Magnetic Resonance Imaging

The value of multi-parameters MRI based on amide proton transfer imaging in evaluating the efficacy of concurrent radiochemotherapy for locally advanced cervical cancer

To evaluate the utility of multi-parametric magnetic resonance imaging (MRI) based on amide proton transfer (APT) imaging in assessing the efficacy of concurrent chemoradiotherapy (CCRT) for locally advanced cervical cancer (CC). We retrospectively analyzed clinical and imaging data from pathologically confirmed CC patients treated at our hospital between January 2020 and May 2025. All patients underwent conventional MRI, APT imaging, and dynamic contrast-enhanced MRI (DCE-MRI) prior to treatment. Apparent diffusion coefficient (ADC) values, APT values, and semi-quantitative parameters from DCE-MRI were measured. Treatment response following CCRT was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST). Complete remission (CR) and partial remission (PR) were classified as the remission group (RG), while stable disease (SD) and progressive disease (PD) were grouped as the non-remission group (NRG). Differences in APT values, ADC values, and semi-quantitative parameters of DCE-MRI between RG and NRG were analyzed. Receiver operating characteristic (ROC) curves were used to assess the sensitivity, specificity, and area under the curve (AUC) of APT, ADC, semi-quantitative parameters of DCE-MRI, and their combinations in predicting CCRT response. The Delong test was used for statistical comparison of AUCs. A total of 43 patients were included, with 5 (11.63 %) achieving CR, 31 (72.09 %) achieving PR, 5 (11.63 %) classified as SD, and 2 (4.65 %) as PD, resulting in a local control rate of 95.35 %. The APT value in the RG was significantly lower than in the NRG (3.13 % ± 0.24 % vs. 3.36 % ± 0.16 %, P = 0.02), while the ADC value was significantly higher [(0.89 ± 0.11) × 10 Multi-parametric MRI based on APT imaging holds promise for predicting CCRT outcomes in locally advanced CC. The combination of APT, ADC, and WIR improves diagnostic accuracy compared to individual parameters, offering a potential non-invasive tool for guiding clinical management after CCRT.

Dynamic contrast-enhanced and diffusion-weighted MRI of cervical carcinoma: Correlations with Ki-67 proliferation status

To investigate the association of quantitative parameter (apparent diffusion coefficient [ADC]) from diffusion-weighted imaging (DWI) and various quantitative and semiquantitative parameters from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with Ki-67 proliferation index (PI) in cervical carcinoma (CC). A total of 102 individuals with CC who received 3.0 T MRI examination (DWI and DCE MRI) between October 2016 and December 2022 were enrolled in our investigation. Two radiologists separately assessed the ADC parameter and various quantitative and semiquantitative parameters including (volume transfer constant [K Both ADC (ρ = -0.457, p < 0.001) and K ADC, K

Comparison of MRI features among squamous cell carcinoma, adenocarcinoma and adenosquamous carcinoma, usual-type endocervical adenocarcinoma and gastric adenocarcinoma of cervix

To compare and explore the characteristics of squamous cell carcinoma (SCC), adenocarcinoma (AC) and adenosquamous carcinoma (ASC), usual-type endocervical adenocarcinoma (UEA) and gastric adenocarcinoma (GAC) of cervix. A total of 728 cervical cancers (254 cases of AC, 252 cases of ASC, and 222 cases of SCC) confirmed by histopathology were retrospectively reviewed. Among AC, 119 UEA and 47 GAC were included. Clinical baseline data and tumor morphological features on MRI (including tumor location, shape, diameter and volume, margin, growth pattern, presence of fluid component or cyst, heterogenous and peritumoral enhancement) of all cases were collected and analyzed. The signal intensity (SI) of tumor and gluteus maximus muscle were measured and their ratios (SIR) were calculated based on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) and contrast-enhanced T1WI at arterial and delay phases (A/DCE-T1WI). These clinical and MRI features were compared between SCC, AC and ASC, UEA and GAC, and the specific ones of each subtype were identified. There was a significant difference in SCC-Ag, CA-199, CEA, ADC value, SIR-DWI, presence of intratumor cyst and peritumoral enhancement between AC and ASC; in patient age, menopausal status, International Federation of Gynecology and Obstetrics (FIGO) stage, SCC-Ag, CA-125, CA-199, CEA, tumor shape, growth pattern, margin, presence of intratumor fluid component and cyst, tumor diameter and volume, ADC value, SIR-T1WI, SIR-T2WI, and SIR-DWI between SCC and AC, as well as SCC and ASC. Also, there was a significant difference in deep stromal invasion (DSI), peritumoral and heterogenous enhancement between SCC and AC, and in SIR-ACE-T1WI between SCC and ASC. There was a significant difference in reproductive history, menopausal status, FIGO stage, CA-199, DSI, lymph node metastasis (LNM), parametrial invasion (PMI), tumor location, shape, margin, growth pattern, presence of fluid component and cyst, tumor diameter and volume, SIR-T1WI, SIR-DWI, and heterogenous enhancement between GAC and UEA. The clinical and MRI features with significant differences among SCC, AC and ASC, and between UEA and GAC, can help to identify each subtype of cervical cancer.

