Accuracy of magnetic resonance imaging in ovarian tumor: a systematic quantitative review

A Novel Approach for Estimating Ovarian Cancer Tissue Heterogeneity through the Application of Image Processing Techniques and Artificial Intelligence

PURPOSE

Tumor heterogeneity may be responsible for poor response to treatment and adverse prognosis in women with HGOEC. The purpose of this study is to propose an automated classification system that allows medical experts to automatically identify intratumoral areas of different cellularity indicative of tumor heterogeneity.

METHODS

Twenty-two patients underwent dedicated pelvic MRI, and a database of 11,095 images was created. After image processing techniques were applied to align and assess the cancerous regions, two specific imaging series were used to extract quantitative features (radiomics). These features were employed to create, through artificial intelligence, an estimator of the highly cellular intratumoral area as defined by arbitrarily selected apparent diffusion coefficient (ADC) cut-off values (ADC < 0.85 × 10-3 mm2/s).

RESULTS

The average recorded accuracy of the proposed automated classification system was equal to 0.86.

CONCLUSION

The proposed classification system for assessing highly cellular intratumoral areas, based on radiomics, may be used as a tool for assessing tumor heterogeneity.

Comparison of ultrasound-based ADNEX model with magnetic resonance imaging for discriminating adnexal masses: a multi-center study

Objectives

The ADNEX model offered a good diagnostic performance for discriminating adnexal tumors, but research comparing the abilities of the ADNEX model and MRI for characterizing adnexal tumors has not been reported to our knowledge. The aim of this study was to evaluate the diagnostic accuracy of the ultrasound-based ADNEX (Assessment of Different NEoplasias in the adneXa) model in comparison with that of magnetic resonance imaging (MRI) for differentiating benign, borderline and malignant adnexal masses.

Methods

This prospective study included 529 women with adnexal masses who underwent assessment via the ADNEX model and subjective MRI analysis before surgical treatment between October 2019 and April 2022 at two hospitals. Postoperative histological diagnosis was considered the gold standard.

Results

Among the 529 women, 92 (17.4%) masses were diagnosed histologically as malignant tumors, 67 (12.7%) as borderline tumors, and 370 (69.9%) as benign tumors. For the diagnosis of malignancy, including borderline tumors, overall agreement between the ADNEX model and MRI pre-operation was 84.9%. The sensitivity of the ADNEX model of 0.91 (95% confidence interval [CI]: 0.85-0.95) was similar to that of MRI (0.89, 95% CI: 0.84-0.94; P=0.717). However, the ADNEX model had a higher specificity (0.90, 95% CI: 0.87-0.93) than MRI (0.81, 95% CI: 0.77-0.85; P=0.001). The greatest sensitivity (0.96, 95% CI: 0.92-0.99) and specificity (0.94, 95% CI: 0.91-0.96) were achieved by combining the ADNEX model and subjective MRI assessment. While the total diagnostic accuracy did not differ significantly between the two methods (P=0.059), the ADNEX model showed greater diagnostic accuracy for borderline tumors (P<0.001).

Conclusion

The ultrasound-based ADNEX model demonstrated excellent diagnostic performance for adnexal tumors, especially borderline tumors, compared with MRI. Accordingly, we recommend that the ADNEX model, alone or with subjective MRI assessment, should be used for pre-operative assessment of adnexal masses.

Attention feature fusion methodology with additional constraint for ovarian lesion diagnosis on magnetic resonance images

PURPOSE

It is challenging for radiologists and gynecologists to identify the type of ovarian lesions by reading magnetic resonance (MR) images. Recently developed convolutional neural networks (CNNs) have made great progress in computer vision, but their architectures still need modification if they are used in processing medical images. This study aims to improve the feature extraction capability of CNNs, thus promoting the diagnostic performance in discriminating between benign and malignant ovarian lesions.

