Difference in Diffusion-Weighted Magnetic Resonance Imaging and Diffusion Tensor Imaging Parameters Between Endometrioid Endometrial Adenocarcinoma and Uterine Serous Adenocarcinoma: A Retrospective Study

Whole-tumor apparent diffusion coefficient histogram analysis for preoperative risk stratification in endometrial endometrioid adenocarcinoma

OBJECTIVE

To investigate the application of whole-tumor apparent diffusion coefficient (ADC) histogram metrics for preoperative risk stratification in endometrial endometrioid adenocarcinoma (EEA).

METHODS

Preoperative MRI of 502 EEA patients were retrospectively analyzed. Whole tumor ADC histogram analysis was performed with regions of interest drawn on all tumor slices of diffusion-weighted imaging scans. Risk stratification was based on ESMO-ESTRO-ESP guidelines: low-, intermediate-, high-intermediate-, and high-risk. Univariable analysis was used to compare ADC histogram metrics (tumor volume, minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC [recorded as P10, P25, P50, P75, and P90 ADC, respectively]; skewness; and kurtosis) between different risk EEAs, and multivariable logistic regression analysis to determine the optimal metric or combined model for risk stratifications. Receiver operating characteristic curve analysis with the area under the curve (AUC) was used for diagnostic performance evaluation.

RESULTS

A decreasing tendency in multiple ADC values was observed from the low- to high-intermediate-risk EEAs. The (low + intermediate)-risk EEAs and low-risk EEAs had significantly smaller tumor volumes and higher minADCs, meanADCs, P10, P25, P50, P75, and P90 ADCs than the (high-intermediate + high)-risk EEAs and non-low-risk EEAs (all P < 0.05), respectively. The combined models of the (meanADC + volume) and the (P75 ADC + volume) yielded the largest AUCs of 0.775 and 0.780 in identifying the (low + intermediate)- and the low-risk EEAs from the other EEAs, respectively.

CONCLUSION

Whole-tumor ADC histogram metrics might be helpful for preoperatively identifying low- and (low + intermediate)-risk EEAs, facilitating personalized therapeutic planning.

Diffusion kurtosis imaging with multiple quantitative parameters for predicting microsatellite instability status of endometrial carcinoma

PURPOSE

To explore the value of Diffusion kurtosis imaging (DKI) with multiple quantitative parameters in predicting microsatellite instability (MSI) status in endometrial carcinoma (EC).

METHODS

Data of 38 patients with EC were retrospectively analyzed, including 12 MSI and 26 microsatellite stability (MSS). All patients underwent preoperative 1.5T MR examination. The quantitative values of the DKI sequence in the tumor parenchyma of the two groups, including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), fractional anisotropy (FA), fractional anisotropy of kurtosis (FAk), mean diffusivity (MD), axial diffusivity (Da), and radial diffusivity (Dr) were measured by two observers, respectively.

RESULTS

The MK, Ka, Kr, FA, FAk, MD, Da, and Dr values of the MSI group were 1.074 ± 0.162, 1.253 ± 0.229, 0.886 ± 0.205, 0.207 ± 0.041, 0.397 ± 0.129, 0.890 ± 0.158 μm2/ms, 1.083 ± 0.218 μm2/ms, and 0.793 ± 0.133 μm2/ms, and 0.956 (0.889,1.002), 1.048 ± 0.211, 0.831 ± 0.099, 0.188 ± 0.061, 0.334 (0.241,0.410), 1.043 ± 0.217 μm2/ms, 1.235 ± 0.229 μm2/ms, and 0.946 ± 0.215 μm2/ms in the MSS group. The MK and Ka values of the MSI group were higher than those of the MSS group (P<0.05), while the MD and Dr values were lower than those of the MSS group (P<0.05). The AUC of MK, Ka, MD, and Dr values in predicting MSI status of EC was 0.763, 0.729, 0.731, 0.748, respectively. The sensitivity was 58.3%, 50.0%, 65.4%, 61.5%, and the specificity was 96.2%, 92.3%, 75.0%, 83.3%, respectively.

CONCLUSION

DKI can provide multiple quantitative parameters for predicting the MSI status of EC, and assist gynecologist to optimize the treatment plan for the patients.

Radiomics nomogram in aiding preoperatively dilatation and curettage in differentiating type II and type I endometrial cancer

AIM

To established a radiomics nomogram for improving the dilatation and curettage (D&C) result in differentiating type II from type I endometrial cancer (EC) preoperatively.