Amide proton transfer weighted MRI in differential diagnosis of ovarian masses with cystic components: A preliminary study

To evaluate the performance of three-dimensional (3D) amide proton transfer-weighted (APTw) MRI in the differentiation between benign and malignant ovarian masses based on single-slice and all-slice analysis of cystic regions. Patients were consecutively recruited and underwent conventional pelvic MRI and APTw MRI. Two radiologists independently assessed ovarian masses blinded to the histopathological results. Three APTw SI values were generated from the cystic regions of the masses: (1) APTw SI of a single representative slice (RS); (2) average (AVE) of APTw SIs of all slices of the mass; (3) area-weighted (AW) average of APTw SIs of all slices of the mass. O-RADS MRI score of each mass was reported. Independent sample t-test and receiver operating characteristic (ROC) curve analysis were performed for comparison. Inter- and intra-observer reliability were assessed by the intraclass correlation coefficient (ICC) and quadratic kappa coefficient. 46 ovarian masses were included for final analysis. The three APTw SI values were higher in cystic regions of malignant ovarian masses compared with benign lesions (p<0.0001). ROC curve analysis showed no significant difference in diagnostic performance among three APTw SI values and the O-RADS MRI score (AUC: RS-APTw SI, 0.930; AVE-APTw SI, 0.927; AW-APTw SI, 0.935; O-RADS score, 0.937). APTw MRI may be used as a noninvasive tool for the differentiation of benign and malignant ovarian masses based on the analysis of the cystic regions.

3D MR elastography-based stiffness as a marker for predicting tumor grade and subtype in cervical cancer

Increasing evidence has indicated that high tissue stiffness (TS) may be a potential biomarker for evaluation of tumor aggressiveness. To investigate the value of magnetic resonance elastography (MRE)-based quantitative parameters preoperatively predicting the tumor grade and subtype of cervical cancer (CC). Retrospective. Twenty-five histopathology-proven CC patients and 7 healthy participants. 3.0T, magnetic resonance imaging (MRI) (LAVA-flex) and MRE with a three-dimensional spin-echo echo-planar imaging. The regions of interest (ROIs) were manually drawn by two observers in tumors to measure mean TS, storage modulus (G'), loss modulus (G″) and damping ratio (DR) values. Surgical specimens were evaluated for tumor grades and subtypes. Intraclass correlation coefficient (ICC) was expressed in terms of inter-observer agreements. t-test or Mann-Whitney nonparametric test was used to compare the complex modulus and apparent diffusion coefficient (ADC) values between different tumor groups. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the diagnostic performance. The TS of endocervical adenocarcinoma (ECA) group was significantly higher than that in squamous cell carcinoma (SCC) group (5.27 kPa vs. 3.44 kPa, P = 0.042). The TS also showed significant difference between poorly and well/moderately differentiated CC (5.21 kPa vs. 3.47 kPa, P = 0.038), CC patients and healthy participants (4.18 kPa vs. 1.99 kPa, P < 0.001). The cutoff value of TS to discriminate ECA from SCC was 4.10 kPa (AUC: 0.80), while it was 4.42 kPa to discriminate poorly from well/moderately differentiated CC (AUC: 0.83), and 2.25 kPa to distinguish normal cervix from CC (AUC: 0.88), respectively. There were no significant difference in G″, DR and ADC values between any subgroups except for comparison of healthy participants and CC patients (P = 0.001, P = 0.004, P < 0.001, respectively). 3D MRE-assessed TS shows promise as a potential biomarker to preoperatively assess tumor grade and subtype of CC.