METHODS

We introduce a feature fusion architecture and insert the attention models in the neural network. The features extracted from different middle layers are integrated with reoptimized spatial and channel weights. We add a loss function to constrain the additional probability vector generated from the integrated features, thus guiding the middle layers to emphasize useful information. We analyzed 159 lesions imaged by dynamic contrast-enhanced MR imaging (DCE-MRI), including 73 benign lesions and 86 malignant lesions. Senior radiologists selected and labeled the tumor regions based on the pathology reports. Then, the tumor regions were cropped into 7494 nonoverlapping image patches for training and testing. The type of a single tumor was determined by the average probability scores of the image patches belonging to it.

RESULTS

We implemented fivefold cross-validation to characterize our proposed method, and the distribution of performance matrics was reported. For all the test image patches, the average accuracy of our method is 70.5% with an average area under the curve (AUC) of 0.785, while the baseline is 69.4% and 0.773, and for the diagnosis of single tumors, our model achieved an average accuracy of 82.4% and average AUC of 0.916, which were better than the baseline (81.8% and 0.899). Moreover, we evaluated the performance of our proposed method utilizing different CNN backbones and different attention mechanisms.

CONCLUSIONS

The texture features extracted from different middle layers are crucial for ovarian lesion diagnosis. Our proposed method can enhance the feature extraction capabilities of different layers of the network, thereby improving diagnostic performance.

Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis

BACKGROUND

Accurate identification of ovarian cancer (OC) is of paramount importance in clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the medical imaging recognition. We systematically review articles on the diagnostic performance of AI in OC from medical imaging for the first time.

METHODS

The Medline, Embase, IEEE, PubMed, Web of Science, and the Cochrane library databases were searched for related studies published until August 1, 2022. Inclusion criteria were studies that developed or used AI algorithms in the diagnosis of OC from medical images. The binary diagnostic accuracy data were extracted to derive the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022324611.

FINDINGS

Thirty-four eligible studies were identified, of which twenty-eight studies were included in the meta-analysis with a pooled SE of 88% (95%CI: 85-90%), SP of 85% (82-88%), and AUC of 0.93 (0.91-0.95). Analysis for different algorithms revealed a pooled SE of 89% (85-92%) and SP of 88% (82-92%) for machine learning; and a pooled SE of 88% (84-91%) and SP of 84% (80-87%) for deep learning. Acceptable diagnostic performance was demonstrated in subgroup analyses stratified by imaging modalities (Ultrasound, Magnetic Resonance Imaging, or Computed Tomography), sample size (≤300 or >300), AI algorithms versus clinicians, year of publication (before or after 2020), geographical distribution (Asia or non Asia), and the different risk of bias levels (≥3 domain low risk or < 3 domain low risk).

INTERPRETATION

AI algorithms exhibited favorable performance for the diagnosis of OC through medical imaging. More rigorous reporting standards that address specific challenges of AI research could improve future studies.

FUNDING

This work was supported by the Natural Science Foundation of China (No. 82073647 to Q-JW and No. 82103914 to T-TG), LiaoNing Revitalization Talents Program (No. XLYC1907102 to Q-JW), and 345 Talent Project of Shengjing Hospital of China Medical University (No. M0268 to Q-JW and No. M0952 to T-TG).

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.

Comparison of conventional, doppler and contrast-enhanced ultrasonography in differential diagnosis of ovarian masses: a systematic review and meta-analysis

OBJECTIVES

To assess the value of conventional, Doppler and contrast-enhanced ultrasonography (CEUS) (conventional ultrasonography (US), Doppler US and CEUS) for diagnosing ovarian cancer.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

PubMed, Embase and the Cochrane Library were conducted for studies published until October 2021.

ELIGIBILITY CRITERIA

Studies assessed the diagnostic value of conventional US, Doppler US or CEUS for detecting ovarian cancer, with no restrictions placed on published language and status.

