Can reduced field-of-view diffusion sequence help assess microsatellite instability in FIGO stage 1 endometrial cancer?

ZOOMit diffusion kurtosis imaging combined with diffusion weighted imaging for the assessment of microsatellite instability in endometrial cancer

PURPOSE

Detecting microsatellite instability (MSI) plays a key role in the management of endometrial cancer (EC), as it is a critical predictive biomarker for Lynch syndrome or immunotherapy response. A pressing need exists for cost-efficient, broadly accessible tools to aid patient for universal testing. Herein, we investigate the value of ZOOMit diffusion kurtosis imaging (DKI) and diffusion weighted imaging (DWI) based on preoperative pelvic magnetic resonance imaging (MRI) images in assessing MSI in EC.

METHODS

Preoperative MRI examination including ZOOMit DKI and DWI of 81 EC patients were retrospectively analyzed. The apparent diffusion coefficient (ADC), mean kurtosis (MK), mean diffusivity (MD) and the largest tumor size based on MRI images, as well as patients' clinicopathological features were compared and analyzed according to different microsatellite statuses.

RESULTS

Of the 81 patients, 59 (72.8%) who were microsatellite stability (MSS) and 22 (27.2%) who were MSI. Interobserver agreement for the quantitative parameter measurements was excellent (ICC 0.78-0.98). The ADC and MD values were significantly lower, while Ki-67 proliferation level and MK values were significantly higher in the MSI group compared to those of the MSS group. The parameters of MD and MK were independent predictors for determining MSI, and their combination showed better diagnostic efficacy with an area under the receiver operating characteristic curve (AUROC) of 0.860 (95% confidence interval, 0.765, 0.927), although there was no significant difference compared to each individual parameter.

CONCLUSION

The microstructural heterogeneity assessment of ZOOMit DKI allowed for characterizing MSI status in EC. Within the current universal MSI testing paradigm, DKI may provide added value as a potential noninvasive imaging biomarker for preoperative assessment of MSI tumors, thereby facilitating clinical decision-making.

A prediction model based on deep learning and radiomics features of DWI for the assessment of microsatellite instability in endometrial cancer

BACKGROUND

To explore the efficacy of a prediction model based on diffusion-weighted imaging (DWI) features extracted from deep learning (DL) and radiomics combined with clinical parameters and apparent diffusion coefficient (ADC) values to identify microsatellite instability (MSI) in endometrial cancer (EC).

METHODS

This study included a cohort of 116 patients with EC, who were subsequently divided into training (n = 81) and test (n = 35) sets. From DWI, conventional radiomics features and convolutional neural network-based DL features were extracted. Random forest (RF) and logistic regression were adopted as classifiers. DL features, radiomics features, clinical variables, ADC values, and their combinations were applied to establish DL, radiomics, clinical, ADC, and combined models, respectively. The predictive performance was evaluated through the area under the receiver operating characteristic curve (AUC), total integrated discrimination index (IDI), net reclassification index (NRI), calibration curves, and decision curve analysis (DCA).

RESULTS

The optimal predictive model, based on an RF classifier, comprised four DL features, three radiomics features, two clinical variables, and an ADC value. In the training and test sets, this model exhibited AUC values of 0.989 (95% CI: 0.935-1.000) and 0.885 (95% CI: 0.731-0.967), respectively, demonstrating different degrees of improvement compared with the clinical, DL, radiomics, and ADC models (AUC-training = 0.671, 0.873, 0.833, and 0.814, AUC-test = 0.685, 0.783, 0.708, and 0.713, respectively). The NRI and IDI analyses revealed that the combined model resulted in improved risk reclassification of the MSI status compared to the clinical, radiomics, DL, and ADC models. The calibration curves and DCA indicated good consistency and clinical utility of this model, respectively.

CONCLUSIONS

The predictive model based on DWI features extracted from DL and radiomics combined with clinical parameters and ADC values could effectively assess the MSI status in EC.

A comparative study of mono-exponential and advanced diffusion-weighted imaging in differentiating stage IA endometrial carcinoma from benign endometrial lesions

PURPOSE

The purpose of the current investigation is to compare the efficacy of different diffusion models and diffusion kurtosis imaging (DKI) in differentiating stage IA endometrial carcinoma (IAEC) from benign endometrial lesions (BELs).