Evaluation of multiplexed sensitivity encoding diffusion-weighted imaging in detecting uterine lesions: Image quality optimization

To compare the image quality of multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and single-shot echo-planar imaging (SS-EPI-DWI) techniques in uterine MRI. Eighty-eight eligible patients underwent MUSE-DWI and SS-EPI-DWI examinations simultaneously using a 3.0 T MRI system. Two radiologists independently performed quantitative and qualitative analysis of the two groups of images using a double-blind method. The weighted Kappa test was used to evaluate the interobserver agreement. Wilcoxon's rank sum test was used for qualitative parameters, and paired t-test was used for quantitative parameters. Spearman rank correlation analysis was used to obtained correlation between pathological results and mean apparent diffusion coefficient (ADC) value. The qualitative and quantitative analysis of the images by the two radiologists were in good or excellent agreement, with weighted kappa value ranging from 0.636 to 0.981. The scores of total subjective image quality (15.4 ± 0.99) and signal-to-noise ratio (158.99 ± 60.71) of MUSE-DWI were significantly higher than those of SS-EPI-DWI (12.93 ± 1.62 P < 0.001; 130.23 ± 48.29 P < 0.05). It effectively reduced image distortion and artifact, and had better lesion conspicuity. There was no significant difference in contrast-to-noise ratio score and average ADC values between the two DWI sequences. The average ADC values of the two DWI sequences were highest in the normal uterus group and lowest in the endometrial cancer group, with statistically significant differences among groups (P < 0.01). In addition, the average ADC values of the two DWI sequences were negatively correlated with the type of lesions, decreasing with the malignancy of the lesions (r = -0.805 P < 0.01, r = -0.815 P < 0.01). Compared to SS-EPI-DWI, MUSE-DWI can significantly reduce distortion, artifacts, and fuzziness in MRI of uterine lesions, which is more conducive to lesion detection.

Amide proton transfer weighted combined with diffusion kurtosis imaging for predicting lymph node metastasis in cervical cancer

To investigate the value of amide proton transfer weighted (APTw) combined with diffusion kurtosis imaging (DKI) in quantitative prediction of lymph node metastasis (LNM) in cervical carcinoma (CC). Data of 19 LNM(+) and 50 LNM(-) patients with CC were retrospectively analyzed. 3.0 T MRI scan was performed before the operation, including APTw and DKI. After post-processing, quantitative magnetization transfer ratio asymmetric at 3.5 ppm [MTRasym (3.5 ppm)], mean kurtosis (MK), and mean diffusivity (MD) maps were obtained. The MTRasym(3.5 ppm), MK, and MD values were respectively measured by two observers, and intra-class correlation coefficients (ICC) were used to test the consistency of the results. The independent samples t-test or Mann-Whitney U test was used to compare the differences in the values of each parameter. The ROC curve was used to analyze the predictive performance of parameters with significant differences and their combination parameter. The two observers had good agreement in the measurement of each data (ICC > 0.75). The MTRasym(3.5 ppm) and MK values of the LNM(+) group(3.260 ± 0.538% and 0.531 ± 0.202) were higher than those of the LNM(-) group(2.698 ± 0.597% and 0.401 ± 0.148) (P  0.05). The area under the curves (AUCs) of MTRasym(3.5 ppm), MK value, and MTRasym(3.5 ppm) + MK value were 0.763, 0.716, and 0.813, respectively, when predicting LNM status of CC. APTw and DKI can quantitatively predict LNM status of CC, which is of importance in clinical diagnosis and treatment.

MRI-based radiomics features for the non-invasive prediction of FIGO stage in cervical carcinoma: A multi-center study

To develop and validate a model based on MRI radiomics modals for predicting surgical high FIGO(IB3 and ≥ IIA2) and low FIGO(IB1, IB2, and IIA1) stages in patients with cervical carcinoma (CC). A total of 296 early-stage patients with CC (preoperative FIGO stages IB-IIA) confirmed by surgery and pathology were included in this retrospective study from two institutions For each patient,we extracted radiomics features from spectral attenuated inversion-recovery T2-weighted (SPAIR-T2W) and contrast-enhanced T1-weighted (CE-T1W) images.Manual segmentation was performed using the 3D Slicer software, while radiomics features were extracted, screened using the R software. A 2-stage feature extraction strategy involving univariate analysis and the Least Absolute Shrinkage Selection Operator technique was performed. A support vector machine-based model was eventually constructed. Predictive accuracy of the training and validation datasets was assessed in terms of area under the ROC curve (AUC). A total of 1130 features were extracted from SPAIR-T2WI and CET1WI images respectively, in which 8 and 7 features significantly were associated with FIGO staging. AUCs of the SPAIR-T2W and CE-T1W models were were 0.803 and 0.790, respectively, in the internal validation group. In the external validation group, the AUCs were 0.767 and 0.749, respectively, which increased to 0.771 in the combined model. Our study demonstrated the feasibility of radiomics features from SPAIR-T2W and CE-T1W images for the prediction of surgical FIGO stage in CC. Our proposed model thereby carries the potential as a non-invasive tool for the staging and treatment planning of this disease. A radiomics model provide a non-invasive and objective method for the detection of FIGO staging in patients with cervical cancer before surgery, thus providing a reference for the selection of treatment options for patients.

