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

The value of multi-sequence magnetic resonance imaging and whole-tumor apparent diffusion coefficient histogram analysis in differentiating p53 abnormal from non-p53 abnormal endometrial carcinoma

Objective

To investigate the utility of multi-sequence magnetic resonance imaging (MRI) and whole-tumor apparent diffusion coefficient (ADC) histogram metrics in preoperatively differentiating p53 abnormal (p53abn) from non-p53abn endometrial carcinoma (EC).

Methods

This retrospective study included 146 EC patients (29 p53abn cases and 117 non-p53abn cases) who underwent preoperative MRI scans. MRI features were analyzed. Whole-tumor ADC histogram analysis was conducted by delineating regions of interest (ROIs) on diffusion-weighted imaging (DWI) scans. Receiver operating characteristic (ROC) curve analysis with the area under the curve (AUC) was used for diagnostic performance evaluation.

Results

Extrauterine extension (p=0.004) and lymphadenopathy (p=0.005) were more frequently observed in p53abn EC compared to non-p53abn EC. p53abn EC exhibited significantly lower value of minADC (p=0.001), meanADC (p=0.005), P10 (p=0.009), P50 (p=0.007), and P90 (p=0.013) ADC and higher value of kurtosis (p=0.008), compared to non-p53abn EC. MinADC demonstrated the highest discrimination ability in differentiating p53abn from non-p53abn EC [AUC 0.70(0.60;0.80)].

Conclusion

Preoperative multi-sequence MRI findings and whole-tumor ADC histogram metrics are conducive to differentiating p53abn from non-p53abn EC.

Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features

OBJECTIVES

To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).

METHODS

DWI and clinical data from 155 EC patients were included in this study, consisting of 80 in the training set, 35 in the test set, and 40 in the external validation set. Radiomics features, convolutional neural network-based DL features, and clinical variables were analyzed. Feature selection was performed using Mann-Whitney U test, LASSO regression, and SelectKBest. Prediction models were established by gaussian process (GP) and decision tree (DT) algorithms and evaluated by the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), calibration curves, and decision curve analysis (DCA).

RESULTS

Compared to the DL (AUCtraining = 0.830, AUCtest = 0.779, and AUCvalidation = 0.711), radiomics (AUCtraining = 0.810, AUCtest = 0.710, and AUCvalidation = 0.839), and clinical (AUCtraining = 0.780, AUCtest = 0.685, and AUCvalidation = 0.695) models, the combined model based on the GP algorithm, which consisted of four DL features, five radiomics features, and two clinical variables, not only demonstrated the highest diagnostic efficacy (AUCtraining = 0.949, AUCtest = 0.877, and AUCvalidation = 0.914) but also led to an improvement in risk reclassification of the TP53 mutation (NIRtraining = 66.38%, 56.98%, and 83.48%, NIRtest = 50.72%, 80.43%, and 89.49%, and NIRvalidation = 64.58%, 87.50%, and 120.83%, respectively). In addition, the combined model exhibited good agreement and clinical utility in calibration curves and DCA analyses, respectively.

CONCLUSIONS

A prediction model based on the GP algorithm and consisting of DL and radiomics features of DWI as well as clinical variables can effectively assess TP53 mutation in EC.

Evaluation of lymphovascular space invasion in endometrial carcinoma by APTw and mDixon-Quant

BACKGROUND

Lymphovascular space invasion (LVSI) is a strong and independent risk factor that increases the probability of endometrial carcinoma (EC) recurrence and reduces the survival rate of patients.

PURPOSE

To investigate the value of amide proton transfer weighted (APTw) and mDixon-Quant techniques in evaluating EC lymphovascular space invasion (LVSI).

MATERIAL AND METHODS

Data of 50 EC patients (18 LVSI+ and 32 LVSI-) confirmed by surgery and pathology were retrospectively analyzed. Preoperative magnetic resonance imaging (MRI) scans included APTw and mDixon-Quant imaging. APT, transverse relaxation rate (R2*), and fat fraction (FF) plots were obtained by postprocessing. The APT, R2*, and FF values of the two groups of cases were measured by two observers.

