe CT artifact. Among our 60 breast cancer patients (median age, 46 years) with pathologically proven ovarian lesions, 49 patients had benign diseases, seven had primary ovarian cancer and four had ovarian metastasis from breast cancer. The histologic types of breast cancer with ovarian metastases included invasive ductal carcinoma, lobular carcinoma and angiosarcoma. Cystic ovarian lesions identified on WBCT during the breast cancer follow-up are more likely to be benign, while solid-cystic lesions are likely to be primary ovarian cancers, and solid lesions may indicate ovarian metastasis. The diagnostic accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve of WBCT were 98.3%, 100.0%, 98.0%, and 0.99 (malignant vs. benign); 90.0%, 100.0%, 85.7%, and 0.93 (metastasis vs. primary ovarian cancer), respectively. The only false positive solid lesion was a Sertoli–Leydig tumor. In conclusion, WBCT may help diagnose incidental ovarian tumors in patients with prior breast cancers and guide disease management.

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.

The diagnosis and outcome of Krukenberg tumors

Background

Accurate diagnostic tools are crucial to distinguish patients with Krukenberg tumors from those with ovarian cancers before decision on initial management. To address this unmet need, we aimed to evaluate the diagnostic utility of clinical, biochemical, and radiographic factors in this patient population.

Methods

Patients with Krukenberg tumors or primary ovarian cancers were retrospectively identified from institutional cancer registry. Kaplan-Meier method and Cox proportional hazards models were used for survival analysis. Logistic regression evaluated clinical, biochemical, and radiographic factors; residual deep neural network model evaluated features in computed tomography images as predictors to distinguish Krukenberg tumors from ovarian cancers. Model performance was summarized as accuracy and area under the receiver operating characteristic curve (AUC).

Results

This study included 214 patients with Krukenberg tumors with median age of 52 years. Among 104 (48.6%) patients with colorectal cancer, those who received palliative surgery had significantly higher median overall survival (48.1 versus 30.6 months, P=0.015) and progression-free survival (22.2 versus 6.7 months, P<0.001) than those with medical management only. The accuracy of radiology reports to make either diagnosis of Krukenberg tumors or primary ovarian cancers was 60.7%. In contrast, multivariable logistic regression model with age [odds ratio (OR) 2.98, P<0.001], carbohydrate antigen 125 (OR 1.57, P=0.004), and carcinoembryonic antigen (OR 0.03, P=0.031) had 87.5% [95% confidence interval (CI): 75.0-100.0%] accuracy with AUC 0.96 (95% CI: 0.87-1.00). The neural network model had 62.8% (95% CI: 51.8-74.5%) accuracy with AUC of 0.61 (95% CI: 0.53-0.72).

Conclusions

We developed a diagnostic model with clinical and biochemical features to distinguish Krukenberg tumors from primary ovarian cancers with promising accuracy.

Gastric linitis plastica due to signet-ring cell carcinoma with Krukenberg tumors diagnosed by endoscopic ultrasound-guided fine-needle aspiration

Gastric linitis plastica is a rare condition characterized by the stiffening and thickening of the gastric wall, presenting a "leather bottle" appearance. It is generally associated with infiltration of poorly differentiated gastric cancer cells, but similar findings can arise due to metastases from other cancers and benign inflammatory processes. Despite advances in imaging modalities, pathological evaluation is required for diagnosis. As endoscopic mucosal biopsies may not be diagnostic, endoscopic ultrasound-guided fine-needle aspiration of the thickened stomach wall presents a potential diagnostic option. We report a case which presented with bilateral ovarian tumors and was ultimately diagnosed as gastric signet-ring cell carcinoma with Krukenberg tumors by endoscopic ultrasound-guided fine-needle aspiration of the stomach wall. The preoperative diagnosis allowed the patient to avoid surgery and to start chemotherapy for gastric cancer. We also review the existing literature on endoscopic ultrasound-guided fine-needle aspiration in the context of gastric linitis plastica.

Novel classification of ovarian metastases originating from colorectal cancer by radiological imaging and macroscopic appearance

BACKGROUND

Diagnosis of secondary ovarian tumors originating from colorectal cancer has previously been based upon history of malignancy and radiological findings of bilateral masses with a "stained glass appearance." The purpose of this study was to perform a detailed investigation of the radiological and macroscopic features of ovarian metastases originating from colorectal cancer, which remain to be fully characterized.

METHODS

Study participants were 48 consecutive patients with ovarian metastases from colorectal cancer who underwent resection of ovarian tumors at the National Cancer Center Hospital between August 1998 and January 2019. Ovarian tumors were classified into subgroups using computed tomography (CT), magnetic resonance imaging (MRI), and macroscopic appearance.

RESULTS

CT/MRI findings and macroscopic appearance were classified into the following four types: type 1 (oval, homogeneous-solid) (n = 5); type 2 (heterogeneous-solid, small in size with multinodular surface) (n = 3); type 3 (solid-cystic, predominantly solid) (n = 18); and type 4 (cystic-solid, multilocular with solid components) (n = 22). Type 1 mimics Krukenberg tumors, type 2 mimics ovarian metastases from breast cancer, type 3 mimics primary ovarian endometrioid cancer, and type 4 mimics primary ovarian mucinous cancer, with a "stained glass appearance". Twenty-eight (58%) patients had bilateral metastases. Eleven patients (23%) underwent hysterectomy and/or pelvic lymph node dissection in addition to ovarian resection.

CONCLUSION

We introduced a novel classification system for ovarian metastases originating from colorectal cancer, which may be beneficial for assessing ovarian metastases from colorectal cancer and avoiding unnecessary surgery due to misdiagnosis of primary ovarian tumors.