MATERIAL AND METHODS

EC patients (n=875) were enrolled retrospectively and divided randomly into a training cohort (n=437) and a test cohort (n=438), according to the ratio of 1:1. Radiomics signatures were extracted and selected from apparent diffusion coefficient (ADC) maps. A multivariate logistic regression analysis was used to identify the independent clinical risk factors. An ADC based-radiomics nomogram was built by integrating the selected radiomics signatures and the independent clinical risk factors. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the radiomics nomogram. The net reclassification index (NRI) and total integrated discrimination index (IDI) were calculated to compare the discrimination performances between the radiomics nomogram and the D&C result.

RESULTS

Receiver operating characteristic (ROC) curves showed that the clinical risk factors, the D&C, and the ADC based-radiomics nomogram yielded areas under the ROC curves (AUCs) of 0.70 (95% CI: 0.64-0.76), 0.85 (95% CI: 0.80-0.89), and 0.93 (95% CI: 0.90-0.96) in the training cohort and 0.64 (95% CI: 0.57-0.71), 0.82 (95% CI: 0.77-0.87) and 0.91 (95% CI: 0.87-0.95) in the test cohort, respectively. The DCA, NRI, and IDI demonstrated the clinically usefulness of the ADC based-radiomics nomogram.

CONCLUSION

The ADC-based radiomics nomogram could be used to improve the D&C result in differentiating type II from type I EC preoperatively.

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.

Nomograms of Combining Apparent Diffusion Coefficient Value and Radiomics for Preoperative Risk Evaluation in Endometrial Carcinoma

Objectives

To evaluate the value of nomogram models combining apparent diffusion coefficient (ADC) value and radiomic features on magnetic resonance imaging (MRI) in predicting the type, grade, deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) of endometrial carcinoma (EC) preoperatively.

Methods

This study included 210 EC patients. ADC value was calculated, and radiomic features were measured on T2-weighted images. The univariate and multivariate logistic regressions and cross-validations were performed to reduce valueless features, then radiomics signatures were developed. Nomogram models using ADC combined with radiomic features were developed in the training cohort. The receiver operating characteristic (ROC) curve was performed to estimate the diagnostic efficiency of nomogram models by the area under the curve (AUC) in the training and validation cohorts.

Results

The ADC value was significantly different between each subgroup. Radiomic features were ultimately limited to four features for type, six features for grade, six features for DMI, four features for LVSI, and eight features for LNM for the nomogram models. The AUC of the nomogram model combining ADC value and radiomic features in the training and validation cohorts was 0.851 and 0.867 for type, 0.959 and 0.880 for grade, 0.839 and 0.766 for DMI, 0.816 and 0.746 for LVSI, and 0.910 and 0.897 for LNM.

Conclusions

The nomogram models of ADC value combined with radiomic features were associated with the type, grade, DMI, LVSI, and LNM of EC, and provide an effective, non-invasive method to evaluate preoperative risk stratification for EC.

Volumetric ADC histogram analysis for preoperative evaluation of LVSI status in stage I endometrioid adenocarcinoma

OBJECTIVES

To investigate the value of volumetric ADC histogram metrics for evaluating lymphovascular space invasion (LVSI) status in stage I endometrioid adenocarcinoma (EAC).

METHODS

Preoperative MRI of 227 patients with stage I EAC were retrospectively analyzed. ADC histogram data were derived from the whole tumor with ROIs drawn on all slices of DWI scans (b = 0, 1000 s/mm²). The Student t-test was performed to compare ADC histogram metrics (minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC; skewness; and kurtosis) between the LVSI-positive and LVSI-negative groups, as well as between stage Ia and Ib EACs. ROC curve analysis was carried out to evaluate the diagnostic performance of ADC histogram metrics in predicting LVSI status in EAC.

RESULTS

The minADC and meanADC and 10th, 25th, 50th, 75th, and 90th percentiles of ADC were significantly lower in LVSI-positive EACs compared with those in the LVSI-negative groups for stage I, Ia, and Ib EACs (all p < 0.05). MeanADC ≤ 0.857 × 10^-3 mm²/s, meanADC ≤ 0.854 × 10^-3 mm²/s, and the 90th percentile of ADC ≤ 1.06 × 10^-3 mm²/s yielded the largest AUC of 0.844, 0.844, and 0.849 for evaluating LVSI positivity in stage I, Ia, and Ib tumors, respectively, with sensitivity of 75.4%, 75.0%, and 76.2%; specificity of 80.0%, 83.1%, and 82.1%; and accuracy of 79.3%, 81.5%, and 79.6%, respectively.