METHODS

Patients with IAEC, endometrial hyperplasia (EH), or a thickened endometrium confirmed between May 2016 and August 2022 were retrospectively enrolled. All of the patients underwent a preoperative pelvic magnetic resonance imaging (MRI) examination. The apparent diffusion coefficient (ADC) from the mono-exponential model, pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) from the bi-exponential model, distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index from the stretched-exponential model, diffusion coefficient (Dk) and diffusion kurtosis (K) from the DKI model were calculated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficiency.

RESULTS

A total of 90 patients with IAEC and 91 patients with BELs were enrolled. The values of ADC, D, DDC and Dk were significantly lower and D* and K were significantly higher in cases of IAEC (p < 0.05). Multivariate analysis showed that K was the only predictor. The area under the ROC curve of K was 0.864, significantly higher compared with the ADC (0.601), D (0.811), D* (0.638), DDC (0.743) and Dk (0.675). The sensitivity, specificity and accuracy of K were 78.89%, 85.71% and 80.66%, respectively.

CONCLUSION

Advanced diffusion-weighted imaging models have good performance for differentiating IAEC from EH and endometrial thickening. Among all of the diffusion parameters, K showed the best performance and was the only independent predictor. Diffusion kurtosis imaging was defined as the most valuable model in the current context.

Predictive Value of Magnetic Resonance Imaging in Risk Stratification and Molecular Classification of Endometrial Cancer

This study evaluated the magnetic resonance imaging (MRI) findings of endometrial cancer (EC) patients and identified differences based on risk group and molecular classification. The study involved a total of 175 EC patients. The MRI data were retrospectively reviewed and compared based on the risk of recurrence. Additionally, the associations between imaging phenotypes and genomic signatures were assessed. The low-risk and non-low-risk groups (intermediate, high-intermediate, high, metastatic) showed significant differences in tumor diameter (p < 0.001), signal intensity and heterogeneity on diffusion-weighted imaging (DWI) (p = 0.003), deep myometrial invasion (involvement of more than 50% of the myometrium), cervical invasion (p < 0.001), extrauterine extension (p = 0.002), and lymphadenopathy (p = 0.003). Greater diffusion restriction and more heterogeneity on DWI were exhibited in the non-low-risk group than in the low-risk group. Deep myometrial invasion, cervical invasion, extrauterine extension, lymphadenopathy, recurrence, and stage discrepancy were more common in the non-low-risk group (p < 0.001). A significant difference in microsatellite stability status was observed in the heterogeneity of the contrast-enhanced T1-weighted images (p = 0.027). However, no significant differences were found in MRI parameters related to TP53 mutation. MRI features can be valuable predictors for differentiating risk groups in patients with EC. However, further investigations are needed to explore the imaging markers based on molecular classification.

Radiomics analysis of multiparametric MRI for preoperative prediction of microsatellite instability status in endometrial cancer: a dual-center study

Objective

To develop and validate a multiparametric MRI-based radiomics model for prediction of microsatellite instability (MSI) status in patients with endometrial cancer (EC).

Methods

A total of 225 patients from Center I including 158 in the training cohort and 67 in the internal testing cohort, and 132 patients from Center II were included as an external validation cohort. All the patients were pathologically confirmed EC who underwent pelvic MRI before treatment. The MSI status was confirmed by immunohistochemistry (IHC) staining. A total of 4245 features were extracted from T2-weighted imaging (T2WI), contrast enhanced T1-weighted imaging (CE-T1WI) and apparent diffusion coefficient (ADC) maps for each patient. Four feature selection steps were used, and then five machine learning models, including Logistic Regression (LR), k-Nearest Neighbors (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF), were built for MSI status prediction in the training cohort. Receiver operating characteristics (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of these models.

Results

The SVM model showed the best performance with an AUC of 0.905 (95%CI, 0.848-0.961) in the training cohort, and was subsequently validated in the internal testing cohort and external validation cohort, with the corresponding AUCs of 0.875 (95%CI, 0.762-0.988) and 0.862 (95%CI, 0.781-0.942), respectively. The DCA curve demonstrated favorable clinical utility.

Conclusion

We developed and validated a multiparametric MRI-based radiomics model with gratifying performance in predicting MSI status, and could potentially be used to facilitate the decision-making on clinical treatment options in patients with EC.

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.

