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

Prediction of grading of ovarian endometrioid carcinoma using conventional MRI features

OBJECTIVE

The purpose of this study was to evaluate MRI findings of ovarian endometrioid carcinoma (OEC) as a predictor of histological grade.

MATERIALS AND METHODS

This study included 60 patients with histopathologically confirmed OEC (20, 30, and 10 with grades 1, 2, and 3, respectively). Clinical and MRI results were retrospectively reviewed. We compared the following parameters between the three grades: age, tumor markers, presence of uterine corpus cancer, bilaterality, configuration, peritoneal dissemination, abnormal ascites, signal intensities of cystic and solid components, tumor size, and apparent diffusion coefficient (ADC) values of solid components.

RESULTS

T1-hyperintense cysts were more common in grade 1 than in grades 2-3 OEC (80% vs. 60%, vs. 40%, p < 0.05). The signal intensity ratio between the cystic components with the largest solid component and muscle (1.49 vs. 1.08 vs. 0.98, p < 0.05) was higher in grade 1 than in grades 2-3 OEC. Necrosis within solid components was less common in grade 1 than in grades 2-3 OEC (31% vs. 68% vs. 88%, p < 0.05), and the ADC values of solid components were higher in grade 1 than in grades 2-3 OEC (1.10 vs. 0.99 vs. 0.79 × 10-3 mm2/sec, p < 0.05). There were no significant differences in other factors.

CONCLUSION

On T1-weighted images, grade 1 OEC showed a higher signal intensity in the cystic components than grades 2-3 OEC. Necrosis and lower ADC values were more frequently observed in grades 2-3 than in grade 1 OEC.

Two case reports of ovarian clear cell carcinoma: Atypical MRI findings with a renal corticomedullary contrast-like appearance

The MR images of the two cases of ovarian clear cell carcinomas (CCCs), presenting as entirely solid-masses, revealed a band-like peripheral area and a geographic central area: The peripheral area images resembled those of the renal cortex, while the central area images resembled those of the renal medulla. Compared to the iliac muscle, the band-like peripheral area appeared iso-hyperintense on T2-weighted imaging (T2WI), isointense on unenhanced T1-weighted imaging (T1WI), hyperintense on diffusion-weighted imaging (DWI), and hypo-isointense on an apparent diffusion coefficient (ADC) map, with gradual enhancement on dynamic contrast-enhanced T1WI. The geographic central area of the tumor appeared hyperintense on T2WI, hypointense on T1WI, hypointense on DWI, and hyperintense on an ADC map, with less enhancement on dynamic contrast-enhanced T1WI. Simple total hysterectomy, salpingo-oophorectomy, and partial omentectomy were performed in both cases. Both tumors were entirely solid, and histopathological analysis revealed cancer cell proliferation accompanied by an increase in stromal fibrous tissue in the peripheral area. Edematous stroma and fibrosis were predominantly observed with minimal cancer cells in the central area. The patients were diagnosed with ovarian CCC (pT1a). The mechanism behind the appearance of peripheral and central areas with renal corticomedullary contrast in such cases remains unknown, and MRI can provide valuable insights for the differential diagnosis of epithelial ovarian cancer.

Diagnostic Value of ADC in Distinguishing Endometrial Cancer from Atypical Endometrial Hyperplasia and Within Molecular Subtypes

Purpose

The study aimed to evaluate the effectiveness of using specific indicators, particularly the apparent diffusion coefficient (ADC), alone or in combination to differentiate endometrial cancer (EC) from atypical endometrial hyperplasia (AEH) and to explore non-invasive biomarkers for the molecular classification of EC.

Methods

A retrospective analysis was conducted on 300 EC and 126 AEH cases who had undergone preoperative magnetic resonance imaging, complete blood count, coagulation profile testing, and tumor biomarkers assessment. Postoperative molecular classification was conducted on 76 EC samples. Diagnostic values were assessed using receiver operating characteristic (ROC) analysis and binary logistic regression with forward selection to determine the optimal indicator combinations. Furthermore, this study evaluated the variability of parameters across EC molecular subtypes.