IVIM-DWI and MRI-based radiomics in cervical cancer: Prediction of concurrent chemoradiotherapy sensitivity in combination with clinical prognostic factors

To identify the feasibility and value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and magnetic resonance imaging (MRI)-based radiomics combined with clinical prognostic factors (CPF) in predicting concurrent chemoradiotherapy (CCRT) sensitivity of locally advanced cervical cancer (LACC). A retrospective analysis of 163 patients (assigned to training or test groups) who underwent conventional MRI and IVIM-DWI before CCRT were divided into sensitive and resistant groups according to their efficacy at 6 months after CCRT. Per-treatment IVIM-DWI parameters (ADC, D, D Clinical stage, f value, D value, InverseVariance, SizeZoneNonUniformity, and Minimum were selected to construct prediction model. All parameters except D value showed independent diagnostic value in multivariate Logistic regression analysis and composed prediction model, with AUCs of 0.987 and 0.984 for training and test groups, respectively. The calibration curve (Brier score of 0.042, C-index of 0.987), decision curve and clinical impact curve further demonstrated the reliability and clinical value of prediction model. IVIM-DWI, MRI-based radiomics and CPF showed high clinical value in predicting CCRT sensitivity for LACC with better predictive performance when combined.

Time-dependent diffusion MRI for 2023 FIGO stage of uterine endometrial cancer

This study investigated the utility of time-dependent diffusion MRI in assessing pathological characteristics of endometrial cancer (EC) associated with the 2023 revised International Federation of Gynecology and Obstetrics (FIGO) stage, including histological type, substantial lymphovascular space invasion (LVSI), and lymph node metastasis (LNM). This retrospective single-center study included 93 patients with EC who underwent diffusion-weighted imaging (DWI) MRI with oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE) sequences. Mean apparent diffusion coefficient (ADC) values for OGSE (ADCOGSE) and PGSE (ADCPGSE) and ADCOGSE/ADCPGSE ratio were measured using tumor regions of interest. Mann-Whitney U test, receiver operating characteristic (ROC) curve analysis, and Spearman's rank correlation coefficients were conducted to evaluate the associations between ADC parameters and pathological factors. ADCPGSE was significantly lower in the presence of LNM (P = 0.011). ADCOGSE/ADCPGSE was significantly higher in aggressive type, substantial LVSI, LNM, and FIGO stages II-IV (all P < 0.001). Area under the ROC curve of the ADCOGSE/ADCPGSE ratio consistently demonstrated statistical superiority over ADCOGSE and ADCPGSE independently for the prediction of aggressive type (0.85, 95 % confidence interval [CI]: 0.76-0.91), substantial LVSI (0.91, 95 % CI: 0.83-0.96), LNM (0.93, 95 % CI: 0.85-0.97), and FIGO stages II-IV (0.78, 95 % CI: 0.68-0.86) (all P < 0.05). ADCOGSE/ADCPGSE was the only metric significantly correlated with the 2023 FIGO stage (P < 0.001, ρ = 0.50). Time-dependent diffusion MRI effectively identifies EC characteristics associated with the 2023 FIGO stage.

Value of non-Gaussian diffusion imaging with a fractional order calculus model combined with conventional MRI for differentiating histological types of cervical cancer

This study aimed to evaluate the value of a fractional order calculus (FROC) model combined with conventional magnetic resonance imaging (MRI) for differentiating cervical adenocarcinoma (CAC) from squamous cell carcinoma (SCC). Diffusion-weighted imaging (DWI) with 9 b values (0-2000s/mm β was significantly lower in the CAC group than in the SCC group (0.682 ± 0.054 vs. 0.723 ± 0.084, P = 0.035), while D and μ were not significantly different between the two groups (D, P = 0.171; μ, P = 0.127). There was no significant difference in the ADC value between the two groups (P = 0.053). In conventional MRI features, enhanced-T The FROC model can serve as a noninvasive and quantitative imaging technique for differentiating CAC from SCC. β combined with ADC and enhanced-T

Can the combination of DWI and T2WI radiomics improve the diagnostic efficiency of cervical squamous cell carcinoma?

