Journal of Magnetic Resonance Imaging

An Integrated Clinical‐MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer

BackgroundDetermination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning.PurposeTo identify clinical features and imaging signatures on T2‐weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects.Study TypeRetrospective.PopulationFour hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years).Field Strength/Sequence1.5‐T and 3‐T scanners with sagittal T2‐weighted spin echo sequence.AssessmentTumor regions were manually segmented on T2‐weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets.Statistical TestsA likelihood‐ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model.ResultsThree radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively.Data ConclusionThe proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer.Evidence Level4Technical EfficacyStage 2

Editorial for “MRI‐Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors”

Level of Evidence5Technical Efficacy Stage3 J. Magn. Reson. Imaging 2020;52:905–905.

Editorial for “MRI‐Based Multiple Instance Convolutional Neural Network (MICNN) for Increased Accuracy in the Differentiation of Borderline and Malignant Epithelial Ovarian Tumors”

MRI‐Based Multiple Instance Convolutional Neural Network for Increased Accuracy in the Differentiation of Borderline and Malignant Epithelial Ovarian Tumors

BackgroundPreoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT vs. MEOT) is challenging and can significantly impact surgical management.PurposeTo develop a multiple instance convolutional neural network (MICNN) that can differentiate BEOT from MEOT, and to compare its diagnostic performance with that of radiologists.Study TypeRetrospective study of eight clinical centers.SubjectsBetween January 2010 and June 2018, a total of 501 women (mean age, 48.93 ± 14.05 years) with histopathologically confirmed BEOT (N = 165) or MEOT (N = 336) were divided into the training (N = 342) and validation cohorts (N = 159).Field Strength/SequenceThree axial sequences from 1.5 or 3 T scanner were used: fast spin echo T2‐weighted imaging with fat saturation (T2WI FS), echo planar diffusion‐weighted imaging, and 2D volumetric interpolated breath‐hold examination of contrast‐enhanced T1‐weighted imaging (CE‐T1WI) with FS.AssessmentThree monoparametric MICNN models were built based on T2WI FS, apparent diffusion coefficient map, and CE‐T1WI. Based on these monoparametric models, we constructed an early multiparametric (EMP) model and a late multiparametric (LMP) model using early and late information fusion methods, respectively. The diagnostic performance of the models was evaluated using the receiver operating characteristic (ROC) curve and compared to the performance of six radiologists with varying levels of experience.Statistical TestsWe used DeLong test, chi‐square test, Mann–Whitney U‐test, and t‐test, with significance level of 0.05.ResultsBoth EMP and LMP models differentiated BEOT from MEOT, with an area under the ROC curve (AUC) of 0.855 (95% CI, 0.795–0.915) and 0.884 (95% CI, 0.831–0.938), respectively. The AUC of the LMP model was significantly higher than the radiologists' pooled AUC (0.884 vs. 0.797).Data ConclusionThe developed MICNN models can effectively differentiate BEOT from MEOT and the diagnostic performances (AUCs) were more superior than that of the radiologists' assessments.Level of Evidence3Technical Efficacy Stage2

Editorial for “Diffusion‐Weighted Magnetic Resonance Imaging and Morphological Characteristics Evaluation for Outcome Prediction of Primary Debulking Surgery for Advanced High‐Grade Serous Ovarian Carcinoma”

Diffusion‐Weighted Magnetic Resonance Imaging and Morphological Characteristics Evaluation for Outcome Prediction of Primary Debulking Surgery for Advanced High‐Grade Serous Ovarian Carcinoma

BackgroundPreoperative assessment of whether a successful primary debulking surgery (PDS) can be performed in patients with advanced high‐grade serous ovarian carcinoma (HGSOC) remains a challenge. A reliable model to precisely predict resectability is highly demanded.PurposeTo investigate the value of diffusion‐weighted MRI (DW‐MRI) combined with morphological characteristics to predict the PDS outcome in advanced HGSOC patients.Study TypeProspective.SubjectsA total of 95 consecutive patients with histopathologically confirmed advanced HGSOC (ranged from 39 to 77 years).Fields Strength/SequenceA 3.0 T, readout‐segmented echo‐planar DWI.AssessmentThe MRI morphological characteristics of the primary ovarian tumor, a peritoneal carcinomatosis index (PCI) derived from DWI (DWI‐PCI) and histogram analysis of the primary ovarian tumor and the largest peritoneal carcinomatosis were assessed by three radiologists. Three different models were developed to predict the resectability, including a clinicoradiologic model combing MRI morphological characteristic with ascites and CA125 level; DWI‐PCI alone; and a fusion model combining the clinical‐morphological information and DWI‐PCI.Statistical TestsMultivariate logistic regression analyses, receiver operating characteristic (ROC) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI) were used. A P < 0.05 was considered to be statistically significant.ResultsSixty‐seven cases appeared as a definite mass, whereas 28 cases as an infiltrative mass. The morphological characteristics and DWI‐PCI were independent factors for predicting the resectability, with an AUC of 0.724 and 0.824, respectively. The multivariable predictive model consisted of morphological characteristics, CA‐125, and the amount of ascites, with an incremental AUC of 0.818. Combining the application of a clinicoradiologic model and DWI‐PCI showed significantly higher AUC of 0.863 than the ones of each of them implemented alone, with a positive NRI and IDI.Data ConclusionsThe combination of two clinical factors, MRI morphological characteristics and DWI‐PCI provide a reliable and valuable paradigm for the noninvasive prediction of the outcome of PDS.Evidence Level2Technical EfficacyStage 2