Results

The ADC effectively balanced sensitivity and specificity in differentiating EC from AEH. An optimal diagnostic model including age, fibrinogen, and ADC achieved the area under the curve (AUC) of 0.9143, with 84.67% sensitivity and 88.89% specificity. ADC values were found to be lower in EC cases that exhibited a higher Ki-67 index or a higher histological grade. Notably, the NSMP subtype presented significantly higher ADC values compared to the other three molecular subtypes. The p53abn subtype exhibited the highest prevalence of abnormal HE4 levels and patients aged ≥65 (both 6/12, 50%) yet normal CA125 and CA19-9 levels.

Conclusion

This retrospective study demonstrated that ADC, especially when combined with age and fibrinogen, is a valuable biomarker for distinguishing EC from AEH. In addition to indicating the Ki-67 index and histological grade, ADC values also serve as a promising tool for identifying the NSMP subtype within EC. Future studies should focus on multi-center, prospective studies with larger sample sizes to validate and refine the diagnostic value of ADC in differentiating EC from AEH, as well as in the molecular classification of EC.

Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study

PURPOSE

To retrospectively evaluate the diagnostic potential of magnetic resonance imaging (MRI)-based features and radiomics analysis (RA)-based features for discriminating ovarian clear cell carcinoma (CCC) from endometrioid carcinoma (EC).

MATERIALS AND METHODS

Thirty-five patients with 40 ECs and 42 patients with 43 CCCs who underwent pretherapeutic MRI examinations between 2011 and 2022 were enrolled. MRI-based features of the two groups were compared. RA-based features were extracted from the whole tumor volume on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (cT1WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation method was performed to select features. Logistic regression analysis was conducted to construct the discriminating models. Receiver operating characteristic curve (ROC) analyses were performed to predict CCC.

RESULTS

Four features with the highest absolute value of the LASSO algorithm were selected for the MRI-based, RA-based, and combined models: the ADC value, absence of thickening of the uterine endometrium, absence of peritoneal dissemination, and growth pattern of the solid component for the MRI-based model; Gray-Level Run Length Matrix (GLRLM) Long Run Low Gray-Level Emphasis (LRLGLE) on T2WI, spherical disproportion and Gray-Level Size Zone Matrix (GLSZM), Large Zone High Gray-Level Emphasis (LZHGE) on cT1WI, and GLSZM Normalized Gray-Level Nonuniformity (NGLN) on ADC map for the RA-based model; and the ADC value, spherical disproportion and GLSZM_LZHGE on cT1WI, and GLSZM_NGLN on ADC map for the combined model. Area under the ROC curves of those models were 0.895, 0.910, and 0.956. The diagnostic performance of the combined model was significantly superior (p = 0.02) to that of the MRI-based model. No significant differences were observed between the combined and RA-based models.

CONCLUSION

Conventional MRI-based analysis can effectively distinguish CCC from EC. The combination of RA-based features with MRI-based features may assist in differentiating between the two diseases.

MR Relaxometry for Discriminating Malignant Ovarian Cystic Tumors: A Prospective Multicenter Cohort Study

BACKGROUND

Endometriosis-associated ovarian cancer (EAOC) is a well-known type of cancer that arises from ovarian endometrioma (OE). OE contains iron-rich fluid in its cysts due to repeated hemorrhages in the ovaries. However, distinguishing between benign and malignant tumors can be challenging. We conducted a retrospective study on magnetic resonance (MR) relaxometry of cyst fluid to distinguish EAOC from OE and reported that this method showed good accuracy. The purpose of this study is to evaluate the accuracy of a non-invasive method in re-evaluating pre-surgical diagnosis of malignancy by a prospective multicenter cohort study.

METHODS

After the standard diagnosis process, the R2 values were obtained using a 3T system. Data on the patients were then collected through the Case Report Form (CRF). Between December 2018 and March 2023, six hospitals enrolled 109 patients. Out of these, 81 patients met the criteria required for the study.