Field-of-view optimized and constrained undistorted single-shot study of intravoxel incoherent motion and diffusion-weighted imaging of the uterus during the menstrual cycle: a prospective study

PURPOSE

This study aimed to compare the variability of the uterus during the menses phase (MP), follicular phase (FP), and luteal phase (LP) of the menstrual cycle using intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI).

METHODS

This prospective study was conducted at the Guangdong Provincial Hospital of Traditional Chinese Medicine between January 2022 and January 2023. Women of childbearing age (18-45 years) with appropriate progesterone levels were included in this study. Conventional magnetic resonance imaging and IVIM-DWI scans were performed during the MP, FP, and LP. The differences in IVIM-DWI-derived parameters between these phases were then compared, and the overlap was quantitatively described.

RESULTS

The apparent diffusion coefficient (ADC) and pure molecular diffusion coefficient (D) values from the endometrium, uterine junctional zone (UJZ), and myometrium indicated statistical differences between the MP and FP and the MP and LP (ADC: endometrium, both P < 0.001; UJZ, P = 0.008 and P < 0.001, respectively; myometrium, P = 0.033 and P = 0.006, respectively; D: endometrium, both P < 0.001; UJZ, P = 0.008 and P = 0.006, respectively; myometrium, P = 0.041 and P = 0.045, respectively). The perfusion-related diffusion coefficient (D*) values from the myometrium indicated statistical differences between the FP and MP and the FP and LP (D*: myometrium, P = 0.049 and P = 0.009, respectively). The overlapping endometrium ratios between the MP and FP or LP were lower than 50% in the ADC and D values (ADC: overlapping of MP and FP: 33.33%, overlapping of MP and LP: 23.33%; D: overlapping of MP and FP: 40.00%, overlapping of MP and LP: 43.33%).

CONCLUSION

The ADC and IVIM-derived parameters indicated differences in the uterus in diverse phases of the menstrual cycle, especially in the endometrium in relation to ADC and D values.

Amide Proton Transfer-Weighted Imaging Combined With Intravoxel Incoherent Motion for Evaluating Microsatellite Instability in Endometrial Cancer

BACKGROUND

Microsatellite instability (MSI), caused by mismatch repair (MMR) protein defects that lead to uncorrectable mismatch bases, results in the accumulation of gene mutations and ultimately to tumors. Preoperative prediction of MSI can provide a basis for personalized and precise treatment of endometrial cancer (EC) patients.

PURPOSE

To investigate amide proton transfer weighting (APTw) imaging combined with intravoxel incoherent motion (IVIM) in the assessment of MSI in EC.

STUDY TYPE

Retrospective.

POPULATION

A total of 71 patients with EC (12 classified as the MSI group and 22 as the microsatellite stabilization [MSS] group after entering and leaving the group standard).

FIELD STRENGTH/SEQUENCE

A 3.0 T/IVIM, diffusion-weighted imaging (DWI) and APTw.

ASSESSMENT

Amide proton transfer (APT) value, apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo diffusion coefficient (D*), and perfusion fraction (f) were calculated and compared between MSI and MSS groups.

STATISTICAL TESTS

The Kendall's W test; Mann-Whitney U-test; Chi-square test or Fisher's exact test; logistic regression analysis; Area under the receiver operating characteristic (ROC) curve (AUC); The Delong test; Pearson or Spearman correlation coefficients. The significance threshold was set at P < 0.05.

RESULTS

APT and D* values of the MSI group were significantly higher than those of the MSS group. While ADC, D, and f values in the MSI group were significantly lower than those in the MSS group. The multivariate analysis revealed that only APT and D* values were independent predictors to evaluate the MSI status. And the ROC curves indicated that the combination of APT and D* values could distinguish the MSI status of EC with the highest diagnostic efficacy (AUC = 0.973), even without significant difference to those by APT (AUC = 0.894) or D* (AUC = 0.920) value separately (P = 0.149 and 0.078, respectively).

CONCLUSION

Combination of APTw and IVIM imaging may serve as an effective noninvasive method for clinical assessment of MSI in EC.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

The value of magnetic resonance imaging-based tumor shape features for assessing microsatellite instability status in endometrial cancer

BACKGROUND

Microsatellite instability (MSI) status can be used for the classification and risk stratification of endometrial cancer (EC). This study aimed to investigate whether magnetic resonance imaging (MRI)-based tumor shape features can help assess MSI status in EC before surgery.