Editorial for: “PET/MRI in Cervical Cancer: Associations Between Imaging Biomarkers and Tumor Stage, Disease Progression, and Overall Survival”

Editorial for: “Novel T2 Mapping for Evaluating Cervical Cancer Features by Providing Quantitative T2 Maps and Synthetic Morphologic Images: A Preliminary Study”

PET/MRI in Cervical Cancer: Associations Between Imaging Biomarkers and Tumor Stage, Disease Progression, and Overall Survival

BackgroundPositron emission tomography (PET)/MRI biomarkers have been shown to have prognostic significance in patients with cervical cancer. Their associations with progression‐free survival (PFS) and overall survival (OS) merit further investigation.PurposeTo evaluate the association between PET/MRI biomarkers and tumor stage, PFS, and OS in patients with cervical cancer.Study TypeProspective cohort study.PopulationIn all, 54 patients with newly diagnosed cervical cancer and measurable tumors (>1 cm) were included in the image analysis.Field Strength/Sequence3.0T integrated PET/MRI including diffusion‐weighted echo‐planar imaging (b = 50 and 1000 s/mm2) and [18F]fluorodeoxyglucose PET.AssessmentTwo radiologists measured the minimum and mean apparent diffusion coefficient (ADCmin and ADCmean), maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary tumors.Statistical TestsA Mann–Whitney U‐test was used to evaluate the association between the imaging biomarkers and tumor stage. A Cox proportional hazards model was used to assess the relationships between the imaging biomarkers and survival.ResultsIn advanced tumors (T ≥ 1b2, M1, stage ≥ IB3), ADCmin was significantly lower and MTV, TLG, MTV/ADCmin, and TLG/ADCmin were significantly higher (P values between <0.001 and 0.036). In N1 tumors, ADCmin was significantly lower and MTV and MTV/ADCmin were significantly higher (P values between 0.005 and 0.016). In survival analysis, SUVmax was an independent predictor of PFS (hazard ratio [HR] = 4.57, P < 0.05), and ADCmin was an independent predictor of OS (HR = 0.02, P < 0.05). In subgroup analysis of patients with different stages, MTV/ADCmin was a predictor of PFS in stage I disease (P = 0.003), ADCmin (P = 0.038), and MTV (P = 0.020) in stage II, SUVmax (P = 0.006), and TLG (P = 0.006) in stage IV; and ADCmin was a predictor of OS in stage III disease (P = 0.008).Data ConclusionPET/MRI biomarkers of cervical cancer are associated with tumor stage and survival. SUVmax and ADCmin are independent predictors of PFS and OS, respectively.Level of Evidence1Technical Efficacy3

Multiparametric MRI‐Based Radiomics Nomogram for Predicting Lymph Node Metastasis in Early‐Stage Cervical Cancer

BackgroundLymph node metastasis (LNM) is a critical risk factor affecting treatment strategy and prognosis in patients with early‐stage cervical cancer.PurposeTo establish a multiparametric MRI (mpMRI)‐based radiomics nomogram for preoperatively predicting LNM status.Study TypeRetrospective.PopulationAmong 233 consecutive patients, 155 patients were randomly allocated to the primary cohort and 78 patients to the validation cohort.Field StrengthRadiomic features were extracted from a 1.5T mpMRI scan (T1‐weighted imaging [T1WI], fat‐saturated T2‐weighted imaging [FS‐T2WI], contrast‐enhanced [CE], diffusion‐weighted imaging [DWI], and apparent diffusion coefficient [ADC] maps).AssessmentThe performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The area under the receiver operating characteristics curve (ROC AUC), accuracy, sensitivity, and specificity were also calculated.Statistical TestsThe least absolute shrinkage and selection operator (LASSO) method was used for dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the radiomics nomogram. An independent sample t‐test and chi‐squared test were used to compare the differences in continuous and categorical variables, respectively.ResultsThe radiomic signature allowed a good discrimination between the LNM and non‐LNM groups, with a C‐index of 0.856 (95% confidence interval [CI], 0.794–0.918) in the primary cohort and 0.883 (95% CI, 0.809–0.957) in the validation cohort. Additionally, the radiomics nomogram also had a good discriminating performance and yielded good calibration both in the primary and validation cohorts (C‐index, 0.882 [95% CI, 0.827–0.937], C‐index, 0.893 [95% CI, 0.822–0.964], respectively). Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.Data ConclusionA radiomics nomogram was developed by incorporating the radiomics signature with the MRI‐reported LN status and FIGO stage. This nomogram might be used to facilitate the individualized prediction of LNM in patients with early‐stage cervical cancer.Level of Evidence3Technical Efficacy Stage2 J. Magn. Reson. Imaging 2020;52:885–896.