RESULTS

The R2 values calculated using MR relaxometry showed good discriminating ability with a cut-off of 15.74 (sensitivity 80.6%, specificity 75.0%, AUC = 0.750, p < 0.001) when considering atypical or borderline tumors as EAOC. When atypical and borderline cases were grouped as OE, EAOC could be distinguished with a cut-off of 16.87 (sensitivity 87.0%, specificity 61.1%).

CONCLUSIONS

MR relaxometry has proven to be an effective tool for discriminating EAOC from OE. Regular use of this method is expected to provide significant insights for clinical practice.

Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma

PURPOSE

To investigate the feasibility of whole-tumor apparent diffusion coefficient (ADC) histogram analysis for improving the differentiation of endometriosis-related tumors: seromucinous borderline tumor (SMBT), clear cell carcinoma (CCC) and endometrioid carcinoma (EC).

METHODS

Clinical features, solid component ADC (ADC_SC) and whole-tumor ADC histogram-derived parameters (volume, the ADC_mean, 10th, 50th and 90th percentile ADCs, inhomogeneity, skewness, kurtosis and entropy) were compared among 22 SMBTs, 42 CCCs and 21 ECs. Statistical analyses were performed using chi-square test, one-way ANOVA or Kruskal-Wallis test, and receiver operating characteristic curves.

RESULTS

A significantly higher ADC_SC and smaller volume were associated with SMBT than with CCC/EC. The ADC_mean was significantly higher in CCC than in EC. The 10th percentile ADC was significantly lower in EC than in SMBT/CCC. The 50th and 90th percentile ADCs were significantly higher in CCC than in SMBT/EC. For differentiating SMBT from CCC, AUCs of the ADC_SC, volume, and 50th and 90th percentile ADCs were 0.97, 0.86, 0.72 and 0.81, respectively. For differentiating SMBT from EC, AUCs of the ADC_SC, volume and 10th percentile ADC were 0.97, 0.71 and 0.72, respectively. For differentiating CCC from EC, AUCs of the ADC_mean and 10th, 50th and 90th percentile ADCs were 0.79, 0.72, 0.81 and 0.85, respectively.

CONCLUSION

Whole-tumor ADC histogram analysis was valuable for differentiating endometriosis-related tumors, and the 90th percentile ADC was optimal in differentiating CCC from EC.

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.

Magnetic resonance imaging findings of cystic ovarian tumors: major differential diagnoses in five types frequently encountered in daily clinical practice

There are many types of ovarian tumors, and these different types often form cystic masses with a similar appearance, which can make their differentiation difficult. However, with the exclusion of rare ovarian tumors, the number of ovarian tumors encountered in daily practice is somewhat fixed. It goes without saying that magnetic resonance imaging (MRI) is useful for differentiating ovarian tumors. In this review, we summarize the differential diagnoses for each of the five types of MRI findings commonly encountered in daily practice. First, unilocular cystic masses without mural nodules/solid components include benign lesions such as serous cystadenoma, functional cysts, surface epithelial inclusion cysts, paratubal cysts, and endometriosis. Second, multilocular cystic ovarian lesions include mucinous tumors and ovarian metastases. It should be noted that mucinous tumors may be diagnosed as borderline or carcinoma, even if no solid component is observed. Third, cystic lesions with mural nodules that are unrelated to endometriosis include serous borderline tumor and serous carcinoma. Cystic lesions with solid components are more likely to be malignant, but some may be diagnosed as benign. Fourth, ovarian tumors deriving from endometriosis include seromucinous borderline tumors, endometrioid carcinoma, and clear cell carcinoma. These tumors sometimes need to be differentiated from serous tumors. Finally, cystic lesions with lipid contents include teratoma-related tumors. In mature cystic teratoma, mural nodules (called "Rokitansky protuberance" or "dermoid nipple") are sometimes seen, but they do not suggest malignancy. Some of these lesions can be diagnosed accurately by considering their characteristic imaging findings, their changes over time, MRI findings other than those of the primary lesion, and information from other modalities such as tumor markers. To ensure the optimal treatment for ovarian tumors, it is important to estimate the histological type as well as to diagnose whether a lesion is benign or malignant.