DATA EXTRACTION AND SYNTHESIS

The study selection and data extraction were performed by two independent authors. The sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR), diagnostic OR (DOR) and area under the receiver operating characteristic curve (AUC) were pooled using the bivariate generalised linear mixed model and random effects model.

RESULTS

The meta-analysis included 72 studies and involved 9296 women who presented with ovarian masses. The pooled sensitivity, specificity, PLR, NLR, DOR and AUC for conventional US were 0.91 (95% CI: 0.87 to 0.94) and 0.87 (95% CI: 0.82 to 0.91), 6.87 (95% CI: 4.98 to 9.49) and 0.10 (95% CI: 0.07 to 0.15), 57.52 (95% CI: 36.64 to 90.28) and 0.95 (95% CI: 0.93 to 0.97), respectively. The sensitivity, specificity, PLR, NLR, DOR and AUC for Doppler US were 0.93 (95% CI: 0.91 to 0.95) and 0.85 (95% CI: 0.80 to 0.89), 6.10 (95% CI: 4.59 to 8.11) and 0.08 (95% CI: 0.06 to 0.11), 61.76 (95% CI: 39.99 to 95.37) and 0.96 (95% CI: 0.94 to 0.97), respectively. The pooled sensitivity, specificity, PLR, NLR, DOR and AUC for CEUS were 0.97 (95% CI: 0.92 to 0.99) and 0.92 (95% CI: 0.85 to 0.95), 11.47 (95% CI: 6.52 to 20.17) and 0.03 (95% CI: 0.01 to 0.09), 152.11 (95% CI: 77.77 to 297.51) and 0.99 (95% CI: 0.97 to 0.99), respectively. Moreover, the AUC values for conventional US (p=0.002) and Doppler US (p=0.005) were inferior to those of CEUS.

CONCLUSIONS

Conventional US, Doppler US and CEUS have a relatively high differential diagnostic value for differentiating between benign and malignant ovarian masses. The diagnostic performance of CEUS was superior to that of conventional US and Doppler US.

Non-contrast MRI can accurately characterize adnexal masses: a retrospective study

Objective

To determine the accuracy of interpretation of a non-contrast MRI protocol in characterizing adnexal masses.

Methods and materials

Two hundred ninety-one patients (350 adnexal masses) who underwent gynecological MRI at our institution between the 1^st of January 2008 and the 31^st of December 2018 were reviewed. A random subset (102 patients with 121 masses) was chosen to evaluate the reproducibility and repeatability of readers’ assessments. Readers evaluated non-contrast MRI scans retrospectively, assigned a 5-point score for the risk of malignancy and gave a specific diagnosis. The reference standard for the diagnosis was histopathology or at least one-year imaging follow-up. Diagnostic accuracy of the non-contrast MRI score was calculated. Inter- and intra-reader agreement was analyzed with Cohen’s kappa statistics.

Results

There were 53/350 (15.1%) malignant lesions in the whole cohort and 20/121 (16.5%) malignant lesions in the random subset. Good agreement between readers was found for the non-contrast MRI score (к = 0.73, 95% confidence interval [CI] 0.58–0.86) whilst the intra-reader agreement was excellent (к = 0.81, 95% CI 0.70–0.88). The non-contrast MRI score value of ≥ 4 was associated with malignancy with a sensitivity of 84.9%, a specificity of 95.9%, an accuracy of 94.2% and a positive likelihood ratio of 21 (area under the receiver operating curve 0.93, 95% CI 0.90–0.96).

Conclusion

Adnexal mass characterization on MRI without the administration of contrast medium has a high accuracy and excellent inter- and intra-reader agreement. Our results suggest that non-contrast studies may offer a reasonable diagnostic alternative when the administration of intravenous contrast medium is not possible.

Key Points

• A non-contrast pelvic MRI protocol may allow the characterization of adnexal masses with high accuracy.