RESULTS

The agreement between the two observers was good. The mean APT, R2*, and FF values of LVSI+ EC were 2.947% ± 0.399%, 20.605 /s (range = 18.525-27.953), and 2.234% ± 1.047%, respectively, while the parameters of LVSI- EC were 2.628% ± 0.307%, 18.968 /s (range = 16.225-20.544), and 2.103% ± 1.070%, respectively. The APT and R2* values of LVSI+ EC were higher than those of LVSI- EC (P < 0.05). There was no significant difference in FF value between the two groups. The AUC values of APT, R2*, and APT + R2* for LVSI were 0.751, 0.713, and 0.781, respectively (all P > 0.05). APT value was moderately correlated with R2* value (r = 0.528, P < 0.001) and weakly correlated with FF value (r = 0.312, P = 0.027).

CONCLUSION

APTw and mDixon-Quant techniques could evaluate the LVSI status of EC, and their combined application could improve diagnostic efficiency.

Synthetic MRI and amide proton transfer-weighted MRI for differentiating between benign and malignant sinonasal lesions

OBJECTIVES

To explore the value of the synthetic MRI (SyMRI), combined with amide proton transfer-weighted (APTw) MRI for quantitative and morphologic assessment of sinonasal lesions, which could provide relative scale for the quantitative assessment of tissue properties.

METHODS

A total of 80 patients (31 malignant and 49 benign) with sinonasal lesions, who underwent the SyMRI and APTw examination, were retrospectively analyzed. Quantitative parameters (T1, T2, proton density (PD)) and APT % were obtained through outlining the region of interest (ROI) and comparing the two groups utilizing independent Student t test or a Wilcoxon test. Receiver operating characteristic curve (ROC), Delong test, and logistic regression analysis were performed to assess the diagnostic efficiency of one-parameter and multiparametric models.

RESULTS

SyMRI-derived mean T1, T2, and PD were significantly higher and APT % was relatively lower in benign compared to malignant sinonasal lesions (p < 0.05). The ROC analysis showed that the AUCs of the SyMRI-derived quantitative (T1, T2, PD) values and APT % ranged from 0.677 to 0.781 for differential diagnosis between benign and malignant sinonasal lesions. The T2 values showed the best diagnostic performance among all single parameters for differentiating these two masses. The AUCs of combined SyMRI-derived multiple parameters with APT % (AUC = 0.866) were the highest than that of any single parameter, which was significantly improved (p < 0.05).

CONCLUSION

The combination of SyMRI and APTw imaging has the potential to reflect intrinsic tissue characteristics useful for differentiating benign from malignant sinonasal lesions.

CLINICAL RELEVANCE STATEMENT

Combining synthetic MRI with amide proton transfer-weighted imaging could function as a quantitative and contrast-free approach, significantly enhancing the differentiation of benign and malignant sinonasal lesions and overcoming the limitations associated with the superficial nature of endoscopic nasal sampling.

KEY POINTS

• Synthetic MRI and amide proton transfer-weighted MRI could differentiate benign from malignant sinonasal lesions based on quantitative parameters. • The diagnostic efficiency could be significantly improved through synthetic MRI + amide proton transfer-weighted imaging. • The combination of synthetic MRI and amide proton transfer-weighted MRI is a noninvasive method to evaluate sinonasal lesions.

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.

Preliminary exploration of amide proton transfer weighted imaging in differentiation between benign and malignant bone tumors

Purpose

To explore the value of 3D amide proton transfer weighted imaging (APTWI) in the differential diagnosis between benign and malignant bone tumors, and to compare the diagnostic performance of APTWI with traditional diffusion-weighted imaging (DWI).

Materials and methods

Patients with bone tumors located in the pelvis or lower limbs confirmed by puncture or surgical pathology were collected from January 2021 to July 2023 in the First Affiliated Hospital of Zhengzhou University. All patients underwent APTWI and DWI examinations. The magnetization transfer ratio with asymmetric analysis at the frequency offset of 3.5 ppm [MTRasym(3.5 ppm)] derived by APTWI and the apparent diffusion coefficient (ADC) derived by DWI for the tumors were measured. The Kolmogorou-Smirnou and Levene normality test was used to confirm the normal distribution of imaging parameters; and the independent sample t test was used to compare the differences in MTRasym(3.5 ppm) and ADC between benign and malignant bone tumors. In addition, the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of different imaging parameters in differentiation between benign and malignant bone tumors. P<0.05 means statistically significant.