To investigate the value of MRI multi-sequence imaging model in differentiation of cervical squamous cell carcinoma (CSCC). A total of 104 CSCC patients confirmed with pathology were retrospectively enrolled. All patients underwent conventional MRI examination before treatment. The lesions were segmented using ITK-SNAP software manually and radiomics features were extracted by Artificial Intelligence Kit (AK) software. 396 tumor texture features were obtained and then the mRMR and Lasso algorithms were used to reduce the feature dimension. Three models including T2WI model, DWI model and Joint model (combined TWI and DWI) were constructed in training group and evaluated in validation group. and the receiver operator characteristics and calibration curve were used to evaluate the model performance. The Joint model and T2WI model both showed a better diagnostic efficacy than single DWI model in differentiation of CSCC in training group (Joint model: AUC = 0.841; T2WI model: AUC = 0.804; DWI model: AUC = 0.732) and validation group (Joint model: AUC = 0.822; T2WI model: AUC = 0.791; DWI model: AUC = 0.724). But there was no statistical difference between Joint model and T2WI model by Delong test(P > 0.05). The study suggested that the conventional T2WI sequence may be more suitable for prognosis evaluation of CSCC, which can provide a potential tool to facilitate the differential diagnosis of low-differentiation and high-differentiation CSCC.

Pelvic bones ADC could help to predict severe hematologic toxicity in patients undergoing concurrent chemoradiotherapy for cervical cancer

Hematologic toxicity (HT) during concurrent chemoradiotherapy (CCRT) for cervical cancer can lead to treatment breaks and compromise efficacy. To evaluate the association between severe hematologic toxicity (HT) and clinical factors and pelvic apparent diffusion coefficient (ADC) during CCRT of cervical cancer patients. Data from 120 patients with cervical cancer who were treated with CCRT from January 2016 and December 2018 were retrospectively analyzed. The clinical data (age, menopausal status, clinical stage, body mass index, chemotherapy regimen and chemotherapy cycle) of the patients were collected, and the cohort were divided into two groups based on the HT grade: HT3+ group (HT grade ≥ 3; 66 patients) and HT3- group (HT grade<3; 54 patients). All patients performed MRI before CCRT, and pelvic (ilium, pubis, ischium) ADC value was measured on ADC map. The correlation between severe HT and clinical parameters and pelvic ADC value were analyzed by univariate analysis, and the diagnostic performance was further assessed by receiver operating characteristic (ROC) analysis. In univariate analysis, the menopausal status (p = 0.012) and chemotherapy regimen (p = 0.011) were significantly correlated with severe HT in overall patients, and menopausal patients or patients receiving paclitaxel plus cisplatin (TP) regimen were more likely to develop severe HT. HT3+ group showed a significantly lower pelvic ADC value than HT3- group. The ADC value cut-offs derived from our study for predicting severe HT was 0.317 × 10 Severe HT was significantly associated with menopausal status and chemotherapy regimen in patients with cervical cancer treated with CCRT, and HT3+ group showed a lower pelvic ADC value.

MRI radiomics in overall survival prediction of local advanced cervical cancer patients tread by adjuvant chemotherapy following concurrent chemoradiotherapy or concurrent chemoradiotherapy alone

To build radiomics based OS prediction tools for local advanced cervical cancer (LACC) patients treated by concurrent chemoradiotherapy (CCRT) alone or followed by adjuvant chemotherapy (ACT). And, to construct adjuvant chemotherapy decision aid. 83 patients treated by ACT following CCRT and 47 patients treated by CCRT were included in the ACT cohort and non-ACT cohort. Radiomics features extracted from primary tumor area of T2-weighted MRI. Two radiomics models were built for ACT and non-ACT cohort in prediction of 3 years overall survival (OS). Elastic Net Regression was applied to the the ACT cohort, meanwhile least absolute shrinkage and selection operator plus support vector machine was applied to the non-ACT cohort. Cox regression models was used in clinical features selection and OS predicting nomograms building. The two radiomics models predicted the 3 years OS of two cohorts. The receiver operator characteristics analysis was used to evaluate the 3 years OS prediction performance of the two radiomics models. The area under the curve of ACT and non-ACT cohort model were 0.832 and 0.879, respectively. Patients were stratified into low-risk group and high-risk group determined by radiomics models and nomograms, respectively. And, the low-risk group patients present significantly increased OS, progression-free survival, local regional control, and metastasis free survival compare with high-risk group (P < 0.05). Meanwhile the prognosis prediction performance of radiomics model and nomogram is superior to the prognosis prediction performance of Figo stage. The two radiomics model and the two nomograms is a prognosis predictor of LACC patients treated by CCRT alone or followed by ACT.

