Multi-b-value diffusion weighted imaging for preoperative evaluation of risk stratification in early-stage endometrial cancer

Diagnostic utility of apparent diffusion coefficient in preoperative assessment of endometrial cancer: are we ready for the 2023 FIGO staging?

BACKGROUND

Although endometrial cancer (EC) is staged surgically, magnetic resonance imaging (MRI) plays a critical role in assessing and selecting the most appropriate treatment planning. We aimed to assess the diagnostic performance of quantitative analysis of diffusion-weighted imaging (DWI) in preoperative assessment of EC.

METHODS

Prospective analysis was done for sixty-eight patients with pathology-proven endometrial cancer who underwent MRI and DWI. Apparent diffusion coefficient (ADC) values were measured by two independent radiologists and compared with the postoperative pathological results.

RESULTS

There was excellent inter-observer reliability in measuring ADCmean values. There were statistically significant lower ADCmean values in patients with deep myometrial invasion (MI), cervical stromal invasion (CSI), type II EC, and lympho-vascular space involvement (LVSI) (AUC = 0.717, 0.816, 0.999, and 0.735 respectively) with optimal cut-off values of ≤ 0.84, ≤ 0.84, ≤ 0.78 and ≤ 0.82 mm2/s respectively. Also, there was a statistically significant negative correlation between ADC values and the updated 2023 FIGO stage and tumor grade (strong association), and the 2009 FIGO stage (medium association).

CONCLUSIONS

The preoperative ADCmean values of EC were significantly correlated with main prognostic factors including depth of MI, CSI, EC type, grade, nodal involvement, and LVSI.

Oscillating Gradient Diffusion-Weighted MRI for Risk Stratification of Uterine Endometrial Cancer

BACKGROUND

Oscillating gradient diffusion-weighted imaging (DWI) enables elucidation of microstructural characteristics in cancers; however, there are limited data to evaluate its utility in patients with endometrial cancer.

PURPOSE

To investigate the utility of oscillating gradient DWI for risk stratification in patients with uterine endometrial cancer compared with conventional pulsed gradient DWI.

STUDY TYPE

Retrospective.

SUBJECTS

Sixty-three women (mean age: 58 [range: 32-85] years) with endometrial cancer.

FIELD STRENGTH/SEQUENCE

3 T MRI including DWI using oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE) research sequences.

ASSESSMENT

Mean value of the apparent diffusion coefficient (ADC) values for OGSE (ADCOGSE) and PGSE (ADCPGSE) as well as the ADC ratio (ADCOGSE/ADCPGSE) within endometrial cancer were measured using regions of interest. Prognostic factors (histological grade, deep myometrial invasion, lymphovascular invasion, International Federation of Gynecology and Obstetrics [FIGO] stage, and prognostic risk classification) were tabulated.

STATISTICAL TESTS

Interobserver agreement was analyzed by calculating the intraclass correlation coefficient. The associations of ADCOGSE, ADCPGSE, and ADCOGSE/ADCPGSE with prognostic factors were examined using the Kendall rank correlation coefficient, Mann-Whitney U test, and receiver operating characteristic (ROC) curve. A P value of <0.05 was statistically significant.

RESULTS

Compared with ADCOGSE and ADCPGSE, ADCOGSE/ADCPGSE was significantly and strongly correlated with histological grade (observer 1, τ = 0.563; observer 2, τ = 0.456), FIGO stage (observer 1, τ = 0.354; observer 2, τ = 0.324), and prognostic risk classification (observer 1, τ = 0.456; observer 2, τ = 0.385). The area under the ROC curves of ADCOGSE/ADCPGSE for histological grade (observer 1, 0.92, 95% confidence intervals [CIs]: 0.83-0.98; observer 2, 0.84, 95% CI: 0.73-0.92) and prognostic risk (observer 1, 0.80, 95% CI: 0.68-0.89; observer 2, 0.76, 95% CI: 0.63-0.86) were significantly higher than that of ADCOGSE and ADCPGSE.

DATA CONCLUSION

The ADC ratio obtained via oscillating gradient and pulsed gradient DWIs might be useful imaging biomarkers for risk stratification in patients with endometrial cancer.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

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.

Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model

Endometrial carcinoma (EC) risk stratification prior to surgery is crucial for clinical treatment. In this study, we intend to evaluate the predictive value of radiomics models based on magnetic resonance imaging (MRI) for risk stratification and staging of early-stage EC. The study included 155 patients who underwent MRI examinations prior to surgery and were pathologically diagnosed with early-stage EC between January, 2020, and September, 2022. Three-dimensional radiomics features were extracted from segmented tumor images captured by MRI scans (including T2WI, CE-T1WI delayed phase, and ADC), with 1521 features extracted from each of the three modalities. Then, using five-fold cross-validation and a multilayer perceptron algorithm, these features were filtered using Pearson's correlation coefficient to develop a prediction model for risk stratification and staging of EC. The performance of each model was assessed by analyzing ROC curves and calculating the AUC, accuracy, sensitivity, and specificity. In terms of risk stratification, the CE-T1 sequence demonstrated the highest predictive accuracy of 0.858 ± 0.025 and an AUC of 0.878 ± 0.042 among the three sequences. However, combining all three sequences resulted in enhanced predictive accuracy, reaching 0.881 ± 0.040, with a corresponding increase in the AUC to 0.862 ± 0.069. In the context of staging, the utilization of a combination involving T2WI with CE-T1WI led to a notably elevated predictive accuracy of 0.956 ± 0.020, surpassing the accuracy achieved when employing any singular feature. Correspondingly, the AUC was 0.979 ± 0.022. When incorporating all three sequences concurrently, the predictive accuracy reached 0.956 ± 0.000, accompanied by an AUC of 0.986 ± 0.007. It is noteworthy that this level of accuracy surpassed that of the radiologist, which stood at 0.832. The MRI radiomics model has the potential to accurately predict the risk stratification and early staging of EC.

Quantitative dynamic contrast-enhanced parameters and intravoxel incoherent motion facilitate the prediction of TP53 status and risk stratification of early-stage endometrial carcinoma

BACKGROUND

The aim of the study was to investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and intravoxel incoherent motion (IVIM) in differentiating TP53-mutant from wild type, low-risk from non-low-risk early-stage endometrial carcinoma (EC).

PATIENTS AND METHODS

A total of 74 EC patients underwent pelvic MRI. Parameters volume transfer constant (K^trans), rate transfer constant (K_ep), the volume of extravascular extracellular space per unit volume of tissue (V_e), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and microvascular volume fraction (f) were compared. The combination of parameters was investigated by logistic regression and evaluated by bootstrap (1000 samples), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

In the TP53-mutant group, K^trans and K_ep were higher and D was lower than in the TP53-wild group; K^trans, V_e, f, and D were lower in the non-low-risk group than in the low-risk group (all P < 0.05). In the identification of TP53-mutant and TP53-wild early-stage EC, K^trans and D were independent predictors, and the combination of them had an optimal diagnostic efficacy (AUC, 0.867; sensitivity, 92.00%; specificity, 80.95%), which was significantly better than D (Z = 2.169, P = 0.030) and K^trans (Z = 2.572, P = 0.010). In the identification of low-risk and non-low-risk early-stage EC, K^trans, V_e, and f were independent predictors, and the combination of them had an optimal diagnostic efficacy (AUC, 0.947; sensitivity, 83.33%; specificity, 93.18%), which was significantly better than D (Z = 3.113, P = 0.002), f (Z = 4.317, P < 0.001), K^trans (Z = 2.713, P = 0.007), and V_e (Z = 3.175, P = 0.002). The calibration curves showed that the above two combinations of independent predictors, both have good consistency, and DCA showed that these combinations were reliable clinical prediction tools.

CONCLUSIONS

Both DCE-MRI and IVIM facilitate the prediction of TP53 status and risk stratification in early-stage EC. Compare with each single parameter, the combination of independent predictors provided better predictive power and may serve as a superior imaging marker.

Improved differentiation between endometrial carcinoma and endometrial polyp with combination of APTw and IVIM MR imaging

PURPOSE

To assess the value of amide proton transfer weighted (APTw) combined with intra-voxel-incoherent-motion (IVIM) imaging in differential diagnosis of stage I-II endometrial carcinoma (EC) and endometrial polyp (EP).