CONCLUSION

Volumetric ADC histogram metrics might be helpful for the preoperative evaluation of LVSI status and personalized clinical management in patients with stage I EAC.

KEY POINTS

• Volumetric ADC histogram analysis helps evaluate LVSI status preoperatively. • LVSI-positive EAC is associated with a reduction in multiple volumetric ADC histogram metrics. • MeanADC and the 90th percentile of ADC were shown to be best in evaluating LVSI- positivity in stage Ia and Ib EACs, respectively.

The role of volumetric ADC histogram analysis in preoperatively evaluating the tumour subtype and grade of endometrial cancer

PURPOSE

To assess the value of volumetric ADC histogram metrics in evaluating the histological subtype and grade of endometrial cancer.

METHOD

Preoperative MRI datasets of 317 patients with endometrial cancer were used to obtain volumetric ADC histogram metrics (tumour volume; minADC, maxADC and meanADC; 10th, 25th, 50th, 75th and 90th percentiles of ADC; skewness; and kurtosis). The Mann-Whitney test or Student's t-test was used to compare the difference in ADC histogram metrics between endometrioid adenocarcinomas (EACs) and serous endometrial cancers (SECs) and between different tumour grades (G1, G2, G3). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the performance of ADC histogram metrics or combined models in predicting the tumour subtype and grade.

RESULTS

SECs showed a significantly larger tumour volume (P < 0.001) and lower meanADC, 50th, 75th and 90th percentiles of ADC than EACs (all P < 0.05). MinADC, maxADC, meanADC, 10th, 25th, 50th, 75th, 90th percentiles of ADC were significantly higher in G1 than in G2 and G3 EACs (all P < 0.05), while were not significantly different between G2 and G3 EACs (all P > 0.05). A tumour volume ≥ 7.752 cm³ allowed for the prediction of SECs, with an AUC of 0.765 (0.714-0.810). A meanADC ≥ 0.892 × 10^-3  mm²/s enabled to discriminate G1 from G2 and G3 EACs, with an AUC of 0.818 (0.769-0.861).

CONCLUSION

Volumetric ADC histogram analysis is helpful for non-invasive preoperatively predicting the subtype of endometrial cancer and differentiating G1 from G2 and G3 EACs.

Diffusion-tensor imaging for differentiating uterine sarcoma from degenerative uterine fibroids

AIM

To explore the applicability of diffusion-tensor imaging (DTI) sequence quantitative parameters in differentiating uterine sarcoma (USr) from degenerative uterine fibroids (DUF).

MATERIALS AND METHODS

Fourteen cases of USr and 30 cases of DUF were analysed retrospectively. The diffusion-weighted imaging (DWI) and DTI images were analysed by two observers using Functool software on a ADW4.6 workstation. The images were post-processed to generate an apparent diffusion coefficient (ADC) map of DWI, ADC map of DTI (ADC^T map), and fractional anisotropy (FA) map. Three regions of interest (ROI) were selected from the ADC, ADC^T, and FA maps to obtain the ADC, ADC^T, and FA values. The receiver operating characteristic (ROC) curves of all parameters were used to analyse and compare the diagnostic value of USr and DUF.

RESULTS

The ADC value, ADC^T value, and FA value of USr (1.190 ± 0.262 × 10^-3mm²/s, 1.165 ± 0.270 × 10^-9mm²/s, 0.168 ± 0.063) were significantly lower compared to the values for DUF (1.525 ± 0.314 × 10^-3mm²/s, 1.650 ± 0.332 × 10^-9mm²/s, 0.254 ± 0.111; all p<0.001). The diagnostic threshold values for USr were: ADC ≤1.290 × 10^-3mm²/s, ADC^T ≤1.322 × 10^-9mm²/s and FA ≤0.192. The corresponding sensitivities and specificities were 78.6%/90%, 96.7%/92.9%, and 86.7%/85.7%, respectively. The areas under the curve (AUC) were 0.875, 0.974, and 0.831, respectively.

CONCLUSIONS

DTI quantitative parameters can be used to differentiate USr from DUF. The ADC^T value had the highest diagnostic efficacy.