• The non-contrast MRI score may be used in clinical practice for differentiating benign from malignant adnexal lesions when the lack of intravenous contrast medium precludes analysis with the O–RADS MRI score.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07737-9.

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.

Surgery of Benign Ovarian Masses by a Gynecological Cancer Surgeon: A Cohort Study in a Tertiary Cancer Centre

Objectives This study aimed to evaluate diagnostic performance in characterising ovarian masses by our gynaecological oncology multidisciplinary team meeting (MDM). Surgical outcome and overall impact on patients and healthcare service were also assessed. Methods This was a prospective cohort study of all women with adnexal masses presenting to the gynaecological oncology MDM at a central London tertiary cancer centre between February 2017 and February 2018. The multidisciplinary team (MDT) outcome, imaging details, subjective opinion, tumour markers, surgical details, and final histological diagnosis were collected. Diagnostic performance was also determined. Results There were 200 eligible patients in the study period. MDM imaging review demonstrated a sensitivity of 98.4% (95% CI: 94.3% to 99.8%) and a specificity of 52% (95% CI: 40.2% to 63.7%). Thirty-five cases were false positive, either presumed invasive cancers (51%) or borderline tumours (49%). The most common histological types were serous (37%) and mucinous (31%) cystadenomas. A retrospective application of the International Ovarian Tumor Analysis (IOTA) Assessment of Different NEoplasias in the adneXa (ADNEX) model suggests a potential reduction in false-positive rates (17%). Among the false-positive cases, there was no postoperative (90 days) mortality and postoperative morbidity was 14% with only grade 2 (CD2) complications according to Clavien and Dindo's CD classification. Conclusion An MDT has high sensitivity but low specificity when characterising ovarian masses referred with possible ovarian cancer to the tertiary centre. False-positive values in ovarian cancers are an important indicator of over-treatment. More research is required to assess other methods, such as the IOTA ADNEX model, to reduce the false-positive value.

Advances in MR Imaging of the Female Pelvis

This article focuses on advanced MR imaging techniques of the female pelvis and clinical applications for benign and malignant disease. General and abbreviated protocols for female pelvic MR imaging are reviewed. Diffusion-weighted imaging, dynamic contrast-enhanced MR imaging, and susceptibility-weighted imaging are discussed in the context of adnexal mass characterization using the ADNEx-MR scoring system, evaluation of endometriosis, local staging of cervical and endometrial cancers, assessment of nodal and peritoneal metastasis, and potential detection of leiomyosarcoma. MR defecography is also discussed regarding evaluation of multicompartmental pelvic floor disorders.

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.

Accuracy of the ADNEX MR scoring system based on a simplified MRI protocol for the assessment of adnexal masses

PURPOSE

We aimed to evaluate the ADNEX MR scoring system for the prediction of adnexal mass malignancy, using a simplified magnetic resonance imaging (MRI) protocol.

METHODS

In this prospective study, 200 patients with 237 adnexal masses underwent MRI between February 2014 and February 2016 and were followed until February 2017. Two radiologists calculated ADNEX MR scores using an MRI protocol with a simplified dynamic study, not a high temporal resolution study, as originally proposed. Sensitivity, specificity, positive and negative predictive values, likelihood ratios, and the area under the receiver operating characteristic curve were calculated (cutoff for malignancy, score ≥ 4). The reference standard was histopathologic diagnosis or imaging findings during >12 months of follow-up.

RESULTS

Of 237 lesions, 79 (33.3%) were malignant. The ADNEX MR scoring system, using a simplified MRI protocol, showed 94.9% (95% confidence interval [CI], 87.5%-98.6%) sensitivity and 97.5% (95% CI, 93.6%-99.3%) specificity in malignancy prediction; it was thus highly accurate, like the original system. The level of interobserver agreement on simplified scoring was high (κ = 0.91).