METHODS

A total of 53 female patients (37 cases with EC and 16 cases with EP) confirmed by surgical resection or biopsy from June 2019 to Jan. 2022 were retrospectively reviewed. All patients underwent 3.0 T magnetic resonance imaging (MRI) examination including diffusion weighted imaging (DWI), APTw and IVIM scans. The pure diffusion coefficient (D), pseudo-diffusion coefficient (D^⁎), perfusion fraction (f), apparent diffusion coefficient (ADC) and APT values were independently measured by two observers. Intra-class correlation coefficients (ICC) were used to test the consistency of measurements by the two observers. Mann-Whitney U test was performed to analyze the difference of each parameter between EC and EP groups. Receiver operator characteristic (ROC) analysis was performed, and the Delong test was used for ROC curve comparison. Pearson's correlation analysis was used to assess the correlation between APTw and IVIM parameters.

RESULTS

There was no significant difference in clinical manifestations between the two groups (P > 0.05). APT and D^⁎ values of the EC group were significantly higher than those of the EP group [APT: 2.64 ± 0.50% vs. 2.05 ± 0.58%; and D^⁎: (54.06 ± 36.06) × 10^-3 mm²/s vs. (30.54 ± 16.67) × 10^-3 mm²/s]. D, f and ADC values of EC group were significantly lower than those of EP group [D: 0.62(0.53,0.76) × 10^-3 mm²/s vs. (1.45 ± 0.48) × 10^-3 mm²/s; f: 22.18 ± 8.08% vs. 30.80 ± 8.92%; and ADC: (0.88 ± 0.16) × 10^-3 mm²/s vs. (1.57 ± 0.43) × 10^-3 mm²/s]. The area under ROC curves were observed as: AUC (IVIM+APT) > AUC (D) > AUC (ADC) > AUC (APT) > AUC (f) > AUC (D^⁎). Delong test suggested statistical significance between AUC by APT and D, D and D^⁎, D and f, D^⁎ and ADC, APT and com(IVIM+APT), D^⁎ and com(IVIM+APT), as well as f and com(IVIM+APT). No significant correlation between the APT and IVIM parameters was observed in either EC or EP group.

CONCLUSION

Both APT and IVIM parameters showed statistical differences between EC and EP. With combination of APT and IVIM parameters, the diagnostic accuracy between EC and EP can be significantly improved.

A nomogram for preoperative risk stratification based on MR morphological parameters in patients with endometrioid adenocarcinoma

PURPOSE

To develop and validate a nomogram based on MRI morphological parameters to preoperatively discriminate between low-risk and non-low-risk patients with endometrioid endometrial carcinoma (EEC).

METHODS

Two hundred eighty-one women with histologically confirmed EEC were divided into training (1.5-T MRI, n = 182) and validation cohorts (3.0-T MRI, n = 99). According to the European Society of Medical Oncology guidelines, the patients were divided into four risk groups: low, intermediate, high-intermediate, and high. Binary classification models were developed (low-risk vs. non-low-risk). Univariate logistic regression (LR) analyses were used to determine which variables to select to build the predictive models. Five classification models were constructed, and the best model was selected. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and nomogram. P < 0.05 indicated a statistically significant difference.

RESULTS

Age and four morphological parameters (tumor size, tumor volume, maximum anteroposterior tumor diameter on sagittal T2-weighted images (APsag), and tumor area ratio (TAR)) were selected, and the LR model was used to construct an MRI morphological nomogram. The AUCs for the nomogram in predicting a non-low-risk of EEC among patients in the training and validation cohorts were 0.856 (sensitivity = 75.0%, specificity = 83.1%) and 0.849 (sensitivity = 74.6%, specificity = 85.0%), respectively.

CONCLUSION

An MRI morphological nomogram was developed and achieved high diagnostic performance for classifying low-risk and non-low-risk EEC preoperatively, which could provide support for therapeutic decision-making. Furthermore, our findings indicate that this nomogram is robust in the clinical application of various field strength data.