Results

Among 85 bone tumor patients, 33 were benign and 52 were malignant. The MTRasym(3.5 ppm) values of malignant bone tumors were significantly higher than those of benign tumors, while the ADC values were significantly lower in benign tumors. ROC analysis shows that MTRasym(3.5 ppm) and ADC values perform well in the differential diagnosis of benign and malignant bone tumors, with the area under the ROC curve (AUC) of 0.798 and 0.780, respectively. Combination of MTRasym(3.5 ppm) and ADC values can further improve the diagnostic performance with the AUC of 0.849 (sensitivity = 84.9% and specificity = 73.1%).

Conclusion

MTRasym(3.5 ppm) of malignant bone tumors was significantly higher than that of benign bone tumors, reflecting the abnormal increase of protein synthesis in malignant tumors. APTWI combined with DWI can achieve a high diagnostic efficacy in differentiation between benign and malignant bone tumors.

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.

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.

Multi-parametric MRI-based radiomics for preoperative prediction of multiple biological characteristics in endometrial cancer

Purpose

To develop and validate multi-parametric MRI (MP-MRI)-based radiomics models for the prediction of biological characteristics in endometrial cancer (EC).

Methods

A total of 292 patients with EC were divided into LVSI (n = 208), DMI (n = 292), MSI (n = 95), and Her-2 (n = 198) subsets. Total 2316 radiomics features were extracted from MP-MRI (T2WI, DWI, and ADC) images, and clinical factors (age, FIGO stage, differentiation degree, pathological type, menopausal state, and irregular vaginal bleeding) were included. Intra-class correlation coefficient (ICC), spearman's rank correlation test, univariate logistic regression, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features; univariate and multivariate logistic regression were used to identify clinical independent risk factors. Five classifiers were applied (logistic regression, random forest, decision tree, K-nearest neighbor, and Bayes) to construct radiomics models for predicting biological characteristics. The clinical model was built based on the clinical independent risk factors. The combined model incorporating the radiomics score (radscore) and the clinical independent risk factors was constructed. The model was evaluated by ROC curve, calibration curve (H-L test), and decision curve analysis (DCA).

Results

In the training cohort, the RF radiomics model performed best among the five classifiers for the three subsets (MSI, LVSI, and DMI) according to AUC values (AUCMSI: 0.844; AUCLVSI: 0.952; AUCDMI: 0.840) except for Her-2 subset (Decision tree: AUC=0.714), and the combined model had higher AUC than the clinical model in each subset (MSI: AUCcombined =0.907, AUCclinical =0.755; LVSI: AUCcombined =0.959, AUCclinical =0.835; DMI: AUCcombined = 0.883, AUCclinical =0.796; Her-2: AUCcombined =0.812, AUCclinical =0.717; all P<0.05). Nevertheless, in the validation cohort, significant differences between the two models (combined vs. clinical model) were found only in the DMI and LVSI subsets (DMI: AUCcombined =0.803, AUCclinical =0.698; LVSI: AUCcombined =0.926, AUCclinical =0.796; all P<0.05).

Conclusion

The radiomics analysis based on MP-MRI and clinical independent risk factors can potentially predict multiple biological features of EC, including DMI, LVSI, MSI, and Her-2, and provide valuable guidance for clinical decision-making.

Multimodal MRI for Estimating Her-2 Gene Expression in Endometrial Cancer

PURPOSE

To assess the value of multimodal MRI, including amide proton transfer-weighted imaging (APT), diffusion kurtosis imaging (DKI), and T2 mapping sequences for estimating human epidermal growth factor receptor-2 (Her-2) expression in patients with endometrial cancer (EC).

METHODS

A total of 54 patients with EC who underwent multimodal pelvic MRI followed by biopsy were retrospectively selected and divided into the Her-2 positive (n = 24) and Her-2 negative (n = 30) groups. Her-2 expression was confirmed by immunohistochemistry (IHC). Two observers measured APT, mean kurtosis (MK), mean diffusivity (MD), and T2 values for EC lesions.

RESULTS

The Her-2 (+) group showed higher APT values and lower MD and T2 values than the Her-2 (-) group (all p < 0.05); there was no significant difference in MK values (p > 0.05). The area under the receiver operating characteristic curve (AUC) of APT, MD, T2, APT + T2, APT + MD, T2 + MD, and APT + MD + T2 models to identify the two groups of cases were 0.824, 0.695, 0.721, 0.824, 0.858, 0.782, and 0.860, respectively, and the diagnostic efficacy after combined APT + MD + T2 value was significantly higher than those of MD and T2 values individually (p = 0.018, 0.028); the diagnostic efficacy of the combination of APT + T2 values was significantly higher than that of T2 values separately (p = 0.028). Weak negative correlations were observed between APT and T2 values (r = -0.365, p = 0.007), moderate negative correlations between APT and MD values (r = -0.560, p < 0.001), and weak positive correlations between MD and T2 values (r = 0.336, p = 0.013). The APT values were independent predictors for assessing Her-2 expression in EC patients.