Integration of radiomics, habitat imaging, and deep learning for MRI-based prediction of parametrial invasion in cervical cancer: A dual-center study

To assess the diagnostic performance of radiomics, habitat imaging, and 2.5D deep learning models for MRI-based prediction of parametrial invasion in cervical cancer, and to evaluate the clinical utility of a multimodal integrated model. This dual-center retrospective study included 290 patients with FIGO stage IB1-IIB cervical cancer who underwent preoperative MRI. Patients from Center A (n = 227) were divided into training and validation cohorts, while patients from Center B (n = 63) comprised the external test cohort. Radiomic features were extracted, habitat imaging was performed using k-means clustering, and a 2.5D deep learning model incorporated adjacent slices. Feature selection was conducted using Pearson correlation and LASSO regression. Machine learning models were developed, and an integrated model was constructed. Model performance was evaluated using AUC and accuracy. AUCs were compared with DeLong tests, calibration was assessed with the Hosmer-Lemeshow test, and clinical utility was evaluated with decision curve analysis. The integrated model outperformed all individual models, achieving AUCs of 0.973, 0.901, and 0.906 in the training, validation, and external test cohorts, respectively. Among individual models, the deep-learning model showed the highest AUCs (0.954, 0.803, 0.833), followed by habitat imaging (0.860, 0.811, 0.843). In the external test cohort, the peritumoral radiomics model outperformed the intratumoral model (0.843 vs. 0.719). The clinical model showed the lowest performance. Hosmer-Lemeshow tests indicated good calibration, and decision curve analysis confirmed superior clinical utility of the integrated model. The multimodal integrated model, combining radiomics, habitat imaging, 2.5D deep learning, and clinical features, demonstrated superior predictive performance for parametrial invasion in cervical cancer compared with individual models. This approach may enhance preoperative assessment, guide clinical decision-making, and optimize treatment strategies.

The value of amide proton transfer imaging in predicting parametrial invasion and lymph-vascular space invasion of cervical cancer

To explore the value of amide proton transfer (APT) imaging in assessing parametrial invasion (PMI) and lymph-vascular space invasion (LVSI) of cervical cancer. We retrospectively analyzed the clinical and imaging data of cervical cancer patients diagnosed pathologically at our hospital from January 2021 to June 2024. All patients underwent routine magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and APT imaging before treatment. Apparent diffusion coefficient (ADC) and APT values were measured. Based on the pathological results, patients were categorized into LVSI (+) and LVSI (-) groups, and PMI (+) and PMI (-) groups. Independent sample t-tests were used to compare the ADC and APT values between these groups. Receiver operating characteristic (ROC) curves were used to assess the sensitivity, specificity, and area under the curve (AUC) of ADC, APT, and ADC + APT in predicting PMI and LVSI. The Delong test was employed to compare the diagnostic performance among these measures. A total of 83 patients were included, with 56 in the LVSI (-) group, 27 in the LVSI (+) group, 35 in the PMI (-) group, and 16 in the PMI (+) group. The ADC values for the LVSI (+) and PMI (+) groups were significantly lower than those for the LVSI (-) and PMI (-) groups (P < 0.01). The APT values for the LVSI (+) and PMI (+) groups were significantly higher than those for the LVSI (-) and PMI (-) groups (P < 0.01). The AUC values for ADC, APT, and the combination of ADC + APT in predicting LVSI were 0.839, 0.788, and 0.880, respectively, and in predicting PMI were 0.770, 0.764, and 0.796, respectively. There were no statistically significant differences in the diagnostic performance of ADC, APT, and ADC + APT in predicting PMI. However, the diagnostic performance of ADC + APT in predicting LVSI was significantly better than that of ADC and APT alone (P < 0.01). APT imaging can predict LVSI and PMI status in cervical cancer before surgery. When combined with ADC, its diagnostic accuracy for predicting LVSI is higher than that of APT or ADC alone. This suggests a novel approach for assessing LVSI in cervical cancer.

Classifying early stages of cervical cancer with MRI-based radiomics

This study aims to establish a MRI-based classifier to distinguish early stages of cervical cancer with improved diagnostic performance to assist clinical diagnosis and treatment. 57 patients with pathological diagnosis of cervical cancer from January 2018 to May 2019 were enrolled in this study. MRI examinations, including T1-weighted image(T1WI), T2-weighted image(T2W), diffusion weighted imaging (DWI) and dynamic contrast enhanced (DCE), were performed before surgery. MR images from patients of stage Ib or IIa cervical cancer with tumor segmented were used as input. Feature extraction process extracted first-order statistics and texture and applied filters. The dimensionality of the radiomic features was reduced using the least absolute shrinkage and selection operator (LASSO). Models were trained by three machine-learning (k-nearest neighbor (KNN), support vector machine (SVM), and logistic regression (LR)) and diagnostic performance in differentiating stage Ib and stage IIa cases was evaluated. A total of 27 features were extracted to establish models, including 2 features from T1WI, 5 features from T2WI, 5 features from DWI (b = 50), 4 features from DWI (b = 800), 5 features from DCE, and 6 features from ADC. For each machine learning (ML) classifier, six sequences of training set and testing set are modeled and analyzed. Among all the models, the training set and testing set of T2WI model built by SVM classifier were the best (Area under the curve (AUC) 0.915) / (AUC 0.907). Radiomic analysis of ML-based texture features and first-order statistics features can be used to stage the early cervical cancer pre-operatively.