Magnetic resonance spectroscopy associations with clinicopathologic features of estrogen-dependent endometrial cancer

BACKGROUND

We studied the magnetic resonance spectroscopy (MRS) associations with clinicopathologic features of estrogen-dependent endometrial cancer (type I EC).

METHODS

Totally 45 patients with type I EC who underwent preoperative multi-voxel MRS at 3.0 T were enrolled. The mean ratio of the Cho peak integral to the unsuppressed water peak integral (Cho/water) of the tumor was calculated. The Cho/water and apparent diffusion coefficient (ADC) of type I EC with and without local invasion, as well as with different levels of Ki-67 staining index (SI) (≤ 40% and > 40%), were compared. Correlation test was used to examine the relationship of Cho/water, as well as mean ADC, with Ki-67 SI, tumor stage, and tumor grade.

RESULTS

The mean Cho/water of EC with Ki-67 SI ≤ 40% (2.28 ± 0.93) × 10^-3 was lower than that with Ki-67 SI > 40% (4.08 ± 1.00) × 10^-3 (P < 0.001). The mean Cho/water of EC with deep and superficial myometrial invasion was (3.41 ± 1.26) × 10^-3 and (2.43 ± 1.11) × 10^-3, respectively (P = 0.011). There was no significant difference in Cho/water between type I EC with and without cervical invasioin ([2.68 ± 1.00] × 10^-3 and [2.77 ± 1.28] × 10^-3, P = 0.866). The mean Cho/water of type I EC with and without lymph node metastasis was (4.02 ± 1.90) × 10^-3 and (2.60 ± 1.06) × 10^-3, respectively (P = 0.014). The Cho/water was positively correlated with the Ki-67 SI (r = 0.701, P < 0.001). There were no significant differences in ADC among groups (all P > 0.05).

CONCLUSION

MRS is helpful for preoperative assessment of clinicopathological features of type I EC.

Diffusion Weighted Imaging in the Assessment of Tumor Grade in Endometrial Cancer Based on Intravoxel Incoherent Motion MRI

The aim of this study is to investigate the possibility of predicting histological grade in patients with endometrial cancer on the basis of intravoxel incoherent motion (IVIM)-related histogram analysis parameters. This prospective study included 52 women with endometrial cancer (EC) who underwent MR imaging as initial staging in our hospital, allocated into low-grade (G1 and G2) and high-grade (G3) tumors according to the pathology reports. Regions of interest (ROIs) were drawn on the diffusion weighted images and apparent diffusion coefficient (ADC), true diffusivity (D), and perfusion fraction (f) using diffusion models were computed. Mean, median, skewness, kurtosis, and interquartile range (IQR) were calculated from the whole-tumor histogram. The IQR of the diffusion coefficient (D) was significantly lower in the low-grade tumors from that of the high-grade group with an adjusted p-value of less than 5% (0.048). The ROC curve analysis results of the statistically significant IQR of the D yielded an accuracy, sensitivity, and specificity of 74.5%, 70.1%, and 76.5% respectively, for discriminating low from high-grade tumors, with an optimal cutoff of 0.206 (×10⁻³ mm²/s) and an AUC of 75.4% (95% CI: 62.1 to 88.8). The IVIM modeling coupled with histogram analysis techniques is promising for preoperative differentiation between low- and high-grade EC tumors.

The Feasibility of Combining ADC Value With Texture Analysis of T2WI, DWI and CE-T1WI to Preoperatively Predict the Expression Levels of Ki-67 and p53 of Endometrial Carcinoma

PURPOSE

To evaluate the feasibility of apparent diffusion coefficient (ADC) value combined with texture analysis (TA) in preoperatively predicting the expression levels of Ki-67 and p53 in endometrial carcinoma (EC) patients.

METHODS

Clinical, pathological and MRI findings of 110 EC patients were analyzed retrospectively. The expression levels of Ki-67 and p53 in EC tissues were detected by immunohistochemistry. ADC value was calculated, and three-dimensional (3D) texture features were measured on T₂-weighted images (T₂WI), diffusion-weighted images (DWI), and contrast-enhanced T₁-weighted images (CE-T₁WI). The univariate and multivariate logistic regression and cross-validations were used for the selection of texture features. The receiver operating characteristic (ROC) curve was performed to estimate the diagnostic efficiency of prediction model by the area under the curve (AUC) in the training and validation cohorts.