PreliminaryMRIStudy of Extracellular Volume Fraction for Identification of Lymphovascular Space Invasion of Cervical Cancer

BackgroundLymphovascular space invasion (LVSI) is a risk factor for poor prognosis of cervical cancer. Preoperative identification of LVSI is very difficult.PurposeTo evaluate the potential of extracellular volume (ECV) fraction based on T1 mapping in preoperative identification of LVSI in cervical cancer compared with dynamic contrast‐enhanced MRI (DCE‐MRI).Study TypeRetrospective.SubjectsA total of 79 patients (median age 54 years) with cervical cancer were classified into LVSI group (n = 29) and without LVSI group (n = 50) according to postoperative pathology.Field Strength/SequenceA 3‐T, noncontrast and contrast‐enhanced T1 mapping performed with volume interpolated breath hold examination (VIBE) sequence, DCE‐MRI applied with 3D T1‐weighted VIBE sequence.AssessmentRegions of interest along the medial edge of the lesion were drawn on slices depicting the maximum cross‐section of the tumor. The noncontrast and contrast‐enhanced T1 value of the tumor and arteries in the same slice were measured, and ECV was calculated from T1 values. The parametric maps (Ktrans,kep, andve) derived from DCE‐MRI standard Toft's model were evaluated.Statistical TestsECV,Ktrans,kep, andvebetween groups with and without LVSI were compared using Student'st‐test. The receiver operating characteristic (ROC) curve and DeLong test were used to evaluate and compare the diagnostic performance of ECV,Ktrans,kep, andvefor differentiating LVSI.P < 0.05 was considered statistically significant.ResultsThe ECV andKtransof the LVSI group were significantly higher than that of non‐LVSI group (52.86% vs. 36.77%, 0.239 vs. 0.176, respectively), and no significant differences inKeporvevalues were observed (P = 0.071 andP = 0.168, respectively). The ECV fraction showed significantly higher area under ROC curve thanKtransfor differentiating LVSI (0.874 vs. 0.655, respectively).Data ConclusionECV measurements based on T1 mapping might improve the discrimination between patients with and without LVSI in cervical cancer, showing better performance for this purpose than DCE‐MRI.Evidence Level2Technical EfficacyStage 2

Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi‐Parametric MRI by Deep Learning

BackgroundEarly and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi‐parametric MRI (mpMRI) images.PurposeTo develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images.Study TypeRetrospective.PopulationSix hundred twenty‐one patients with histologically proven EC from two institutions, including 111 LNM‐positive and 168 LVSI‐positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively.Field Strength/SequenceT2‐weighted imaging (T2WI), contrast‐enhanced T1WI (CE‐T1WI), and diffusion‐weighted imaging (DWI) were scanned with turbo spin‐echo, gradient‐echo, and two‐dimensional echo‐planar sequences, using either a 1.5 T or 3 T system.AssessmentEC lesions were manually delineated on T2WI by two radiologists and used to train an nnU‐Net model for automatic segmentation. A multi‐task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE‐T1WI, and DWI images as inputs. The performance of the model for LNM‐positive diagnosis was compared with those of three radiologists in the external test cohort.Statistical TestsDice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant.ResultsEC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674).Data ConclusionThe proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning.Evidence Level3Technical EfficacyStage 2

Multitask Deep Learning for Automated Segmentation and Prognostic Stratification of Endometrial Cancer via Biparametric MRI

ABSTRACTBackgroundEndometrial cancer (EC) is a common gynecologic malignancy; accurate assessment of key prognostic factors is important for treatment planning.PurposeTo develop a deep learning (DL) framework based on biparametric MRI for automated segmentation and multitask classification of EC key prognostic factors, including grade, stage, histological subtype, lymphovascular space invasion (LVSI), and deep myometrial invasion (DMI).Study TypeRetrospective.SubjectsA total of 325 patients with histologically confirmed EC were included: 211 training, 54 validation, and 60 test cases.Field Strength/SequenceT2‐weighted imaging (T2WI, FSE/TSE) and diffusion‐weighted imaging (DWI, SS‐EPI) sequences at 1.5 and 3 T.AssessmentThe DL model comprised tumor segmentation and multitask classification. Manual delineation on T2WI and DWI acted as the reference standard for segmentation. Separate models were trained using T2WI alone, DWI alone and combined T2WI + DWI to classify dichotomized key prognostic factors. Performance was assessed in validation and test cohorts. For DMI, the combined model's was compared with visual assessment by four radiologists (with 1, 4, 7, and 20 years' experience), each of whom independently reviewed all cases.Statistical TestsSegmentation was evaluated using the dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), Hausdorff distance (HD95), and average surface distance (ASD). Classification performance was assessed using area under the receiver operating characteristic curve (AUC). Model AUCs were compared using DeLong's test. p < 0.05 was considered significant.ResultsIn the test cohort, DSCs were 0.80 (T2WI) and 0.78 (DWI) and JSCs were 0.69 for both. HD95 and ASD were 7.02/1.71 mm (T2WI) versus 10.58/2.13 mm (DWI). The classification framework achieved AUCs of 0.78–0.94 (validation) and 0.74–0.94 (test). For DMI, the combined model performed comparably to radiologists (p = 0.07–0.84).ConclusionsThe unified DL framework demonstrates strong EC segmentation and classification performance, with high accuracy across multiple tasks.Evidence Level3.Technical EfficacyStage 3.

Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023

The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer.Evidence Level4Technical EfficacyStage 3

Machine Learning‐Based Integration of Prognostic Magnetic Resonance Imaging Biomarkers for Myometrial Invasion Stratification in Endometrial Cancer

BackgroundEstimation of the depth of myometrial invasion (MI) in endometrial cancer is pivotal in the preoperatively staging. Magnetic resonance (MR) reports suffer from human subjectivity. Multiparametric MR imaging radiomics and parameters may improve the diagnostic accuracy.PurposeTo discriminate between patients with MI ≥ 50% using a machine learning‐based model combining texture features and descriptors from preoperatively MR images.Study TypeRetrospective.PopulationOne hundred forty‐three women with endometrial cancer were included. The series was split into training (n = 107, 46 with MI ≥ 50%) and test (n = 36, 16 with MI ≥ 50%) cohorts.Field Strength/SequencesFast spin echo T2‐weighted (T2W), diffusion‐weighted (DW), and T1‐weighted gradient echo dynamic contrast‐enhanced (DCE) sequences were obtained at 1.5 or 3 T magnets.AssessmentTumors were manually segmented slice‐by‐slice. Texture metrics were calculated from T2W and ADC map images. Also, the apparent diffusion coefficient (ADC), wash‐in slope, wash‐out slope, initial area under the curve at 60 sec and at 90 sec, initial slope, time to peak and peak amplitude maps from DCE sequences were obtained as parameters. MR diagnostic models using single‐sequence features and a combination of features and parameters from the three sequences were built to estimate MI using Adaboost methods. The pathological depth of MI was used as gold standard.Statistical TestArea under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, precision and recall were computed to assess the Adaboost models performance.ResultsThe diagnostic model based on the features and parameters combination showed the best performance to depict patient with MI ≥ 50% in the test cohort (accuracy = 86.1% and AUROC = 87.1%). The rest of diagnostic models showed a worse accuracy (accuracy = 41.67%–63.89% and AUROC = 41.43%–63.13%).Data ConclusionThe model combining the texture features from T2W and ADC map images with the semi‐quantitative parameters from DW and DCE series allow the preoperative estimation of myometrial invasion.Evidence Level4Technical EfficacyStage 3

Editorial for “Oscillating Gradient Diffusion‐Weighted MRI for Risk Stratification of Uterine Endometrial Cancer”

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

BackgroundOscillating 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.PurposeTo investigate the utility of oscillating gradient DWI for risk stratification in patients with uterine endometrial cancer compared with conventional pulsed gradient DWI.Study TypeRetrospective.SubjectsSixty‐three women (mean age: 58 [range: 32–85] years) with endometrial cancer.Field Strength/Sequence3 T MRI including DWI using oscillating gradient spin‐echo (OGSE) and pulsed gradient spin‐echo (PGSE) research sequences.AssessmentMean 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 TestsInterobserver 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.ResultsCompared 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 ConclusionThe 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 Evidence3Technical EfficacyStage 2

Preoperative Assessment of MRI‐Invisible Early‐Stage Endometrial Cancer With MRI‐Based Radiomics Analysis

BackgroundRadiomics‐based analyses have demonstrated impact on studies of endometrial cancer (EC). However, there have been no radiomics studies investigating preoperative assessment of MRI‐invisible EC to date.PurposeTo develop and validate radiomics models based on sagittal T2‐weighted images (T2WI) and T1‐weighted contrast‐enhanced images (T1CE) for the preoperative assessment of MRI‐invisible early‐stage EC and myometrial invasion (MI).Study TypeRetrospective.PopulationOne hundred fifty‐eight consecutive patients (mean age 50.7 years) with MRI‐invisible endometrial lesions were enrolled from June 2016 to March 2022 and randomly divided into the training (n = 110) and validation cohort (n = 48) using a ratio of 7:3.Field Strength/Sequence3‐T, T2WI, and T1CE sequences, turbo spin echo.AssessmentTwo radiologists performed image segmentation and extracted features. Endometrial lesions were histopathologically classified as benign, dysplasia, and EC with or without MI. In the training cohort, 28 and 20 radiomics features were selected to build Model 1 and Model 2, respectively, generating rad‐score 1 (RS1) and rad‐score 2 (RS2) for evaluating MRI‐invisible EC and MI.Statistical TestsThe least absolute shrinkage and selection operator logistic regression method was used to select radiomics features. Mann–Whitney U tests and Chi‐square test were used to analyze continuous and categorical variables. Receiver operating characteristic curve (ROC) and decision curve analysis were used for performance evaluation. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. A P‐value <0.05 was considered statistically significant.ResultsModel 1 had good performance for preoperative detecting of MRI‐invisible early‐stage EC in the training and validation cohorts (AUC: 0.873 and 0.918). In addition, Model 2 had good performance in assessment of MI of MRI‐invisible endometrial lesions in the training and validation cohorts (AUC: 0.854 and 0.834).Data ConclusionMRI‐based radiomics models may provide good performance for detecting MRI‐invisible EC and MI.Evidence Level3Technical EfficacyStage 2

Editorial for “Preoperative Assessment of MRI‐Invisible Early‐Stage Endometrial Cancer With MRI‐Based Radiomics Analysis”

Editorial for “Amide Proton Transfer‐Weighted Imaging Combined With Intravoxel Incoherent Motion for Evaluating Microsatellite Instability in Endometrial Cancer”

Editorial for “Machine Learning‐Based Integration of Prognostic MR Imaging Biomarkers for Myometrial Invasion Stratification in Endometrial Cancer”

Editorial for “Whole‐Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer”