CONCLUSION

In a tertiary cancer center, the ADNEX MR scoring system, even based on a simplified MRI protocol, performed well in the prediction of malignant adnexal masses. This scoring system may enable the standardization of MRI reporting on adnexal masses, thereby improving communication between radiologists and gynecologists.

Characterization of tubo-ovarian abscess mimicking adnexal masses: Comparison between contrast-enhanced CT, 18F-FDG PET/CT and MRI

0BJECTIVE: We compared the diagnostic accuracy of contrast-enhanced computed tomography (CT), fluorine 18-labeled-fludeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/CT and conventional magnetic resonance imaging (MRI) without and with diffusion-weighted imaging (DWI) for characterization of tubo-ovarian abscesses (TOAs) that mimic adnexal tumors.

MATERIALS AND METHODS

We evaluated (retrospectively) 43 patients who underwent contrast-enhanced CT, PET/CT, conventional MRI without and with DWI, and who were found to have TOAs and complex adnexal tumors. All images were evaluated independently by four radiologists using a two-point grading system. Results of contrast-enhanced CT, PET/CT, MRI without DWI, and MRI with DWI were compared for each patient using receiver operating characteristic curves. Sensitivity, specificity, and positive predictive value (PPV) were calculated and compared using the chi-square test.

RESULTS

Sensitivity of MRI with DWI (95%) was significantly higher than that of contrast-enhanced CT (78.6%), PET/CT (86.7%) and MRI without DWI (87.5%). Specificities of these modalities were not significantly different. The PPV of MRI with DWI (100%) was significantly higher than that of the other three modalities (CT, 72.4%; PET/CT 78.5%; MRI without DWI, 81.5%). Overall accuracy of MRI with DWI was significantly higher than that of the other three modalities (CT, 74.4%; PET/CT, 81.4%; MRI without DWI, 83.7%).

CONCLUSION

MRI with DWI shows high accuracy for characterization of complex ovarian lesions, and is the most useful method for differentiation of TOAs from ovarian tumors.

Diagnostic accuracy of inflammatory markers for distinguishing malignant and benign ovarian masses

Objective: To evaluate the role of inflammatory markers for distinguishing malignant and benign ovarian masses.

Methods: Preoperative demographic, clinicopathologic, and laboratory variables were reviewed in patients with an ovarian mass that was subsequently diagnosed as either epithelial ovarian cancer (EOC) or a benign ovarian mass on histologic analysis. The differences between variables of the two groups were further evaluated. Logistic regression analysis was applied to evaluate variables to predict the presence of EOC.

Results: According to the analysis of 229 patients with EOC, 120 (52.4%) patients had serous adenocarcinoma. Of the 229 patients, 110 (48.1%) patients had stage I or II disease and 119 (52.0%) had stage III or IV disease. There was a significant difference between EOC and benign ovarian mass in median values of variables such as age, white blood cell (WBC) count, hemoglobin concentration, platelet count, cancer antigen 125 (CA125) levels, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) (all P < 0.001, except for WBC count [P = 0.009]). In addition, there was significant difference in median values of these continuous variables among early-stage EOC, advanced-stage EOC, and benign ovarian mass (P < 0.001 for all variables). On multivariate logistic regression analysis, age (odds ratio [OR] = 4.14, P < 0.001), CA125 levels (OR = 9.87, P < 0.001), NLR (OR = 1.76, P = 0.049), PLR (OR = 2.41, P = 0.004), and LMR (OR = 0.51, P = 0.024) were found to significantly predict the presence of EOC.

Conclusion: The three LMR, NLR, and PLR markers were found to be predictors for the presence of EOC. Further prospective studies to assess these markers as screening tools for the presence of EOC are required.