Prediction of early response to concurrent chemoradiotherapy in cervical cancer: Value of multi-parameter MRI combined with clinical prognostic factors

This study aimed to investigate the prediction of early response to concurrent chemoradiotherapy (CCRT) through a combination of pretreatment multi-parametric magnetic resonance imaging (MRI) with clinical prognostic factors (CPF) in cervical cancer patients. Eighty-five patients with pathologically confirmed cervical cancer underwent conventional MRI, intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) before CCRT. The patients were divided into non- and residual tumor groups according to post-treatment MRI. Univariable and multivariable analyses were performed to pretreatment MRI parameters and CPF between the two groups, and optimal thresholds and predictive performance for post-treatment residual tumor occurrence were estimated by drawing the receiver operating characteristic (ROC) curve. There were 52 patients in non- and 33 in residual group. The residual group showed a lower perfusion fraction (f) value and volume transfer constant (K Pretreatment D, K

MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study

To evaluate intra- and preitumoral radiomics on the contrast-enhanced T1-weighted (CE-T1) and T2-weighted (T2W) MRI for predicting the LNM, and develop a nomogram for potential clinical uses. We enrolled 169 cervical cancer cases who underwent CE-T1 and T2W MR scans from two hospitals between Dec. 2015 and Sep. 2021. Intra- and peritumoral features were extracted separately and selected by the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures were built using the selected features from different regions. Clinical parameters were evaluated by statistical analysis. The nomogram was developed combining the multi-regional radiomics signature and the most predictive clinical parameters. Five radiomics features were finally selected from the peritumoral regions with 1 and 3 mm distances in the CE-T1 and T2W MRI, respectively. The nomogram incorporating multi-regional combined radiomics signature, MR-reported LN status and tumor diameter achieved the highest AUCs in the training (nomogram vs. combined radiomics signature vs. clinical model, 0.891 vs. 0.830 vs. 0.812), internal validation (nomogram vs. combined radiomics signature vs. clinical model, 0.863 vs. 0.853 vs. 0.816) and external validation (nomogram vs. combined radiomics signature vs. clinical model, 0.804 vs. 0.701 vs. 0.787) cohort. DCA suggested good clinical usefulness of our developed models. The current work suggested clinical potential for intra- and peritumoral radiomics with multi-modal MRI for preoperative predicting LNM.

The combined application of amide proton transfer imaging and diffusion kurtosis imaging for differentiating stage Ia endometrial carcinoma and endometrial polyps

This study aims to explore the diagnostic performance of amide proton transfer (APT) imaging combined with diffusion kurtosis imaging (DKI) in differentiating stage Ia endometrial carcinoma (EC) and endometrial polyps (EP). All patients were scanned with APT and DKI sequences with 3.0 T MRI before surgery. The MRI data of 32 patients with histopathologically confirmed stage Ia EC and 17 patients with EP were retrospectively analyzed. Amide proton transfer, mean kurtosis (MK) and mean diffusivity (MD) values were measured. ROC curves were employed to evaluate the efficiency of differentiating and diagnosing stage Ia EC and EP, followed by the Delong test to compare the differences between the areas under the curve (AUCs) of each parameter. Additionally, the Pearson correlation analysis was performed to assess the correlation between APT values and MK and MD. The measured APT, MK, and MD values of the patients with stage Ia EC were 2.609 ± 0.504%,0.641 ± 0.113 and 0.904(0.816, 1.108) μm Both APT and DKI effectively differentiated stage Ia EC and EP; however, when combined, APT and DKI improved the ability to differentiate these diseases, boosting the value of using a combination of these modalities in clinical applications.