Whole‐Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer

BackgroundIn endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI‐based radiomic tumor profiling may aid in preoperative risk‐stratification and support clinical treatment decisions in EC.PurposeTo develop MRI‐based whole‐volume tumor radiomic signatures for prediction of aggressive EC disease.Study TypeRetrospective.PopulationA total of 138 women with histologically confirmed EC, divided into training (nT = 108) and validation cohorts (nV = 30).Field Strength/SequenceAxial oblique T1‐weighted gradient echo volumetric interpolated breath‐hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection.AssessmentPrimary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically‐verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high‐grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area.Statistical TestsLogistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUCT) and validation (AUCV) cohorts. Progression‐free survival was assessed using the Kaplan–Meier and Cox proportional hazard model.ResultsThe whole‐tumor radiomic signatures yielded AUCT/AUCV of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high‐grade (E3) tumor. Single‐slice radiomics yielded comparable AUCT but significantly lower AUCV for LNM and FIGO III + IV (both P < 0.05). Tumor volume yielded comparable AUCT to the whole‐tumor radiomic signatures for prediction of DMI, LNM, FIGO III + IV, and NE, but significantly lower AUCT for E3 tumors (P < 0.05). All of the whole‐tumor radiomic signatures significantly predicted poor progression‐free survival with hazard ratios of 4.6–9.8 (P < 0.05 for all).Data ConclusionMRI‐based whole‐tumor radiomic signatures yield medium‐to‐high diagnostic performance for predicting aggressive EC disease. The signatures may aid in preoperative risk assessment and hence guide personalized treatment strategies in EC.Level of Evidence4Technical Efficacy Stage2

Editorial for “Preoperative Assessment for High‐Risk Endometrial Cancer by Developing an MRI‐ and Clinical‐Based Radiomics Nomogram: A Multicenter Study”

Multiparametric MRI‐Based Radiomics Nomogram for Predicting Lymphovascular Space Invasion in Endometrial Carcinoma

BackgroundLymphovascular space invasion (LVSI) of endometrial carcinoma (EMC) is one of the important prognostic factors, which is not usually visible subjectively. Therefore, clinicians lack imaging‐based evidence about LVSI for preoperative treatment decision‐making.PurposeTo develop a multiparametric MRI (mpMRI)‐based radiomics nomogram for predicting LVSI in EMC and provide decision‐making support to clinicians.Study TypeRetrospective.PopulationIn all, 144 patients with histologically confirmed EMC, 101 patients in a training cohort, and 43 patients in a test cohort.Field Strength/SequenceT2WI, contrast enhanced‐T1WI, and diffusion‐weighted imaging (DWI) at 3.0T MRI.AssessmentTumors were independently segmented images by two radiologists. Two pathologists reviewed the tissue specimens of the tumors to identify the existence of LVSI in consensus.Statistical TestsThe intraclass correlation coefficient was used to test the reliability and least absolute shrinkage and selection operator (LASSO) regression for features selection and then developed a radiomics signature named Rad‐score. A nomogram was developed in the training cohort. The diagnostic performance of the nomogram model was assessed by area under the curve (AUC) of the receiver operator characteristic (ROC) in the training and test cohort, respectively.ResultsLVSI was identified in 32 patients (22.2%). Older age and high grade were correlated with LVSI at univariate analysis. There were five radiomics features that were identified as independent risk factors for LVSI by LASSO regression. Based on age, grade, and Rad‐score, the AUC values of the nomogram model to predict LVSI in the training and test cohort were 0.820 (95% confidence interval [CI]: 0.725, 0.916; sensitivity: 82.6%, specificity: 72.9%), 0.807 (95% CI: 0.673, 0.941; sensitivity: 77.8%, specificity: 78.6%), respectively.Data ConclusionThe radiomic‐based machine‐learning model using a nomogram algorithm achieved high diagnostic performance for predicting LVSI of EMC preoperatively, which could enhance risk stratification and provide support for therapeutic decision‐making.Level of Evidence2.Technical Efficacy Stage3. J. Magn. Reson. Imaging 2020;52:1257–1262.

Editorial for “A Multiparametric MRI‐based Radiomics Nomogram for Predicting Lymphovascular Space Invasion in Endometrial Carcinoma”

Level of Evidence4Technical Efficacy Stage4 J. Magn. Reson. Imaging 2020;52:1263–1264.

Utility of diffusion‐weighted imaging in association with pathologic upgrading in biopsy‐proven grade I endometrial cancer

BackgroundPrediction of pathologic upgrading is clinically meaningful to identify the optimal candidate of fertility‐preserving hormonal treatment in the young patients with biopsy‐proven grade I endometrial cancer.PurposeTo investigate the utility of diffusion‐weighted imaging (DWI) in association with pathologic upgrading in endometrial cancer.Study TypeRetrospective.SubjectsPreoperative MRI datasets of 221 patients with grade I endometrial cancer on endometrial biopsy (n = 146), dilatation and curettage (n = 66), or either (n = 9).Field Strength/Sequence3.0T, including T2‐weighted imaging, DWI with a b‐value of 1000 s/mm2, and dynamic contrast enhanced imaging.AssessmentThe tumor size was determined as the longest diameter of the lesion. The minimum apparent diffusion coefficient (ADCmin) was calculated using histogram analysis of the entire tumor.Statistical TestsMann–Whitney U‐test, Pearson's chi‐square test, Fisher's exact test, intraclass correlation coefficient (ICC) analysis, receiver operating characteristic (ROC) curve analysis, univariate and multivariate logistic regression analysis.ResultsPathologic upgrading was identified in 42 patients (19.0%). Patients with pathologic upgrading had larger tumors and showed lower ADCmin values than those without pathologic upgrading (both P < 0.001). The area under the ROC curve of ADCmin and tumor size was 0.812 and 0.758, respectively. On multivariate analysis, tumor ADCmin ≤0.600 × 10‐3 mm2/s (odds ratio [OR], 11.8; P < 0.001) and tumor size on MRI >3 cm (OR, 3.24; P = 0.009) were independently associated with pathologic upgrading. Upgrading occurred in 23 of 31 patients (74.2%) with ADCmin ≤0.600 × 10‐3 mm2/s and tumor size >3 cm, and in 7 of 114 patients (6.1%) with ADCmin >0.600 × 10‐3 mm2/s and tumor size ≤3 cm.Data ConclusionTumor ADC and tumor size on MRI may be useful parameters in association with pathologic upgrading in biopsy‐proven grade I endometrial cancer.Level of Evidence: 4Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:117–123.