A comparative analysis of MRI findings in endometrial cancer: differentiation between endometrioid adenocarcinoma, serous carcinoma, and clear cell carcinoma

OBJECTIVE

To assess the magnetic resonance imaging (MRI) findings of endometrial cancers and to reveal the differences between endometrioid carcinoma (EC), serous carcinoma (SC), and clear cell carcinoma (CCC).

METHODS

In this study, 274 consecutive patients with histopathologically confirmed endometrial cancer (231 ECs, 25 SCs, and 18 CCCs) who underwent MRI before hysterectomy were enrolled. MRI images were retrospectively reviewed and compared between the three pathologies.

RESULTS

The maximum diameters (55.6 ± 34.7 vs. 39.3 ± 21.6 vs. 39.4 ± 26.8 mm) (p < 0.05) and apparent diffusion coefficient (ADC) values (1.11 ± 0.21 vs. 0.84 ± 0.17 vs. 0.86 ± 0.16 × 10-3 mm2/s) (p < 0.01) were significantly greater in CCCs than in ECs and SCs, respectively. Infiltrative growth pattern (33% vs. 6%) (p < 0.01) was more frequent in CCCs than in ECs. Peritoneal dissemination (16% vs. 0%) (p < 0.01) and heterogeneous signal on diffusion-weighted (61% vs. 32%) (p < 0.05) images were more frequent in SCs than in ECs, respectively. Abnormal ascites (12% vs. 11% vs. 0%) and heterogeneous signal on T1-weighted (28% vs. 50% vs. 9%), T2-weighted (64% vs. 72% vs. 36%), and fat-suppressed gadolinium-enhanced T1-weighted (80% vs. 90% vs. 46%) images were more frequent in SCs and CCCs than in ECs, respectively (p < 0.05).

CONCLUSIONS

SCs frequently exhibited a heterogeneous signal with peritoneal dissemination and abnormal ascites. Alternatively, CCCs tended to have a larger tumor size and higher ADC values with an infiltrative growth pattern, heterogeneous signal, and abnormal ascites.

KEY POINTS

• SCs tend to have a heterogeneous signal intensity with peritoneal dissemination and abnormal ascites compared to ECs. • CCCs tend to have a heterogeneous signal intensity with an infiltrative growth pattern and abnormal ascites compared to ECs. • CCCs have a larger tumor size and higher ADC values compared to ECs and SCs.

Diffusion-Weighted Magnetic Resonance Imaging in Ovarian Cancer: Exploiting Strengths and Understanding Limitations

Detection, characterization, staging, and response assessment are key steps in the imaging pathway of ovarian cancer. The most common type, high grade serous ovarian cancer, often presents late, so that accurate disease staging and response assessment are required through imaging in order to improve patient management. Currently, computerized tomography (CT) is the most common method for these tasks, but due to its poor soft-tissue contrast, it is unable to quantify early response within lesions before shrinkage is observed by size criteria. Therefore, quantifiable techniques, such as diffusion-weighted magnetic resonance imaging (DW-MRI), which generates high contrast between tumor and healthy tissue, are increasingly being explored. This article discusses the basis of diffusion-weighted contrast and the technical issues that must be addressed in order to achieve optimal implementation and robust quantifiable diffusion-weighted metrics in the abdomen and pelvis. The role of DW-MRI in characterizing adnexal masses in order to distinguish benign from malignant disease, and to differentiate borderline from frankly invasive malignancy is discussed, emphasizing the importance of morphological imaging over diffusion-weighted metrics in this regard. Its key role in disease staging and predicting resectability in comparison to CT is addressed, including its valuable use as a biomarker for following response within individual lesions, where early changes in the apparent diffusion coefficient in peritoneal metastases may be detected. Finally, the task of implementing DW-MRI into clinical trials in order to validate this biomarker for clinical use are discussed, along with the trials that include it within their protocols.