A systematic approach to adnexal masses discovered on ultrasound: the ADNEx MR scoring system

Adnexal lesions are a common occurrence in radiology practice and imaging plays a crucial role in triaging women appropriately. Current trends toward early detection and characterization have increased the need for accurate imaging assessment of adnexal lesions prior to treatment. Ultrasound is the first-line imaging modality for assessing adnexal lesions; however, approximately 20% of lesions are incompletely characterized after ultrasound evaluation. Secondary assessment with MR imaging using the ADNEx MR Scoring System has been demonstrated as highly accurate in the characterization of adnexal lesions and in excluding ovarian cancer. This review will address the role of MR imaging in further assessment of adnexal lesions discovered on US, and the utility of the ADNEx MR Scoring System.

Ultrasound-based logistic regression model LR2 versus magnetic resonance imaging for discriminating between benign and malignant adnexal masses: a prospective study

BACKGROUND

The diagnostic performances of the International Ovarian Tumor Analysis (IOTA) ultrasound-based logistic regression model (LR2) and magnetic resonance imaging (MRI) in discriminating between benign and malignant adnexal masses have not been directly compared in a single study.

METHODS

Using the IOTA LR2 model and subjective interpretation of MRI findings by experienced radiologists, 265 consecutive patients with adnexal masses were preoperatively evaluated in two hospitals between February 2014 and December 2015. Definitive histological diagnosis of excised tissues was used as a gold standard.

RESULTS

From the 265 study subjects, 54 (20.4%) tumors were histologically diagnosed as malignant (including 11 borderline and 3 metastatic tumors). Preoperative diagnoses of malignant tumors showed 91.7% total agreement between IOTA LR2 and MRI, with a kappa value of 0.77 [95% confidence interval (CI), 0.68-0.86]. Sensitivity of IOTA LR2 (0.94, 95% CI, 0.85-0.98) for predicting malignant tumors was similar to that of MRI (0.96, 95% CI, 0.87-0.99; P = 0.99), whereas specificity of IOTA LR2 (0.98, 95% CI, 0.95-0.99) was significantly higher than that of MRI (0.91, 95% CI, 0.87-0.95; P = 0.002). Combined IOTA LR2 and MRI results gave the greatest sensitivity (1.00, 95% CI, 0.93-1.00) and had similar specificity (0.91, 95% CI, 0.86-0.94) to MRI.

CONCLUSIONS

The IOTA LR2 model had a similar sensitivity to MRI for discriminating between benign and malignant tumors and a higher specificity compared with MRI. Our findings suggest that the IOTA LR2 model, either alone or in conjunction with MRI, should be included in preoperative evaluation of adnexal masses.

TNFα Increases RANKL Expression via PGE₂-Induced Activation of NFATc1

Tumor necrosis factor α (TNFα) is known to upregulate the expression of receptor activator of NF-κB ligand (RANKL). We investigated the role of the calcineurin/nuclear factor of activated T-cells (NFAT) signaling pathway in TNFα-induced RANKL expression in C2C12 and primary cultured mouse calvarial cells. TNFα-induced RANKL expression was blocked by the calcineurin/NFAT pathway inhibitors. TNFα increased NFAT transcriptional activity and subsequent RANKL promoter binding. Mutations in the NFAT-binding element (MT(N)) suppressed TNFα-induced RANKL promoter activity. TNFα increased prostaglandin E2 (PGE₂) production, which in turn enhanced NFAT transcriptional activity and binding to the RANKL promoter. MT(N) suppressed PGE₂-induced RANKL promoter activity. TNFα and PGE₂ increased the expression of RANKL, NFAT cytoplasmic-1 (NFATc1), cAMP response element-binding protein (CREB), and cyclooxygenase 2 (COX2); which increment was suppressed by indomethacin, a COX inhibitor. Mutations in the CRE-like element blocked PGE₂-induced RANKL promoter activity. PGE₂ induced the binding of CREB to the RANKL promoter, whereas TNFα increased the binding of both CREB and NFATc1 to this promoter through a process blocked by indomethacin. The PGE₂ receptor antagonists AH6809 and AH23848 blocked TNFα-induced expression of RANKL, NFATc1, and CREB; transcriptional activity of NFAT; and binding of NFATc1 or CREB to the RANKL promoter. These results suggest that TNFα-induced RANKL expression depends on PGE₂ production and subsequent transcriptional activation/enhanced binding of NFATc1 and CREB to the RANKL promoter.