Peritumoral MRI Radiomics Features Increase the Evaluation Efficiency for Response to Chemotherapy in Patients With Epithelial Ovarian Cancer

BackgroundIt remains unclear whether extracting peritumoral volume (PTV) radiomics features are useful tools for evaluating response to chemotherapy of epithelial ovarian cancer (EOC).PurposeTo evaluate MRI radiomics signatures (RS) capturing subtle changes of PTV and their added evaluation performance to whole tumor volume (WTV) for response to chemotherapy in patients with EOC.Study TypeRetrospective.Population219 patients aged from 15 to 79 years were enrolled.Field Strength/Sequence3.0 or 1.5T, axial fat‐suppressed T2‐weighted imaging (FS‐T2WI), diffusion‐weighted imaging (DWI), and contrast enhanced T1‐weighted imaging (CE‐T1WI).AssessmentMRI features were extracted from the four axial sequences and six different volumes of interest (VOIs) (WTV and WTV + PTV (WPTV)) with different peritumor sizes (PS) ranging from 1 to 5 mm. Those features underwent preprocessing, and the most informative features were selected using minimum redundancy maximum relevance and least absolute shrinkage and selection operator to construct the RS. The optimal RS, with the highest area under the curve (AUC) of receiver operating characteristic was then integrated with independent clinical characteristics through multivariable logistic regression to construct the radiomics‐clinical model (RCM).Statistical TestsMann–Whitney U test, chi‐squared test, DeLong test, log‐rank test. P < 0.05 indicated a significant difference.ResultsAll the RSs constructed on WPTV exhibited higher AUCs (0.720–0.756) than WTV (0.671). Of which, RS with PS = 2 mm displayed a significantly better performance (AUC = 0.756). International Federation of Gynecology and Obstetrics (FIGO) stage was identified as the exclusive independent clinical evaluation characteristic, and the RCM demonstrated higher AUC (0.790) than the RS, but without statistical significance (P = 0.261).Data ConclusionThe radiomics features extracted from PTV could increase the efficiency of WTV radiomics for evaluating the chemotherapy response of EOC. The cut‐off of 2 mm PTV was a reasonable value to obtain effective evaluation efficiency.Level of Evidence4Technical EfficacyStage 2

O‐RADS MRI SCORE: An Essential First‐Step Tool for the Characterization of Adnexal Masses

The ovarian‐adnexal reporting and data system on magnetic resonance imaging (O‐RADS MRI) score is now a well‐established tool to characterize pelvic gynecological masses based on their likelihood of malignancy. The main added value of O‐RADS MRI over O‐RADS US is to correctly reclassify lesions that were considered suspicious on US as benign on MRI. The crucial issue when characterizing an adnexal mass is to determine the presence/absence of solid tissue and thus need to perform gadolinium injection. O‐RADS MR score was built on a multivariate analysis and must be applied as a step‐by‐step analysis: 1) Is the mass an adnexal mass? 2) Is there an associated peritoneal carcinomatosis? 3) Is there any significant amount of fatty content? 4) Is there any wall enhancement? 5) Is there any internal enhancement? 6) When an internal enhancement is detected, does the internal enhancement correspond to solid tissue or not? 7) Is the solid tissue malignant? With its high value to distinguish benign from malignant adnexal masses and its high reproducibility, the O‐RADS MRI score could be a valuable tool for timely referral of a patient to an expert center for the treatment of ovarian cancers. Finally, to make a precise diagnosis allowing optimal personalized treatment, the radiologist in gynecological imaging will combine the O‐RADS MRI score with many other clinical, biological, and other MR criteria to suggest a pathological hypothesis.Level of Evidence5Technical Efficacy Stage3.

Associating Peritoneal Metastasis With T2‐Weighted MRI Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study