RESULTS

Significant differences of the ADC values were found in predicting Ki-67 and p53 (P=0.039, P=0.007). The AUC of the ADC value in predicting the expression levels of Ki-67 and p53 were 0.698, 0.853 and 0.626, 0.702 in the training and validation cohorts. The AUC of the TA model based on T₂WI, DWI, CE-T₁WI, and ADC value combined with T₂WI + DWI + CE-T₁WI in the training and validation cohorts for predicting the expression of Ki-67 were 0.741, 0.765, 0.733, 0.922 and 0.688, 0.691, 0.651, 0.938, respectively, and for predicting the expression of p53 were 0.763, 0.805, 0.781, 0.901 and 0.796, 0.713, 0.657, 0.922, respectively.

CONCLUSION

ADC values combined with TA are beneficial for predicting the expression levels of Ki-67 and p53 in EC patients before surgery, and they provide higher auxiliary diagnostic values for clinical application.

Pathological characteristics and risk stratification in patients with stage I endometrial cancer: utility of apparent diffusion coefficient histogram analysis

OBJECTIVES

Accurate pre-operative prediction of risk stratification using a non-invasive imaging tool is clinically important for planning optimal treatment strategies, particularly in early-stage endometrial cancer (EC). This study aimed to investigate the utility of apparent diffusion coefficient (ADC) histogram analysis in evaluating the pathological characteristics and risk stratification in patients with Stage I EC.

METHODS

Between October 2009 and December 2014, a total of 108 patients with surgically proven Stage I EC (endometrioid type = 91; non-endometrioid type = 17) excluding stage ≥II that underwent preoperative 3T-diffusion-weighted imaging without administration of contrast medium were enrolled in this retrospective study. Risk stratification was divided into four risk categories based on the ESMO-ESGO-ESTRO Guidelines: low, intermediate, high-intermediate, and high risk. The ADC histogram parameters (minimum, mean [ADCmean], 10th-90th percentile, and maximum [ADCmax]) of the tumor were generated using an in-house software. The ADC histogram parameters were compared between patients with endometrioid type and non-endometrioid type, between Stage IA and IB, between histological grades, and evaluated for differentiating non-high risk group from high risk group. Inter-reader agreement for tumor ADC measurements was also evaluated. Statistical analyses were performed using the Student's t-test, Mann-Whitney U test, receiver operating characteristics (ROC) analysis, or intraclass correlation coefficient (ICC).

RESULTS

In differentiating endometrioid type from non-endometrioid type EC, all ADC histogram parameters were statistically significant (p < 0.05). In differentiating histological grades, 90th percentile ADC and ADCmax showed significantly higher values in tumor Grade III than in tumor Grade I-II (p < 0.05). In differentiating superficial myometrial invasion from deep myometrial invasion, all ADC histogram parameters were statistically significant (p < 0.05), except ADCmax. In differentiating non-high risk group from high risk group, ADCmean, 75th-90th percentile ADC, and ADCmax were statistically significant (p < 0.05). For predicting the high risk group, the area under the ROC curve of ADCmax was 0.628 and the highest among other histogram parameters. All histogram parameters revealed moderate to good inter-reader reliability (ICC = 0.581‒0.769).

CONCLUSION

The ADC histogram analysis as reproducible tool may be useful for evaluating the pathological characteristics and risk stratification in patients with early-stage EC.

ADVANCES IN KNOWLEDGE

ADC histogram analysis may be useful for evaluating risk stratification in early-stage endometrial cancer patients.

Preoperative MRI and immunohistochemical examination for the prediction of high-risk endometrial cancer

Background

Magnetic resonance imaging (MRI) and immunohistochemical (IHC) examination provides useful information for the risk stratification of endometrial cancer (EC). However, the use of the combination of MRI and IHC for the prediction of high-risk EC is controversial. The aim of this study was to evaluate the value of preoperative MRI and IHC examination in prediction of patients with high-risk EC.