METHODS

The medical records of 88 EC patients with MSI status were retrospectively reviewed. Quantitative and subjective shape features based on MRI were used to assess MSI status. Variables were compared using the Student's t-test, χ² test, or Wilcoxon rank-sum test where appropriate. Univariate and multivariate analyses were performed by the logistic regression model. The area under the curve (AUC) was used to estimate the discrimination performance of variables.

RESULTS

There were 23 patients with MSI, and 65 patients with microsatellite stability (MSS) in this study. Eccentricity and shape type showed significant differences between MSI and MSS (P=0.039 and P=0.033, respectively). The AUC values of eccentricity, shape type, and the combination of 2 features for assessing MSI were 0.662 [95% confidence interval (CI): 0.554-0.770], 0.627 (95% CI: 0.512-0.743), and 0.727 (95% CI: 0.613-0.842), respectively. Considering the International Federation of Gynecology and Obstetrics (FIGO) staging, eccentricity maintained a significant difference in stages I-II (P=0.039), while there was no statistical difference in stages III-IV (P=0.601).

CONCLUSIONS

It is possible that MRI-based tumor shape features, including eccentricity and shape type, could be promising markers for assessing MSI status. The features may aid in the preliminary screening of EC patients with MSI.

Using amide proton transfer-weighted MRI to non-invasively differentiate mismatch repair deficient and proficient tumors in endometrioid endometrial adenocarcinoma

OBJECTIVES

To investigate the utility of three-dimensional (3D) amide proton transfer-weighted (APTw) imaging to differentiate mismatch repair deficient (dMMR) and mismatch repair proficient (pMMR) tumors in endometrioid endometrial adenocarcinoma (EEA).

METHODS

Forty-nine patients with EEA underwent T1-weighted imaging, T2-weighted imaging, 3D APTw imaging, and diffusion-weighted imaging at 3 T MRI. Image quality and measurement confidence of APTw images were evaluated on a 5-point Likert scale. APTw and apparent diffusion coefficient (ADC) values were calculated and compared between the dMMR and pMMR groups and among the three EEA histologic grades based on the Federation of Gynecology and Obstetrics (FIGO) grading system criteria. Student's t-test, analysis of variance with Scheffe post hoc test, and receiver operating characteristic analysis were performed. Statistical significance was set at p < 0.05.

RESULTS

Thirty-five EEA patients (9 with dMMR tumors and 26 with pMMR tumors) with good image quality were enrolled in quantitative analysis. APTw values were significantly higher in the dMMR group than in the pMMR group (3.2 ± 0.3% and 2.8 ± 0.5%, respectively; p = 0.019). ADC values of the dMMR and pMMR groups were 0.874 ± 0.104 × 10-3 mm2/s and 0.903 ± 0.100 × 10-3 mm2/s, respectively. No significant between-group difference was noted (p = 0.476). No statistically significant differences were observed in APTw values or ADC values among the three histologic grades (p = 0.766 and p = 0.295, respectively).

CONCLUSIONS

APTw values may be used as potential imaging markers to differentiate dMMR from pMMR tumors in EEA.

Three-dimensional turbo-spin-echo amide proton transfer-weighted and intravoxel incoherent motion MR imaging for type I endometrial carcinoma: Correlation with Ki-67 proliferation status

BACKGROUND

To evaluate 3-dimensional amide proton transfer weighted (APTw) imaging for type I endometrial carcinoma (EC), and investigate correlations of Ki-67 labelling index with APTw and intravoxel incoherent motion (IVIM) imaging.

METHODS

54 consecutive patients suspected of endometrial lesions underwent pelvic APTw and IVIM imaging on a 3 T MR scanner. APTw values and IVIM-derived parameters (Dt, D*, f) were independently measured by two radiologists on 22 postoperative pathological confirmed of type I EC lesions. Results were compared between histological grades and Ki-67 proliferation groups. ROC analysis was performed. Pearson's correlation analysis was performed for APTw values and IVIM-derived parameters with Ki-67 labeling index.