BackgroundThe preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision‐making.PurposeTo investigate the performance of T2‐weighted (T2W) MRI‐based deep learning (DL) and radiomics methods for PM evaluation in EOC patients.Study TypeRetrospective.PopulationFour hundred seventy‐nine patients from five centers, including one training set (N = 297 [mean, 54.87 years]), one internal validation set (N = 75 [mean, 56.67 years]), and two external validation sets (N = 53 [mean, 55.58 years] and N = 54 [mean, 58.22 years]).Field Strength/Sequence1.5 or 3 T/fat‐suppression T2W fast or turbo spin‐echo sequence.AssessmentResNet‐50 was used as the architecture of DL. The largest orthogonal slices of the tumor area, radiomics features, and clinical characteristics were used to construct the DL, radiomics, and clinical models, respectively. The three models were combined using decision‐level fusion to create an ensemble model. Diagnostic performances of radiologists and radiology residents with and without model assistance were evaluated.Statistical TestsReceiver operating characteristic analysis was used to assess the performances of models. The McNemar test was used to compare sensitivity and specificity. A two‐tailed P < 0.05 was considered significant.ResultsThe ensemble model had the best AUCs, outperforming the DL model (0.844 vs. 0.743, internal validation set; 0.859 vs. 0.737, external validation set I) and clinical model (0.872 vs. 0.730, external validation set II). After model assistance, all readers had significantly improved sensitivity, especially for those with less experience (junior radiologist1, from 0.639 to 0.820; junior radiologist2, from 0.689 to 0.803; resident1, from 0.623 to 0.803; resident2, from 0.541 to 0.738). One resident also had significantly improved specificity (from 0.633 to 0.789).Data ConclusionsT2W MRI‐based DL and radiomics approaches have the potential to preoperatively predict PM in EOC patients and assist in clinical decision‐making.Evidence Level4Technical EfficacyStage 2

Editorial for “Deep Learning Nomogram for the Identification of Deep Stromal Invasion in Patients With Early‐Stage Cervical Adenocarcinoma and Adenosquamous Carcinoma: A Multicenter Study”

Deep Learning Nomogram for the Identification of Deep Stromal Invasion in Patients With Early‐Stage Cervical Adenocarcinoma and Adenosquamous Carcinoma: A Multicenter Study

BackgroundDeep stromal invasion (DSI) is one of the predominant risk factors that determined the types of radical hysterectomy (RH). Thus, the accurate assessment of DSI in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC) can facilitate optimal therapy decision.PurposeTo develop a nomogram to identify DSI in cervical AC/ASC.Study TypeRetrospective.PopulationSix hundred and fifty patients (mean age of 48.2 years) were collected from center 1 (primary cohort, 536), centers 2 and 3 (external validation cohorts 1 and 2, 62 and 52).Field Strength/Sequence5‐T, T2‐weighted imaging (T2WI, SE/FSE), diffusion‐weighted imaging (DWI, EPI), and contrast‐enhanced T1‐weighted imaging (CE‐T1WI, VIBE/LAVA).AssessmentThe DSI was defined as the outer 1/3 stromal invasion on pathology. The region of interest (ROI) contained the tumor and 3 mm peritumoral area. The ROIs of T2WI, DWI, and CE‐T1WI were separately imported into Resnet18 to calculate the DL scores (TDS, DDS, and CDS). The clinical characteristics were retrieved from medical records or MRI data assessment. The clinical model and nomogram were constructed by integrating clinical independent risk factors only and further combining DL scores based on primary cohort and were validated in two external validation cohorts.Statistical TestsStudent's t‐test, Mann–Whitney U test, or Chi‐squared test were used to compare differences in continuous or categorical variables between DSI‐positive and DSI‐negative groups. DeLong test was used to compare AU‐ROC values of DL scores, clinical model, and nomogram.ResultsThe nomogram integrating menopause, disruption of cervical stromal ring (DCSRMR), DDS, and TDS achieved AU‐ROCs of 0.933, 0.807, and 0.817 in evaluating DSI in primary and external validation cohorts. The nomogram had superior diagnostic ability to clinical model and DL scores in primary cohort (all P < 0.0125 [0.05/4]) and CDS (P = 0.009) in external validation cohort 2.Data ConclusionThe nomogram achieved good performance for evaluating DSI in cervical AC/ASC.Level of Evidence3Technical EfficacyStage 2

Editorial for “Validity and Reproducibility of ADNEX MR SCORING System in Diagnosis of Sonographically Indeterminate Adnexal Masses”

Editorial for “Histogram Analysis Comparison of Monoexponential, Advanced Diffusion‐ Weighted Imaging, and Dynamic Contrast‐Enhanced MRI for Differentiating Borderline From Malignant Epithelial Ovarian Tumors”

Level of Evidence4Technical EfficacyStage 2J. Magn. Reson. Imaging 2020;52:269–270.

MRI‐Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study

BackgroundPreoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results.PurposeTo develop and validate an objective MRI‐based machine‐learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation.Study TypeRetrospective study of eight clinical centers.PopulationIn all, 501 women with histopathologically‐confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159).Field Strength/SequencePreoperative MRI within 2 weeks of surgery. Single‐ and multiparameter (MP) machine‐learning assessment models were built utilizing the following four MRI sequences: T2‐weighted imaging (T2WI), fat saturation (FS), diffusion‐weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast‐enhanced (CE)‐T1WI.AssessmentDiagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early‐stage MEOT was made. Six radiologists of varying experience also interpreted the MR images.Statistical TestsMann–Whitney U‐test: significance of the clinical characteristics; chi‐square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC).ResultsThe MP‐ST model performed better than the MP‐WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early‐stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679–0.924) and external (mean AUC = 0.797; range, 0.744–0.867) validation cohorts.Data ConclusionPerformance of the MRI‐based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women.Level of EvidenceLevel 4.Technical EfficacyStage 2. J. Magn. Reson. Imaging 2020;52:897–904.

