Utility of conventional and diffusion-weighted MRI features in distinguishing benign from malignant endometrial lesions

Preoperative Haematologic Markers for the Differentiation of Endometrial Cancer from Benign Endometrial Lesions in Postmenopausal Patients with Endometrial Masses

Purpose

The diagnostic value of preoperative hematological changes in endometrial cancer (EC) remains unclear. This study aimed to assess the role of preoperative hematologic parameters in differentiating EC from benign endometrial lesions in postmenopausal women with endometrial masses.

Methods

Preoperative laboratory variables were retrospectively reviewed in patients with malignant or benign endometrial lesions, and the significance of intergroup differences was assessed. Receiver operating characteristic curves were used to analyze the optimal cut-off values for each variable. Logistic regression analysis was used to identify the variables predicting the presence of endometrial malignancy.

Results

Preoperative laboratory variables of 176 patients (84 EC and 92 benign lesions) with endometrial masses were analyzed. Significant differences were observed between malignant and benign lesions in terms of WBC count, ANC, MCV, MPV, PDW, CA125, NLR, PMR, LMR, and SII (P < 0.05). Multivariate analyses showed that a high WBC count, high ANC, low MCV, low MPV, low PDW, high CA125, high NLR, high PMR, high LMR, and high SII independently predicted the presence of endometrial malignancy.

Conclusion

The combination markers, MPV+PDW+NLR, had good discriminatory power for the presence of malignancy (AUC 0.797). Our results suggest that hematologic markers could be useful for the differentiation of malignant and benign endometrial lesions.

Development of MRI-based radiomics predictive model for classifying endometrial lesions

An unbiased and accurate diagnosis of benign and malignant endometrial lesions is essential for the gynecologist, as each type might require distinct treatment. Radiomics is a quantitative method that could facilitate deep mining of information and quantification of the heterogeneity in images, thereby aiding clinicians in proper lesion diagnosis. The aim of this study is to develop an appropriate predictive model for the classification of benign and malignant endometrial lesions, and evaluate potential clinical applicability of the model. 139 patients with pathologically-confirmed endometrial lesions from January 2018 to July 2020 in two independent centers (center A and B) were finally analyzed. Center A was used for training set, while center B was used for test set. The lesions were manually drawn on the largest slice based on the lesion area by two radiologists. After feature extraction and feature selection, the possible associations between radiomics features and clinical parameters were assessed by Uni- and multi- variable logistic regression. The receiver operator characteristic (ROC) curve and DeLong validation were employed to evaluate the possible predictive performance of the models. Decision curve analysis (DCA) was used to evaluate the net benefit of the radiomics nomogram. A radiomics prediction model was established from the 15 selected features, and were found to be relatively high discriminative on the basis of the area under the ROC curve (AUC) for both the training and the test cohorts (AUC = 0.90 and 0.85, respectively). The radiomics nomogram also showed good performance of discrimination for both the training and test cohorts (AUC = 0.91 and 0.86, respectively), and the DeLong test shows that AUCs were significantly different between clinical parameters and nomogram. The result of DCA demonstrated the clinical usefulness of this novel nomogram method. The predictive model constructed based on MRI radiomics and clinical parameters indicated a highly diagnostic efficiency, thereby implying its potential clinical usefulness for the precise identification and prediction of endometrial lesions.

Whole-lesion apparent diffusion coefficient (ADC) histogram as a quantitative biomarker to preoperatively differentiate stage IA endometrial carcinoma from benign endometrial lesions

BACKGROUND

To assess the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in differentiating stage IA endometrial carcinoma (EC) from benign endometrial lesions (BELs) and characterizing histopathologic features of stage IA EC preoperatively.

METHODS

One hundred and six BEL and 126 stage IA EC patients were retrospectively enrolled. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. The Mann-Whitney U or Student's t-test was used to compare the differences between the two groups. Models based on clinical parameters and histogram features were established using multivariate logistic regression. Receiver operating characteristic (ROC) analysis and calibration curves were used to assess the models.