Methods

This retrospective case-control study was conducted from January 1, 2018 to May 1, 2021 at two hospitals. A primary cohort (n=102) comprised patients with histologically confirmed EC in one hospital between January 1, 2018 and May 31, 2020. An additional external cohort (n=35) comprising patients with histologically confirmed EC in a different hospital from January 1, 2020 to May 1, 2021 was included for validation. Imaging features including tumor size, tumor margin, relative T2 value, tumor signal intensity on diffusion-weighted imaging (DWI), T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) were determined from preoperative MRI images. IHC markers including ER, PR, p53 and Ki67 were determined through IHC staining of preoperative curettage specimen. Patients were divided into high-risk and low-intermediate- risk group based on the final histological results. Differences between categorical and numerical variables were assessed using chi-square test and independent-sample t-test, respectively. Multivariate binary logistic regression analyses were used for construction of the prediction model A fusion prediction model was constructed by combining MRI features and IHC markers. The predictive performance of the model was then validated using the external cohort.

Results

Imaging and IHC markers were significantly associated with risk ranks. Model 1 based on MRI features showed an area under the curve (AUC) of 0.822 [95% confidence interval (CI), 0.741-0.903] whereas Model 2 based on IHC markers showed an AUC of 0.894 (95% CI, 0.829-0.960). Notably, model 3 integrating independent MRI and IHC risk factors demonstrated good calibration and high differentiation ability with an AUC of 0.958 (95% CI, 0.923-0.993), and showed good discrimination with an AUC of 0.84 (95% CI, 0.677-0.942) using the external validation set.

Conclusions

This study proposes a comprehensive predictive model comprising MRI and IHC features as a powerful tool for preoperative risk stratification to assist in clinical decision-making for EC patients.

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.

Quantitative diffusion and perfusion MRI in the evaluation of endometrial cancer: validation with histopathological parameters

Objectives:

To investigate the role of quantitative Magnetic Resonance Imaging (MRI) in preoperative assessment of tumour aggressiveness in patients with endometrial cancer, correlating multiple parameters obtained from diffusion and dynamic contrast-enhanced (DCE) MR sequences with conventional histopathological prognostic factors and inflammatory tumour infiltrate.

Methods:

Forty-four patients with biopsy-proven endometrial cancer underwent preoperative MR imaging at 3T scanner, including DCE imaging, diffusion-weighted imaging (DWI) and intravoxel incoherent motion imaging (IVIM). Images were analysed on dedicated post-processing workstations and quantitative parameters were extracted: K_trans, K_ep, V_e and AUC from the DCE; ADC from DWI; diffusion D, pseudo diffusion D*, perfusion fraction f from IVIM and tumour volume from DWI. The following histopathological data were obtained after surgery: histological type, grading (G), lympho-vascular invasion (LVI), lymph node status, FIGO stage and inflammatory infiltrate.

Results:

ADC was significantly higher in endometrioid histology, G1-G2 (low grade), and stage IA. Significantly higher D* were found in endometrioid subptype, negative lymph nodes and stage IA. The absence of LVI is associated with higher f values. K_trans and V_e values were significantly higher in low grade. Higher D*, f and AUC occur with the presence of chronic inflammatory cells, D * was also able to distinguish chronic from mixed type of inflammation. Larger volume was significantly correlated with the presence of mixed-type inflammation, LVI, positive lymph nodes and stage ≥IB.

Conclusions:

Quantitative biomarkers obtained from pre-operative DWI, IVIM and DCE-MR examination are an in vivo representation of the physiological and microstructural characteristics of endometrial carcinoma allowing to obtain the fundamental parameters for stratification into Risk Classes.

Advances in knowledge:

Quantitative imaging biomarkers obtained from DWI, DCE and IVIM may improve preoperative prognostic stratification in patients with endometrial cancer leading to a more informed therapeutic choice.

Amide Proton Transfer-Weighted Imaging and Multiple Models Diffusion-Weighted Imaging Facilitates Preoperative Risk Stratification of Early-Stage Endometrial Carcinoma

BACKGROUND

Endometrial carcinoma (EC) risk stratification is generally based on histological assessment. It would be beneficial to perform risk stratification noninvasively by MRI.

PURPOSE

To investigate the application of amide proton transfer-weighted imaging (APTWI), monoexponential, biexponential, and stretched exponential intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for the evaluation of risk stratification in early-stage EC.

