Diffusion-weighted and diffusion-tensor imaging of normal and diseased uterus

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.

Use of diffusion-weighted imaging and diffusion tensor imaging in assessment of myometrial invasion in patients of endometrial carcinoma and its correlation with histopathological grading (Prospective study)

Background

Endometrial cancer (EMC) is considered one of the most common gynecological cancers worldwide. In particular, the depth of myometrial invasion and histological grade of endometrial cancers (EMCs) are strong prognostic factors. Diffusion tensor measurements as mean diffusivity (MD) and fractional anisotropy (FA) values could be useful for assessing the depth of tumor invasion and its histological grade. The study aimed to evaluate the role of diffusion-weighted imaging (DWI) and diffusion tensor imaging in the detection of myometrial invasion in cases of endometrial carcinoma and prediction of its grade in vivo.

Results

This study included 50 female patients with pathologically proved endometrial carcinoma, and their ages ranged from 38 to 67 years; the mean age was 56.15 years (± 8.229 standard deviation “SD”). There was a significant statistical difference regarding the mean values of diffusion tensor fractional anisotropy (DT-FA), diffusion tensor mean diffusivity (DT-MD) and diffusion-weighted apparent diffusion coefficient(DW-ADC) values in differentiating between intact and infiltrated myometrium with (P value ≤ 0.001). The accuracy of DT-MD, DT-FA and DWI-ADC was 98%, 90% and 86%, respectively, in the detection of myometrial invasion. There was a statistically significant difference in the mean values of DT-FA, DT-MD and DW-ADC for differentiating endometrioid adenocarcinoma grades with the overall P values (˂0.001). The accuracy of DT-FA, DT- MD and DWI-ADC for differentiating grade 3 from grade 1 or 2 endometrioid adenocarcinoma was 94.9%, 84.6% and 74.4%, respectively. For differentiating grade 1 from grade 2 or 3 endometrioid adenocarcinoma, the accuracy of DT-FA, DT-MD and DWI-ADC was 90%, 89.7% and 84.6%, respectively. Mean DT-FA, DT-MD and DW-ADC values were inversely proportional to the degree of pathological grading with r =  − 0.867, − 0.762 and − 0.706, respectively.

Conclusion

Diffusion tensor imaging and DWI are helpful in the assessment of myometrial invasion and have a high negative correlation with histopathological grading in patients with endometrial cancer.

MRI of the endometrium - from normal appearances to rare pathology

MRI was recently included as a standard pre-operative diagnostic tool for patients with endometrial cancer. MR findings allow a better risk assessment and ultimately guides the surgical planning. Therefore, it is vital that the radiological interpretation is as accurate as possible. This requires essential knowledge regarding the appropriate MRI protocol, as well as different appearances of the endometrium, ranging from normal peri- and post-menopausal changes, benign findings (e.g. endometrial hyperplasia, polyp, changes due to exogenous hormones) to common and rare endometrium-related malignancies. Furthermore, this review will emphasize the role of MRI in staging endometrial cancer patients and highlight pitfalls that could result in the underestimation or overestimation of the disease extent.

Objective measures of adenomyosis on MRI and their diagnostic accuracy-a systematic review & meta-analysis

INTRODUCTION

Magnetic resonance imaging (MRI) diagnosis of adenomyosis is considered the most accurate non-invasive technique, but remains subjective, with no consensus on which diagnostic parameters are most accurate. We aimed to systematically review the literature on how adenomyosis can be objectively quantified on MRI in a scoping manner, to review the diagnostic performance of these characteristics compared with histopathological diagnosis, and to summarize correlations between measures of adenomyosis on MRI and clinical outcomes.

MATERIAL AND METHODS

We searched databases Pubmed, Embase, and Cochrane for relevant literature up to April 2020 according to PRISMA guidelines. We included studies that objectively assessed adenomyosis on MRI, and separately assessed studies investigating the diagnostic performance of MRI vs histopathology for inclusion in a meta-analysis. The QUADAS-2 tool was used for risk of bias, with many studies showing an unclear or high risk of bias.

RESULTS

Eighty studies were included, of which 14 assessed the diagnostic performance of individual MRI parameters, with four included in the meta-analysis of diagnostic accuracy. Common MRI parameters were: junctional zone (JZ) characteristics, such as maximum JZ thickness-pooled sensitivity 71.6% (95% CI 46.0%-88.2%), specificity 85.5% (52.3%-97.0%); JZ differential-pooled sensitivity 58.9% (95% CI 44.3%-72.1%), specificity 83.2% (95% CI 71.3%-90.8%); and JZ to myometrial ratio-pooled sensitivity 63.3% (95% CI 51.9%-73.4%), specificity 79.4% (95% CI 42.0%-95.4%); adenomyosis lesion size, uterine morphology (pooled sensitivity 42.9% (95% CI 15.9%-74.9%), specificity 87.7%, (95% CI 37.9-98.8) and changes in signal intensity-eg, presence of myometrium cysts; pooled 59.6% (95% CI 41.6%-75.4%) and specificity of 96.1% (95% CI 80.7%-99.3%). Other MRI parameters have been used for adenomyosis diagnosis, but their diagnostic performance is unknown. Few studies attempted to correlate adenomyosis MRI phenotype to clinical outcomes.

CONCLUSIONS

A wide range of objective parameters for adenomyosis exist on MRI; however, in many cases their individual diagnostic performance remains uncertain. JZ characteristics remain the most widely used and investigated with acceptable diagnostic accuracy. Specific research is needed into how these objective measures of adenomyosis can be correlated to clinical outcomes.