RESULTS

Stage IA EC showed lower ADC_10th, ADC_90th, ADC_min, ADC_max, ADC_mean, ADC_median, interquartile range, mean absolute deviation, robust mean absolute deviation (rMAD), root mean squared, energy, total energy, entropy, variance, and higher skewness, kurtosis and uniformity than BELs (all p < 0.05). ADC_median yielded the highest area under the ROC curve (AUC) of 0.928 (95% confidence interval [CI] 0.895-0.960; cut-off value = 1.161 × 10^-3 mm²/s) for differentiating stage IA EC from BELs. Moreover, multivariate analysis demonstrated that ADC-score (ADC_10th + skewness + rMAD + total energy) was the only significant independent predictor (OR = 2.641, 95% CI 2.045-3.411; p < 0.001) for stage IA EC when considering clinical parameters. This ADC histogram model (ADC-score) achieved an AUC of 0.941 and a bias-corrected AUC of 0.937 after bootstrap resampling. The model performed well for both premenopausal (accuracy = 0.871) and postmenopausal (accuracy = 0.905) patients. Besides, ADC_min and ADC_10th were significantly lower in Grade 3 than in Grade 1/2 stage IA EC (p = 0.022 and 0.047). At the same time, no correlation was found between ADC histogram parameters and the expression of Ki-67 in stage IA EC (all p > 0.05).

CONCLUSIONS

Whole-lesion ADC histogram analysis could serve as an imaging biomarker for differentiating stage IA EC from BELs and assisting in tumor grading of stage IA EC, thus facilitating personalized clinical management for premenopausal and postmenopausal patients.

Different multiparametric MRI-based radiomics models for differentiating stage IA endometrial cancer from benign endometrial lesions: A multicenter study

Purpose

The aim of this study was to evaluate the value of different multiparametric MRI-based radiomics models in differentiating stage IA endometrial cancer (EC) from benign endometrial lesions.

Methods

The data of patients with endometrial lesions from two centers were collected. The radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) map, and late contrast-enhanced T1-weighted imaging (LCE-T1WI). After data dimension reduction and feature selection, nine machine learning algorithms were conducted to determine which was the optimal radiomics model for differential diagnosis. The univariate analyses and logistic regression (LR) were performed to reduce valueless clinical parameters and to develop the clinical model. A nomogram using the radscores combined with clinical parameters was developed. Two integrated models were obtained respectively by the ensemble strategy and stacking algorithm based on the clinical model and optimal radiomics model. The area under the curve (AUC), clinical decisive curve (CDC), net reclassification index (NRI), and integrated discrimination index (IDI) were used to evaluate the performance and clinical benefits of the models.

Results

A total of 371 patients were incorporated. The LR model was the optimal radiomics model with the highest average AUC (0.854) and accuracy (0.802) in the internal and external validation groups (AUC = 0.910 and 0.798, respectively), and outperformed the clinical model (AUC = 0.739 and 0.592, respectively) or the radiologist (AUC = 0.768 and 0.628, respectively). The nomogram (AUC = 0.917 and 0.802, respectively) achieved better discrimination performance than the optimal radiomics model in two validation groups. The stacking model (AUC = 0.915) and ensemble model (AUC = 0.918) had a similar performance compared with the nomogram in the internal validation group, whereas the AUCs of the stacking model (AUC = 0.792) and ensemble model (AUC = 0.794) were lower than those of the nomogram and radiomics model in the external validation group. According to the CDC, NRI, and IDI, the optimal radiomics model, nomogram, stacking model, and ensemble model achieved good net benefits.

Conclusions

Multiparametric MRI-based radiomics models can non-invasively differentiate stage IA EC from benign endometrial lesions, and LR is the best machine learning algorithm. The nomogram presents excellent and stable diagnostic efficiency.

Diagnostic value of diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) in differentiation between normal and abnormally thickened endometrium: prospective study

Background

Diffusion tensor imaging (DTI) can be beneficial to differentiate between endometrium and other uterine layers. It is believed that it can be used to differentiate between normal and abnormally thickened endometrium. The purpose of this study was to find out the diagnostic value of DTI as an extension of DWI in characterization of abnormally thickened endometrium and differentiate it from normal.

Results

This study included 68 females, results of 3 of them were excluded (unable to complete the study), so the final number was 65 females subdivided into 2 groups; (A) control: 24 (13 premenopausal and 11 asymptomatic postmenopausal), (B) pathological thickened endometrium: 41 (11 premenopausal and 30 postmenopausal): benign (21 patients) and malignant (20 patients). The collected data was correlated to the histopathological results (as the gold standard) in cases of endometrial pathologies. The mean DW-ADC values for normal, benign, and malignant patients were 1.43 ± 0.13, 1.56 ± 0.17, and 0.86 ± 0.16 respectively and with significant statistical difference between normal and benign endometrial lesions (P value = 0.006), and between normal and malignant endometrial lesions, and between benign and malignant endometrial lesions (P value ˂ 0.001).