STUDY TYPE

Prospective.

POPULATION

Eighty patients with early-stage EC (47 classified as low risk, 20 as medium risk, and 13 as high risk by histological grade and International Federation of Gynecology and Obstetrics stage).

FIELD STRENGTH/SEQUENCE

T1-weighted imaging, T2-weighted imaging, IVIM, APTWI, and DKI MRI at 3 T.

ASSESSMENT

The magnetization transfer ratio asymmetry (MTRasym [3.5 ppm]), apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index (α), mean kurtosis (MK), and mean diffusivity (MD) were calculated and compared between low-risk and non-low-risk groups.

STATISTICAL TESTS

Individual sample t test, analysis of variance, and logistic regression. A P-value <0.05 was considered statistically significant.

RESULTS

The α, ADC, D, DDC, and MD were significantly higher and the f, MK, and MTRasym (3.5 ppm) were significantly lower in the low-risk group than in the non-low-risk group. The difference in D* between the two groups was not significant (P = 0.289). MTRasym (3.5 ppm), D, and MK were independent predictors of risk stratification. The combination of these three parameters was better able to identify low- and non-low-risk groups than each individual parameter.

DATA CONCLUSION

The IVIM, DKI, and APTWI parameters have potential as imaging markers for risk stratification in early-stage EC.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 3.

Current Status of Anatomical Magnetic Resonance Imaging in Brachytherapy and External Beam Radiotherapy Planning and Delivery

Radiotherapy planning and delivery have dramatically improved in recent times. Imaging is key to a successful three-dimensional and increasingly four-dimensional based pathway with computed tomography embedded as the backbone modality. Computed tomography has significant limitations for many tumour sites where soft-tissue discrimination is suboptimal, and where magnetic resonance imaging (MRI) has largely superseded in the diagnostic arena. MRI is increasingly used together with computed tomography in the radiotherapy planning pathway and is now established as a prerequisite for several tumours. With the advent of combined MRI and linear accelerator (MR-linac) systems, a transition to MRI-based radiotherapy planning is becoming reality, with increasing experience and research involving these new platforms. In this overview, we aim to highlight how magnetic resonance-guided imaging has improved radiotherapy, using gynaecological malignancies to illustrate, in both external beam radiotherapy and image-guided brachytherapy, and will assess the early evidence for magnetic resonance-guided radiotherapy using combined MR-linac systems.

High resolution diffusion-weighted imaging with readout segmentation of long variable echo-trains for determining myometrial invasion in endometrial carcinoma

Background

We assessed the image quality of endometrial cancer lesions by readout segmentation of long variable echo-trains (RESOLVE) diffusion-weighted imaging (DWI) compared with that by single-shot echo-planar imaging (SS-EPI) DWI, aimed to explore the value of RESOLVE DWI for determining myometrial invasion and clinical stage in endometrial cancer.

Materials and methods

From April 2017 to March 2018, a total of 30 endometrial cancer patients (mean age 52.8 ± 9.0 years), who had undergone RESOLVE DWI and SS-EPI DWI, were included in the study. The image quality of endometrial carcinoma by two kinds of DWI scanning methods was compared qualitatively and quantitatively. The Spearman rank correlation test was used to assess the correlation of qualitative image quality scores between two readers. The accuracy of two DWI methods in detecting myometrial invasion and staging of endometrial carcinoma was calculated according to postoperative pathological results. The indexes were analyzed including sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV).

Results

The qualitative score of RESOLVE DWI group was superior to SS-EPI DWI group in every aspect of five aspects (all P < 0.001). Interobserver agreement of depiction was good or excellent in two DWI sequences. Signal to noise ratio and contrast to noise ratio values in RESOLVE DWI group were both higher than those in SS-EPI DWI group (P<0.001). No statistical difference of apparent diffusion coefficient value was observed between two DWI groups (P = 0.261). The specificity, accuracy, PPV, and NPV of estimating myometrial invasion by RESOLVE DWI in three cases (intramucosal lesion, <50% superficial invasion and ≥ 50% deep invasion) were all higher than those by SS-EPI DWI for endometrial carcinoma. Especially RESOLVE DWI was valuable in judging <50% superficial invasion (95%CI:0.586, 0.970). No significant difference in accuracy staging was between the two DWI groups (P = 0.125).