The value of dynamic contrast-enhanced MRI in characterizing complex ovarian tumors

BACKGROUND

The study aimed to investigate the utility of dynamic contrast enhanced MRI (DCE-MRI) in the differentiation of malignant, borderline, and benign complex ovarian tumors.

METHODS

DCE-MRI data of 102 consecutive complex ovarian tumors (benign 15, borderline 16, and malignant 71), confirmed by surgery and histopathology, were analyzed retrospectively. The patterns (I, II, and III) of time-signal intensity curve (TIC) and three semi-quantitative parameters, including enhancement amplitude (EA), maximal slope (MS), and time of half rising (THR), were evaluated and compared among benign, borderline, and malignant ovarian tumors. The types of TIC were compared by Pearson Chi-square χ ² between malignant and benign, borderline tumors. The mean values of EA, MS, and THR were compared using one-way ANOVA or nonparametric Kruskal-Wallis test.

RESULTS

Fifty-nine of 71 (83%) malignant tumors showed a type-III TIC; 9 of 16 (56%) borderline tumors showed a type-II TIC, and 10 of 15 (67%) benign tumors showed a type-II TIC, with a statistically significant difference between malignant and benign tumors (P < 0.001) and between malignant and borderline tumors (P < 0.001). MS was significantly higher in malignant tumors than in benign tumors and in borderline than in benign tumors (P < 0.001, P = 0.013, respectively). THR was significantly lower in malignant tumors than in benign tumors and in borderline than in benign tumors (P < 0.001, P = 0.007, respectively). There was no statistically significant difference between malignant and borderline tumors in MS and THR (P = 0.19, 0.153) or among malignant, borderline, and benign tumors in EA (all P > 0.05).

CONCLUSIONS

DCE-MRI is helpful for characterizing complex ovarian tumors; however, semi-quantitative parameters perform poorly when distinguishing malignant from borderline tumors.

Usefulness of the preoperative platelet count in the diagnosis of adnexal tumors

Platelets seem to play a role in the development of ovarian cancer. Platelet count (PLT) is an ubiquitous available parameter. We analyzed retrospectively data of 756 patients with primary adnexal tumors: 584 benign and 172 malignant (148 invasive and 24 borderline) cases. We compared the diagnostic accuracy of CA125, PLT, and a combination of CA125 and PLT. The cutoff values for CA125 and PLT were 35 U/ml and 350/nl, respectively. The median age of patients with benign and malignant tumors was 45 and 64 years, respectively. A total of 77/172 (44.8 %) malignant and 50/584 (8.6 %) benign cases presented with thrombocytosis (PLT ≥350/nl). The median PLT differed between benign and malignant cases (257/nl vs. 330/nl; p < 0.001), similarly as CA125 did (17 vs. 371 U/ml; p < 0.001). In the multivariate analysis, age, CA125, and thrombocytosis predicted independently the presence of malignancy. The results of CA125 were false positive in 21 % and false negative in 13 %. If considered together, thrombocytosis + CA125 were false positive only in 9 %, whereas the false negative rate was 12 %. The sensitivity and specificity of CA125, thrombocytosis, and thrombocytosis + CA125 for detecting adnexal malignancy were 0.88/0.78, 0.45/0.91, and 0.81/0.94, respectively. The positive predictive value (PPV) of CA125, thrombocytosis, and thrombocytosis + CA125 was 0.79, 0.61, and 0.91, respectively. In conclusion, PLT is an ubiquitously available parameter that could be useful in the diagnostic evaluation of pelvic mass. Considering thrombocytosis additionally to CA125 improves the specificity and PPV and reduces the false positive rate in detecting adnexal malignancy.