Histogram Analysis Comparison of Monoexponential, Advanced Diffusion‐Weighted Imaging, and Dynamic Contrast‐Enhanced MRI for Differentiating Borderline From Malignant Epithelial Ovarian Tumors

BackgroundThe accurate preoperative differentiation between borderline and malignant epithelial ovarian tumors (BEOTs vs. MEOTs) is crucial for determining the proper surgical strategy and improving the patient's postoperative quality of life. Several diffusion and perfusion MRI technologies are valuable for the differentiation; however, which is the best remains unclear.PurposeTo compare the whole solid‐tumor volume histogram analysis of diffusion‐weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and dynamic contrast‐enhanced MRI (DCE‐MRI) in the differentiation of BEOTs vs. MEOTs and to identify the correlations between the perfusion parameters from IVIM and DCE‐MRI.Study TypeRetrospective.PopulationTwenty patients with BEOTs and 42 patients with MEOTs.Field Strength/Sequence1.5T/DWI, DKI, and IVIM models fitting from 13 different b factors and 40 phases DCE‐MRI.AssessmentHistogram metrics were derived from the apparent diffusion coefficient (ADC), diffusion kurtosis (K), diffusion coefficient (Dk), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), volume transfer constant (Ktrans), rate constant (kep), and extravascular extracellular volume fraction (ve).Statistical TestsThe Mann–Whitney U‐test and receiver operating characteristic curve were used to determine the best histogram metrics and parameters. Multivariate logistic regression analysis was used to determine the best combined model for each two from the four technologies. Spearman's rank correlation was used to analyze the correlations between the IVIM and DCE‐MRI parameters.ResultsADC, D, Dk, and D* were significantly higher in BEOTs than in MEOTs (P < 0.05). K, Ktrans, kep, and ve were significantly lower in BEOTs than in MEOTs (P < 0.05). The 10th percentile of Dk was the most reliable single metric, with an area under the curve (AUC) of 0.921. Dk combined with Ktrans yielded the highest AUC of 0.950. A weak inverse correlation was found between D and Ktrans (r = −0.320, P = 0.025) and between D and kep (r = −0.267, P = 0.037).Data ConclusionThe 10th percentile of Dk was the most valuable metric and Dk combined with Ktrans had the best performance for differentiating BEOTs from MEOTs. There was no evident link between perfusion‐related parameters derived from IVIM and DCE‐MRI.Level of Evidence: 4Technical Efficacy Stage: 2J. Magn. Reson. Imaging 2020;52:257–268.

Novel T2 Mapping for Evaluating Cervical Cancer Features by Providing Quantitative T2 Maps and Synthetic Morphologic Images: A Preliminary Study

BackgroundThe application value of T2 mapping in evaluating cervical cancer (CC) features remains unclear.PurposeTo investigate the role of T2 values in evaluating CC classification, grade, and lymphovascular space invasion (LVSI) in comparison to apparent diffusion coefficient (ADC), and to compare synthetic T2‐weighted (T2W) images calculated from T2 values to conventional T2W images for CC staging.Study TypeRetrospective.PopulationSixty‐three patients with histopathologically confirmed CC.Field Strength/Sequence3T, conventional T2W turbo spin‐echo, diffusion‐weighted echo‐planar, and accelerated T2 mapping sequence.AssessmentT2 and ADC values between different pathological features of CC were compared. The diagnostic accuracies of conventional and synthetic T2W images in staging were also compared.Statistical TestsParameters were compared using an independent t‐test, Wilcoxon signed‐rank test, and the chi‐square test. Receiver operating characteristic analysis was performed.ResultsThe T2 values varied significantly between well/moderately differentiated and poorly differentiated tumors ([92.8 ± 9.5 msec] vs. [83.8 ± 9.5 msec], P < 0.05) and between LVSI‐positive and LVSI‐negative CC ([82.2 ± 8.2 msec] vs. [93.9 ± 9.1 msec], P < 0.05). The ADC values showed a significant difference for grade ([0.76 ± 0.10 × 10−3 mm2/s] vs. [0.65 ± 0.11 × 10−3 mm2/s], P < 0.05) and no difference for LVSI status ([0.71 ± 0.11× 10−3 mm2/s] vs. [0.73 ± 0.12× 10−3 mm2/s], P = 0.472). There was no significant difference in T2 and ADC values between squamous cell carcinoma and adenocarcinoma (P = 0.378 and P = 0.661, respectively). In MRI staging, the conventional and synthetic T2W images resulted in a similar accuracy (71% vs. 68%, P = 0.698).Data ConclusionThe accelerated T2 mapping sequence may facilitate grading and staging of CC by providing quantitative T2 maps and synthetic T2W images in one acquisition. T2 values may be superior to ADC in predicting LVSI.Level of Evidence2Technical Efficacy Stage2 J. MAGN. RESON. IMAGING 2020;52:1859–1869.

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

BackgroundEndometrial carcinoma (EC) risk stratification is generally based on histological assessment. It would be beneficial to perform risk stratification noninvasively by MRI.PurposeTo 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 TypeProspective.PopulationEighty 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/SequenceT1‐weighted imaging, T2‐weighted imaging, IVIM, APTWI, and DKI MRI at 3 T.AssessmentThe 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 TestsIndividual sample t test, analysis of variance, and logistic regression. A P‐value <0.05 was considered statistically significant.ResultsThe 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 ConclusionThe IVIM, DKI, and APTWI parameters have potential as imaging markers for risk stratification in early‐stage EC.Level of Evidence2Technical EfficacyStage 3