Conclusion

RESOLVE DWI can provide higher quality images of endometrial carcinoma than SS-EPI DWI. The high-quality images are helpful for precise assessment of myometrial invasion in endometrial cancer.

Gaussian mixture model-based cluster analysis of apparent diffusion coefficient values: a novel approach to evaluate uterine endometrioid carcinoma grade

OBJECTIVES

The purpose of our study was to perform Gaussian mixture model (GMM)-based cluster analysis of the apparent diffusion coefficient (ADC) data of patients with endometrioid carcinoma, and to evaluate the relationship between histological grade and the ratios of the different clusters in each patient.

METHODS

This retrospective study enrolled 122 patients (training: n = 63; and validation: n = 59) imaged between May 2015 and February 2020. In the training cohort, manual segmentation was performed on the ADC maps to obtain the ADC data of each patient, and these ADC data were summated to obtain the "All-patient" ADC data. Cluster analysis (three clusters) was performed on this All-patient ADC data, and the ADC ranges of each cluster were defined as follows: cluster 1, 490-699 × 10^-6 mm²/s; cluster 2, 700-932 × 10^-6 mm²/s; and cluster 3, over 933 × 10^-6 mm²/s. In the training and validation cohorts, the ADC data of each patient was classified into three clusters according to these ADC ranges. The cluster ratios of each patient were calculated and compared with histological grade.

RESULTS

In the training cohort, a significant positive correlation was found between the cluster 1 ratio and histological grade (ρ = 0.34, p = 0.0059). The cluster 1 ratios of high-grade lesions (grade 3) were significantly higher than those of low-grade lesions (grades 1 and 2) (p = 0.0084). A similar significant positive correlation was found between the cluster 1 ratio and histological grade in the validation cohort (ρ = 0.44, p = 0.0006).

CONCLUSIONS

The cluster 1 ratio containing voxels with low ADC was significantly correlated with the histological grade of endometrioid carcinoma.

KEY POINTS

• We performed Gaussian mixture model (GMM)-based cluster analysis of the apparent diffusion coefficient (ADC) data of patients with endometrioid carcinoma. • The cluster 1 ratio, which included low ADC values, was significantly positive correlated with histological grade in the training and validation cohorts. • The GMM-based cluster analysis of voxel-based ADC data was effective for grading endometrioid carcinoma.

Multiple mathematical models of diffusion-weighted imaging for endometrial cancer characterization: Correlation with prognosis-related risk factors

PURPOSE

To investigate mono-exponential, bi-exponential, and stretched-exponential models of diffusion-weighted imaging (DWI) for evaluation of prognosis-related risk factors of endometrial cancer (EC).

METHOD

Sixty-one consecutive patients with EC who preoperatively underwent pelvic MRI with multiple b value DWI between September 2016 and May 2018 were enrolled. The apparent-diffusion-coefficient (ADC), bi-exponential model parameters (D, D* and f) and stretched-exponential model parameters (DDC and α) were measured and compared to analyze the following prognosis-related risk factors confirmed by pathology: histological grade, depth of myometrial invasion, cervical stromal infiltration (CSI) and lymphovascular invasion (LVSI). A stepwise multilvariate logistic regression and the receiver operating characteristic (ROC) curves were performed for further statistical analysis.

RESULTS

Lower ADC, D, f, and DDC were observed in tumor with high grade compared with a low-grade group, and the largest area under curve (AUC) was obtained when combining f and DDC values. ADC, D, f, DDC, and α were significantly different in patients with deep myometrial invasion (DMI) compared to those without DMI; the combination of f, DDC and α showed the highest AUC. Significantly different ADC and f were found between patients' presence and absence CSI; the f values showed the highest diagnostic performance with an AUC of 0.825. Regarding the LVSI, ADC, D*, f, and DDC were significantly lower in tumors with LVSI compared to those without LVSI; the combination of f and DDC showed the largest AUC.

CONCLUSION

Multiple mathematical DWI models are a useful approach for the prediction of prognosis-related risk factors in EC.