Fertility sparing treatment in borderline ovarian tumours

Borderline ovarian tumours are low malignant potential tumours. They represent 10-15% of all epithelial ovarian malignancies. Patients with this type of tumour are younger at the time of diagnosis than patients with invasive ovarian cancer. Most of them are diagnosed in the early stages and have an excellent prognosis. It has been quite clearly established that the majority of borderline ovarian tumours should be managed with surgery alone. Because a high proportion of women with this malignancy are young and the prognosis is excellent, the preservation of fertility is an important issue in the management of these tumours. In this systemic review of the literature, we have evaluated in-depth oncological safety and reproductive outcomes in women with borderline ovarian tumours treated with fertility-sparing surgery, reviewing the indications, benefits, and disadvantages of each type of conservative surgery, as well as new alternative options to surgery to preserve fertility.

Prospective evaluation of 3T MRI findings for primary adnexal lesions and comparison with the final histological diagnosis

PURPOSE

We prospectively investigated the diagnostic accuracy of magnetic resonance imaging (MRI) at 3.0 Tesla (3T) for the detection of suspected primary adnexal masses in a large cohort of patients.

METHODS

This prospective clinical study included 223 patients with suspected gynaecological disease who were referred for 3T MRI assessments before laparoscopy or laparotomy. Fifty-nine patients were excluded. All detected adnexal pathologies on MRI were categorized into the four groups (endometric cysts, teratomas, benign tumours and malignant tumours). Histological findings were used as the comparative reference standard. As measures to detect or rule out primary adnexal masses, accuracy, sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) were determined by lesion-based evaluations.

RESULTS

The reference standard method detected 141 primary adnexal lesions in 125 patients. The areas under the receiver operating characteristic curve of the lesion-based evaluations for endometric cysts, teratomas, benign lesions and malignant lesions were 92.8, 93.6, 95.1 and 94.4 %. Lesion-based evaluation yielded an accuracy of 90.3 %, sensitivity of 92.7 %, specificity of 89.3 %, PPV of 77.6 % and NPV of 96.8 % in differentiating malignancies from non-malignant lesions. The diagnostic value of 3T MRI for detecting malignancies was superior to that for benign tumours.

CONCLUSIONS

3T MRI well categorize the characteristics of primary adnexal lesions and may be a reliable modality for distinguishing malignancies from benign tumours.

Diagnostic accuracy of diffusion-weighted imaging with conventional MR imaging for differentiating complex solid and cystic ovarian tumors at 1.5T

Background

Preoperative characterization of complex solid and cystic adnexal masses is crucial for informing patients about possible surgical strategies. Our study aims to determine the usefulness of apparent diffusion coefficients (ADC) for characterizing complex solid and cystic adnexal masses.

Methods

One-hundred and 91 patients underwent diffusion-weighted (DW) magnetic resonance (MR) imaging of 202 ovarian masses. The mean ADC value of the solid components was measured and assessed for each ovarian mass. Differences in ADC between ovarian masses were tested using the Student’s t-test. The receiver operating characteristic (ROC) was used to assess the ability of ADC to differentiate between benign and malignant complex adnexal masses.

Results

Eighty-five patients were premenopausal, and 106 were postmenopausal. Seventy-four of the 202 ovarian masses were benign and 128 were malignant. There was a significant difference between the mean ADC values of benign and malignant ovarian masses (p < 0.05). However, there were no significant differences in ADC values between fibrothecomas, Brenner tumors and malignant ovarian masses. The ROC analysis indicated that a cutoff ADC value of 1.20 x10^-3 mm²/s may be the optimal one for differentiating between benign and malignant tumors.

Conclusions

A high signal intensity within the solid component on T2WI was less frequently in benign than in malignant adnexal masses. The combination of DW imaging with ADC value measurements and T2-weighted signal characteristics of solid components is useful for differentiating between benign and malignant ovarian masses.