CONCLUSION

The APT, DKI, and T2 mapping sequences can be used to preoperatively assess the Her-2 expression in EC, which can contribute to more precise treatment for clinical preoperative.

The value of multimodal functional magnetic resonance imaging in differentiating p53abn from p53wt endometrial carcinoma

BACKGROUND

Endometrial carcinoma (EC) is the sixth most common cancer in women. P53 gene expression in patients with endometrial cancer can predict the efficacy and prognosis of patients with neoadjuvant therapy.

PURPOSE

To explore the value of multimodal magnetic resonance imaging (MRI) in differentiating p53 abnormal (p53abn) from p53 wild-type (p53wt) EC.

MATERIAL AND METHODS

Data from 47 EC patients, including 14 p53abn cases and 33 p53wt cases, were retrospectively analyzed. The preoperative MRI sequences included amide proton transfer weighted (APTw) imaging, T2 mapping, mDIXON-Quant imaging and diffusion-weighted imaging (DWI). After post-processing, APT, T2, transverse relaxation rate (R2*), fat fraction (FF) and apparent diffusion coefficient (ADC) maps were obtained. The APT, T2, R2*, FF and ADC values for lesions of the two groups of cases were measured by two observers who were blind to the pathological data.

RESULTS

The APT value and R2* value in the p53abn group were higher than those in the p53wt group, while the ADC value was lower (all P < 0.05). There was no statistically significant difference in T2 value and FF value between the two groups (all P > 0.05). The area under curve of APT, R2*, ADC and combined APT + R2*+ADC values for identification of p53abn and p53wt EC were 0.739, 0.689, 0.718 and 0.820, respectively (all P > 0.05).

CONCLUSION

APTw, mDIXON-Quant and DWI techniques can be usedfor quantitative identification of p53abn and p53wt EC. The multimodal MRI provides a new way for preoperative quantitative evaluation of EC molecular typing, which has certain clinical application value.

Predicting Preoperative Pathologic Grades of Bladder Cancer Using Intravoxel Incoherent Motion and Amide Proton Transfer-Weighted Imaging

RATIONALE AND OBJECTIVES

To investigate the predictive value of intravoxel incoherent motion (IVIM) combined with amide proton transfer-weighted (APTw) imaging for the preoperative grading of bladder cancer (BC).

MATERIALS AND METHODS

A total of 69 patients with histopathologically confirmed BC underwent diffusion-weighted imaging (DWI), IVIM, and APTw imaging at 3.0 T MRI. Two radiologists independently measured the mean apparent diffusion coefficient (ADC) in DWI, true diffusion coefficient (D), perfusion-related pseudo-diffusion coefficient (D*), and perfusion fraction (f) in IVIM, and APTw values, respectively. The areas under the receiver operating characteristic curves (AUCs) were utilized to compare the diagnostic efficacy of these single and combined quantitative parameters.

RESULTS

ADC and D values of low-grade BC were significantly higher than those of high-grade BC ([1.42 ± 0.20 ×10-3 mm2/s] vs. [1.09 ± 0.25 ×10-3 mm2/s] and [1.24 ± 0.24 ×10-3 mm2/s] vs. [0.89 ± 0.18 ×10-3 mm2/s], respectively; all P < 0.001). Opposite patterns were found for APTw ( [1.53 ± 0.42]% vs. [2.38 ± 0.71]%, P < 0.001). The ROC curves indicated that the combination of D and APTw values could distinguish low- from high-grades of BC with the highest predictive efficacy (AUC = 0.96), as well as a significant difference compared to those by ADC, D, and APTw values separately (AUC = 0.84, 0.88, 0.85, respectively; all P < 0.05).

CONCLUSION

IVIM combined with APTw imaging significantly improved the predictive efficacy of assessing low- and high-grade BC compared to the individual parameters on their own, providing an effective non-invasive method for clinical preoperative prediction of BC grading.