Low-Grade Serous Carcinoma of the Ovary: The Current Status

Low-grade serous carcinoma (LGSC) of the ovary is a rare histological subtype of epithelial ovarian carcinoma. It has distinct clinical behavior and a specific molecular profile. Compared with high-grade serous carcinoma, this tumor presents at a younger age, has an indolent course, and is associated with prolonged survival. LGSC can arise de novo or originate following a serous borderline tumor (SBT). Pathological differentiation between LGSC and other ovarian carcinoma histological subtypes is fundamental. Several factors might influence the overall outcome, such as the age at diagnosis, current smoking, elevated body mass index, mutational status, hormonal receptors’ expression, and Ki-67 proliferation index. Surgery is the main treatment option in LGSC, and efforts must be maximized to achieve a microscopic residual in metastatic disease. Despite being relatively chemo-resistant, adjuvant platinum-based chemotherapy remains the standard of care in LGSC. Hormonal maintenance therapy after adjuvant chemotherapy results in improved outcomes. Treatment options for disease recurrence include secondary cytoreductive surgery, chemotherapy, hormonal therapy, targeted therapy, and clinical trials. Advancements in genomic studies and targeted therapies are expected to change the treatment landscape in LGSC.

An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer

This retrospective single-center study included patients diagnosed with epithelial ovarian cancer (EOC) using preoperative pelvic magnetic resonance imaging (MRI). The apparent diffusion coefficient (ADC) of the axial MRI maps that included the largest solid portion of the ovarian mass was analysed. The mean ADC values (ADC_mean) were derived from the regions of interest (ROIs) of each largest solid portion. Logistic regression and three types of machine learning (ML) applications were used to analyse the ADCs and clinical factors. Of the 200 patients, 103 had high-grade serous ovarian cancer (HGSOC), and 97 had non-HGSOC (endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, and low-grade serous ovarian cancer). The median ADC_mean of patients with HGSOC was significantly lower than that of patients without HGSOCs. Low ADC_mean and CA 19-9 levels were independent predictors for HGSOC over non-HGSOC. Compared to stage I disease, stage III disease was associated with HGSOC. Gradient boosting machine and extreme gradient boosting machine showed the highest accuracy in distinguishing between the histological findings of HGSOC versus non-HGSOC and between the five histological types of EOC. In conclusion, ADC_mean, disease stage at diagnosis, and CA 19-9 level were significant factors for differentiating between EOC histological types.

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.

MR imaging findings of low-grade serous carcinoma of the ovary: comparison with serous borderline tumor

PURPOSE

To assess MR imaging findings of low-grade serous carcinoma (LGSC) of the ovary compared with those of serous borderline tumor (SBT).

METHODS

Twenty-four patients with histopathologically proven 7 LGSCs and 25 SBTs who underwent preoperative MR imaging were included. We retrospectively reviewed MR images and compared MR findings between the two pathologies.

RESULTS

The predominantly solid lesions were marginally more frequent in LGSCs than in SBTs (43% vs. 8%, p = 0.057). All predominantly cystic LGSCs were multilocular cystic lesions with mural nodules. Predominantly solid LGSCs exhibited pure solid masses in 2 of 3 and solid masses with intratumoral cysts in 1 of 3. Papillary growth pattern with internal branching was observed only in 18 of 25 SBTs. Signal intensity ratio on T2-weighted images (4.48 ± 1.55 vs. 8.40 ± 3.53, p < 0.01) and apparent diffusion coefficient (ADC) values (1.12 ± 0.21 vs. 1.73 ± 0.27 × 10^-3 mm²/s, p < 0.01) of solid components was significantly lower in LGSCs than in SBTs.

CONCLUSION

Compared with SBTs, lower signal intensity on T2-weighted images and lower ADC values were characteristic features of solid components in LGSCs. Papillary growth pattern with internal branching was not observed in LGSCs.