RESULTS

APTw values and Dt, D*, f of all type I EC were 2.9 ± 0.1%, 0.677 ± 0.027 × 10-3 mm2/s, 31.801 ± 11.492 × 10-3 mm2/s, 0.179 ± 0.050 with inter-observer ICC 0.996, 0.850, 0.956, 0.995, respectively. APTw values of Ki-67 low-proliferation group (<30%, n = 8) were 2.5 ± 0.2%, significantly lower than the high-proliferation group (>30%, n = 14) with APTw values of 3.1 ± 0.1% (p = 0.016). Area under the curve was 0.768. APTw values of type I EC were moderately positively correlated with Ki-67 labelling index (r = 0.583, p = 0.004). There was no significant difference of Dt (p = 0.843), D* (p = 0.262), f (p = 0.553) between the two groups. No correlation was found between IVIM-derived parameters and Ki-67 labelling index (Dt, p = 0.717; D* p = 0.151; f, p = 0.153).

CONCLUSION

3D TSE APTw imaging is a feasible approach for detecting type I EC. Ki-67 labeling index positively moderately correlates with APTw not with IVIM.

Endometrial Cancer, the Current International Federation of Gynecology and Obstetrics Staging System, and the Role of Imaging

Imaging plays a crucial role in the diagnosis, staging, and follow-up of endometrial cancer. Endometrial cancer is staged surgically using the International Federation of Gynecology and Obstetrics (FIGO) staging system. Preoperative imaging can complement surgical staging but is not yet considered a required component in the current FIGO staging system. Preoperative imaging can help identify some tumor characteristics and tumor spread, both locally and distally. More accurate assessment of endometrial cancers optimizes management and treatment plan, including degree of surgical intervention. In this article, we review the epidemiology, FIGO staging system, and the importance of imaging in the staging of endometrial cancer.

The value of single-source dual-energy CT imaging for discriminating microsatellite instability from microsatellite stability human colorectal cancer

OBJECTIVES

To demonstrate the value of single-source dual-energy computed tomography (ssDECT) imaging for discriminating microsatellite instability (MSI) from microsatellite stability (MSS) colorectal cancer (CRC).

METHODS

Thirty-eight and seventy-six patients with pathologically proven MSI and MSS CRC, respectively, were retrospectively selected and compared. These patients underwent contrast-enhanced abdominal ssDECT scans before any anti-cancer treatment. Effective atomic number (Eff-Z) in precontrast phase, slope k of spectral HU curve in precontrast (k-P), arterial (k-A), venous (k-V), and delayed phase (k-D), normalized iodine concentration in arterial (NIC-A), venous (NIC-V), and delayed phase (NIC-D), of tumors in two groups were measured by two reviewers. Consistency of measurements was tested by intra-class correlation coefficients (ICC). Mann-Whitney U test or Student's t test was used to compare above values between MSI and MSS. Multivariate logistic regression was used to analyze multiple parameters. Receiver operating characteristic curves were calculated to assess diagnostic efficacies.

RESULTS

Interobserver agreement was excellent (ICC > 0.80). MSI CRC had significantly lower values in all measurements (NIC-A, V, D; k-P, A, V, D; Eff-Z) than MSS CRC. For discriminating MSI from MSS CRC, the area under curve (AUC) using k-A was the highest (AUC, 0.803; sensitivity, 72.4%; specificity, 76.3%). The multivariate logistic regression (selection method, Enter) with combined ssDECT parameters (NIC-A, NIC-V, NIC-D, Eff-Z, k-P, k-A, k-V, k-D) significantly improved diagnostic capability with AUC of 0.886 (sensitivity, 81.6%; specificity, 81.6%).

CONCLUSIONS

The combination of multiple parameters in ssDECT imaging by multivariate logistic regression provides relatively high diagnostic accuracy for discriminating MSI from MSS CRC.

KEY POINTS

• ssDECT generates multiple parameters for discriminating CRC with MSI from MSS. • ssDECT measurements for MSI CRC were significantly lower than MSS CRC. • Combination of ssDECT parameters further improves diagnostic capability for differentiation.

Endometrial cancer: an overview of novelties in treatment and related imaging keypoints for local staging

Endometrial cancer is the most common gynaecologic malignancy in developed countries and its incidence is increasing. First-level treatment, if no contraindicated, is based on surgery. Pre-operative imaging is needed for evaluation of local extent and detection of distant metastases in order to guide treatment planning. Radiological evaluation, based on transvaginal ultrasound, MR and CT, can make the difference in disease management, paying special attention to assessment of entity of myometrial invasion, cervical stromal extension, and assessment of lymph nodal involvement and distant metastases.