The DTI-FA mean values for normal, benign, and malignant patients were 0.349 ± 0.08, 0.29 ± 0.09, and 0.299 ± 0.08 respectively and with significant statistical difference between normal and benign endometrial lesions (P value = 0.02), but there is no significant statistical difference regarding DTI-FA values between normal and malignant endometrial lesions or between benign and malignant endometrial lesions (P value ˃ 0.05). Also, there is a significant statistical difference regarding DTI-MD mean values between normal (1.59 ± 0.06) and benign (1.37 ± 0.09), normal and malignant (0.71 ± 0.25), and between benign and malignant endometrial lesions (P value ˂ 0.001). The DT-MD had a higher sensitivity, specificity, and accuracy than both DW-ADC and DT-FA in differentiating normal, benign, and malignant endometrial pathologies.

Conclusion

DTI (added to DWI) is a valuable non-invasive tool that can increase the accuracy in differentiating normal, benign, and malignant endometrial conditions, helping early management, and decrease the possibility of misdiagnosis.

Multiparametric magnetic resonance imaging of endometrial polypoid lesions

Endometrial polypoid lesions encompass various conditions from physiologic changes to benign or malignant disease. Differentiating between the various causes of endometrial polypoid lesions remains difficult by transvaginal sonography. Magnetic resonance imaging (MRI) can provide valuable information regarding endometrial polypoid lesions in situations where it is difficult to obtain histologic samples. Multiparametric MRI including T2-weighted images, T1-weighted fat-saturation contrast-enhanced images, and diffusion-weighted images may be helpful for differentiating the various endometrial polypoid lesions and establishing specific diagnoses and appropriate treatment.

ACR Appropriateness Criteria® Abnormal Uterine Bleeding

This publication summarizes the relevant literature for the imaging of patients with symptoms of abnormal uterine bleeding, including initial imaging, follow-up imaging when the original ultrasound is inconclusive, and follow-up imaging when surveillance is appropriate. For patients with abnormal uterine bleeding, combined transabdominal and transvaginal ultrasound of the pelvis with Doppler is the most appropriate initial imaging study. If the uterus is incompletely visualized with the initial ultrasou2nd, MRI of the pelvis without and with contrast is the next appropriate imaging study, unless a polyp is suspected on the original ultrasound, then sonohysterography can be performed. If the patient continues to experience abnormal uterine bleeding, assessment with ultrasound of the pelvis, sonohysterography, and MRI of the pelvis without and with contrast would be appropriate. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

The usefulness of diffusion-weighted MRI in the differentiation between focal uterine endometrial soft tissue lesions

Background

Several endometrial conditions may be challenging for radiologists due to the overlap of imaging features and variable endometrial pathologies. MRI with DWI is the most commonly used imaging technique for the diagnosis and characterization of endometrial focal lesions.

Results

The 50 studied lesions were classified according to their histopathological results into the benign group (28 lesions, 56%) and the malignant group (22 lesions, 44%). Conventional MRI could correctly diagnose 39 of the 50 lesions (22/28 benign and 17/22 malignant lesions), achieving a sensitivity of 77.27%, specificity of 78.56%, accuracy of 78%, predictive positive value (PPV) of 73.91%, and negative positive value (NPV) of 81.48%. By combining DWI and apparent diffusion coefficient (ADC) value mapping at a high b value (b = 1000) in MRI, we could correctly diagnose 47 of the 50 lesions (26/28 benign and 21/22 malignant lesions), with increased sensitivity (95.45%), specificity (92.86%), accuracy (94%), PPV (91.3%), and NPV (96%).

Conclusion

Combining DWI with ADC mapping at a high b value in pelvic MRI examination is valuable in differentiating endometrial focal lesions with increased diagnostic sensitivity, specificity, and accuracy.

Aim of the work

This study aimed to evaluate the role of DWI in the diagnosis and differential diagnosis of benign and malignant focal endometrial masses.

Exploratory Study of Apparent Diffusion Coefficient Histogram Metrics in Assessing Pancreatic Malignancy

PURPOSE

To evaluate whole-lesion 3D-histogram apparent diffusion coefficient (ADC) metrics for assessment of pancreatic malignancy.

METHODS

Forty-two pancreatic malignancies (36 pancreatic adenocarcinoma [PDAC], 6 pancreatic neuroendocrine [PanNET]) underwent abdominal magnetic resonance imaging (MRI) with diffusion-weighted imaging before endoscopic ultrasound biopsy or surgical resection. Two radiologists independently placed 3D volumes of interest to derive whole-lesion histogram ADC metrics. Mann-Whitney tests and receiver operating characteristic analyses were used to assess metrics' diagnostic performance for lesion histology, T-stage, N-stage, and grade.