Whole-brain structural magnetic resonance imaging-based classification of primary dysmenorrhea in pain-free phase: a machine learning study

To develop a machine learning model to investigate the discriminative power of whole-brain gray-matter (GM) images derived from primary dysmenorrhea (PDM) women and healthy controls (HCs) during the pain-free phase and further evaluate the predictive ability of contributing features in predicting the variance in menstrual pain intensity. Sixty patients with PDM and 54 matched female HCs were recruited from the local university. All participants underwent the head and pelvic magnetic resonance imaging scans to calculate GM volume and myometrium-apparent diffusion coefficient (ADC) during their periovulatory phase. Questionnaire assessment was also conducted. A support vector machine algorithm was used to develop the classification model. The significance of model performance was determined by the permutation test. Multiple regression analysis was implemented to explore the relationship between discriminative features and intensity of menstrual pain. Demographics and myometrium ADC-based classifications failed to pass the permutation tests. Brain-based classification results demonstrated that 75.44% of subjects were correctly classified, with 83.33% identification of the patients with PDM (P < 0.001). In the regression analysis, demographical indicators and myometrium ADC accounted for a total of 29.37% of the variance in pain intensity. After regressing out these factors, GM features explained 60.33% of the remaining variance. Our results suggested that GM volume can be used to discriminate patients with PDM and HCs during the pain-free phase, and neuroimaging features can further predict the variance in the intensity of menstrual pain, which may provide a potential imaging marker for the assessment of menstrual pain intervention.

Multiparametric MRI Characterization of Funaki Types of Uterine Fibroids Considered for MR-Guided High-Intensity Focused Ultrasound (MR-HIFU) Therapy

RATIONALE AND OBJECTIVES

To compare quantitative multiparametric magnetic resonance imaging (mpMRI) data of symptomatic uterine fibroids being considered for MR-guided high-intensity focused ultrasound ablation with fibroid characterization based on the Funaki Classification scheme.

MATERIALS AND METHODS

This was a prospective, Institutional Review Board -approved, Health Insurance Portability, and Accountability Act-compliant study. Informed consent was obtained. From December 2013 to April 2015, 48 women with symptomatic fibroids underwent screening with mpMRI protocol including sagittal/axial T2-weighted fast spin-echo, sagittal diffusion-weighted, and sagittal dynamic contrast-enhanced 3D T1-weighted gradient echo imaging on a 3T magnet. All fibroids were assigned Funaki type 1, 2, or 3 based on T2-weighted imaging. Differences in size, perfusion, and diffusion/intravoxel incoherent motion parameters among the three Funaki types were determined using linear mixed model. A logistic regression analysis was performed to select the best model in predicting type 3 fibroids.

RESULTS

A total of 100 fibroids were assessed (20 type 1, 66 type 2, and 14 type 3). Apparent diffusion coefficient and D of type 3 fibroids were significantly higher than those of type 1 (P < 0.0001, P < 0.0001) and 2 fibroids (P = 0.004, P < 0.0001) respectively. Transfer constant of type 3 fibroids was significantly higher than type 1 (P = 0.0357), but not than type 2 (P = 0.0752). A cutoff value of D = 1 × 10^-3 mm²/s offers an accuracy, sensitivity, and specificity of 76%, 71%, and 77%, respectively, for the diagnosis of Funaki 3 fibroids.

CONCLUSION

mpMRI-derived quantitative parameters may enable a more objective selection of patients prior to MR-guided high-intensity focused ultrasound therapy.

Differentiation grade for extrahepatic bile duct adenocarcinoma: Assessed by diffusion-weighted imaging at 3.0-T MR

PURPOSE-

To assess the pathological differentiation grade in the patients with extrahepatic bile duct adenocarcinoma (EBDA) using diffusion-weighted imaging (DWI) at 3.0-T MR.

METHODS-

Sixty-eight patients who were clinically and histologically diagnosed with EBDA underwent abdominal DWI within 2 weeks before surgery. The lesion signal intensity, signal intensity ratio of the lesion and hepar (SIR-LH) value, and apparent diffusion coefficient (ADC) value in patients with EBDA were retrospectively analysed.

RESULTS

-In the 68 patients, 22 well-differentiated, 36 moderately-differentiated, and 10 poorly-differentiated EBDAs were histopathological confirmed. These EBDAs exhibited hyper-intensity on DWI in 95.59% of patients. Hyper-intensity lesions were found in 90.91% of patients with good-differentiation, in 97.22% with moderate-differentiation and in 100% with poor-differentiation. There showed no statistical difference for the lesion signal intensity (P=0.426) and SIR-LH value (P=0.766) on DWI among three groups. The median ADC value of the well-differentiated, moderately-differentiated and poorly-differentiated EBDAs were 1.506×10^-3mm²/s, 1.275×10^-3mm²/s and 1.154×10^-3mm²/s, respectively. As the pathological differentiation grade decreased, the lesion ADC value of EBDA gradually declined (x²=51.220, P=0.000). The ADC value <1.184×10^-3mm²/s can predict the poorly-differentiated EBDA with a sensitivity of 100% and a specificity of 94.83%. The ADC value >1.316×10^-3mm²/s can forecast the well-differentiated EBDA with a sensitivity of 100% and a specificity of 84.78%.

CONCLUSIONS-

The histopathological differentiation grade of EBDA can be detected non-invasively using DWI at 3.0-T MR.