RESULTS

Whole-lesion ADC histogram metrics lower in PDACs than PanNETs for both readers (P ≤ .026) were mean ADC (area under the curve [AUC] = 0.787-0.792), mean of the bottom 10th percentile (mean_0-10) (AUC = 0.787-0.880), mean of the 10th-25th percentile (mean_10-25) (AUC = 0.884-0.917) and mean of the 25th-50th percentile (mean_25-50) (AUC = 0.829-0.829). For mean_10-25 (metric with highest AUC for identifying PDAC), for reader 1 a threshold > 0.94 × 10^-3 mm²/s achieved sensitivity 94% and specificity 83%, and for reader 2 a threshold > 0.82 achieved sensitivity 97% and specificity 67%. Metrics lower in nodal status ≥ N₁ than N₀ for both readers (P ≤ .043) were mean_0-10 (AUC = 0.789-0.822) and mean_10-25 (AUC = 0.800-0.822). For mean_10-25 (metric with highest AUC for identifying N₀), for reader 1 a threshold <1.17 achieved sensitivity 87% and specificity 67%, and for reader 2 a threshold <1.04 achieved sensitivity 87% and specificity 83%. No metric was associated with T-stage (P > .195) or grade (P > .215).

CONCLUSION

Volumetric ADC histogram metrics may serve as non-invasive biomarkers of pancreatic malignancy. Mean_10-25 outperformed standard mean for lesion histology and nodal status, supporting the role of histogram analysis.

The ability of ADC measurements in the assessment of patients with stage I endometrial carcinoma based on three risk categories

BACKGROUND

Better selection of patients with intermediate and high-risk stage I endometrial carcinoma (EC) for lymphadenectomy has an important effect on the prognosis.

PURPOSE

To investigate the role of apparent diffusion coefficient (ADC) measurements in the assessment of stage I EC patients based on three risk categories.

MATERIAL AND METHODS

We retrospectively studied 80 patients with EC and 28 cervical cancer patients with normal endometrium. 1.5-T conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) (b = 0, 1000 s/mm²) were performed, and ADC values were calculated. Sixty-eight stage I EC patients were divided into three groups: low-risk EC (group 1); intermediate-risk EC (group 2); and high-risk EC (group 3). The remaining 12 EC patients were in stages II and III. Intraclass coefficient, Mann-Whitney U test, Kruskal-Wallis test, and receiver operating characteristics were used for statistical analysis.

RESULTS

The mean ADC values ( × 10^-3 mm² /s) were 0.851 ± 0.131, 0.734 ± 0.108, and 0.710 ± 0.108 for groups 1, 2 and 3, respectively. Significant statistical differences were achieved for the three groups ( P = 0.0005). The mean ADC values of group 1 were significantly lower than those in group 2 + 3 (0.725 ± 0.106; P = 0.0001). For the prediction of groups 2 + 3, the area under the curve of 0.786 and the cut-off value of ≤ 0.742 were identified, with a sensitivity, specificity, and accuracy of 66.67%, 84.09%, and 73.53%, respectively.

CONCLUSION

ADC measurements may have the potential to select intermediate-risk and high-risk stage I EC patients for lymphadenectomy.

Current strategies in the diagnosis of endometrial cancer

PURPOSE

Endometrial cancer is the most common gynecological malignancy in developed countries. There are no uniform recommendations for endometrial cancer screening in the general population. Therefore, it is of paramount importance that the primary physician profoundly understands, and is familiar with the methods for prevention and early detection of endometrial cancer. The aim of this review is to provide the primary physician with a toolbox to reach these goals.

METHODS

We performed a systemic review to summarize the current strategies to diagnose and prevent endometrial cancer. Many published articles from the last years were identified and included.

RESULTS

A systematic review that summarizes the important subjects in the diagnosis and prevention of endometrial cancer.

CONCLUSION

Maintaining a high index of suspicion and obtaining endometrial biopsies from all suspected patients is the key for achieving a timely diagnosis.

A case of polypoid endometriosis with malignant transformation

Polypoid endometriosis is a benign, rare variant of endometriosis which forms multiple polypoid nodules in the female pelvis mimicking malignant tumors; however, it may rarely cause malignant transformation. We report magnetic resonance imaging findings of a case of polypoid endometriosis with malignant transformation. Multiple high-signal intensity polypoid nodules in the cul-de-sac surrounded by low-signal intensity rim-like fibrous adhesion protruding to the posterior wall of the uterine body were demonstrated on T2-weighted images. The polypoid nodules showed weak contrast enhancement compared with that of uterine myometrium on post-contrast T1-weighted images, and slight high signal intensity on diffusion-weighted images with relatively high mean apparent diffusion coefficient. Reported cases of polypoid endometriosis showed intense contrast enhancement similar to that of uterine myometrium, and weak contrast enhancement similar to that of endometrial carcinoma may be suggestive for malignant transformation of polypoid endometriosis.