Evaluation of the diagnostic value of DCE-MRI radiomics features and K-trans parameters in differentiating endometrial cancer from submucosal uterine fibroids

To explore the diagnostic value of Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) radiomics features and qualitative parameters for the differential diagnosis of endometrial cancer (EC) and submucosal uterine fibroidsuterine fibroids. This retrospective study included 70 cases of endometrial cancer patients collected from our hospital between October 2022 and October 2024, assigned to the EC group, and another 35 cases of uterine leiomyoma patients during the same period were collected as the benign group according to the 2:1 matching principle. Baseline data, DCE-MRI parameters [rate constant (Kep), volume transport constant (Ktrans), and volume fraction of extracellular space (Ve)] and radiomics characteristics were compared between the two groups. The influencing factors of DCE-MRI parameters and radiomics features on EC were analyzed, as well as their diagnostic value in differentiation, and external validation of the diagnostic value in differentiation was conducted. Ktrans and Kep in EC group were higher than those in benign group, while Ve was lower (P < 0.05). The radiomic score of the EC group was higher than that of the benign group (P < 0.05). Logistic regression analysis found that Ktrans, Kep, Ve, and radiomic score were factors affecting EC (P < 0.05). The AUC values for the DCE-MRI model, radiomics model, and combined model in predicting the differential diagnosis of EC were 0.695, 0.775, and 0.867, respectively. Among them, the combined model demonstrated the highest predictive value, significantly surpassing that of the DCE-MRI and radiomics models (P < 0.05). The decision curve indicated that the clinical positive benefit achieved by the combined model in differential diagnosis of EC surpassed that of the DCE-MRI and radiomics models (P < 0.05). The calibration curve showed that the calibration curve for differential diagnosis of EC fitted well with the ideal curve. The external validation results demonstrated that the combined diagnostic model exhibited good predictive value. The combination of DCE-MRI parameters and radiomics features can be used for the differential diagnosis of EC, demonstrating good predictive efficacy and clinical applicability, providing a reliable imaging diagnostic method for clinical diagnosis of EC.

High correlation between Ki-67 expression and a novel perfusion MRI biomarker diffusion-derived vessel density (DDVD) in endometrial carcinoma

This study aimed to investigate the feasibility of diffusion-derived vessel density (DDVD) in characterizing tumor microvasculature in endometrial carcinoma (EC), and to explore the correlations with Ki-67 proliferation status and histological type based on DDVD values. There were in total 81 EC patients. There were 64 cases of non-aggressive histological type, and 17 cases of aggressive histological type. Ki-67 labeling index was low (<50 %) in 35 cases and high (≥50 %) in 46 cases. DDVD Endometrial carcinoma showed lower DDVD DDVD can act as an imaging marker reflecting Ki-67 proliferation and histological aggressiveness of EC, thus helping pretreatment risk assessment in EC.

Associations between MRI radiomic phenotypes and clinical outcomes in endometrial cancer: Implications for preoperative risk stratification

This study aimed to investigate the correlation between imaging phenotypes of endometrial cancer (EC) and clinical, pathologic, and molecular characteristics, as well as disease-free survival (DFS). The clinical, pathologic, and molecular characteristics, along with MRI radiomics features, of 356 patients with EC were collected retrospectively. The patients were divided into 2 groups based on radiomics features using unsupervised machine learning. The obtained characteristics and DFS of patients were compared between the various imaging phenotypes. The lesions with deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), cervical stromal invasion (CSI), lymph node metastasis, aggressive histologic type, advanced postoperative International Federation of Gynecology and Obstetrics (FIGO) stage, overexpression of p53, and absent expression of estrogen receptor or progesterone receptor were associated with poor DFS. Two clusters were identified and defined as imaging phenotype 1 and 2, respectively. Compared with phenotype 2, phenotype 1 exhibited a higher correlation with DMI (33.7 % vs 13.0 %), LVSI (23.8 % vs 9.2 %), CSI (16.3 % vs 3.8 %), aggressive histologic type (36.0 % vs 17.4 %), and advanced FIGO stage (IB or higher, 43.6 % vs 22.3 %) (p < 0.001). The incidence of p53 overexpression was higher in phenotype 1 than in phenotype 2 (20.2 % vs 8.5 %, p = 0.022). Survival analysis exhibited a higher risk of poor DFS in phenotype 1 than in phenotype 2 (log-rank p = 0.002). EC imaging phenotypes identified through MRI radiomics features were associated with pathologic, molecular characteristics, and DFS, suggesting potential for preoperative risk stratification.

Preoperative risk stratification of early-stage endometrial cancer assessed by multimodal magnetic resonance functional imaging

Endometrial cancer is a common disease in women. Stratifying the risk of early-stage endometrial cancer can aid in personalized treatment for patients. Risk stratification is primarily based on tumor grade, histological type, lymph node metastasis, and depth of myometrial invasion. Multimodal magnetic resonance functional imaging (including DCE-MRI, DWI, IVIM, DTI, DKI) has significant value in assessing the extent of myometrial and cervical infiltration, extrauterine involvement range, determining lymph node metastasis and tumor size. This article provides a brief overview of these techniques.

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

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. 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. APTw values and Dt, D*, f of all type I EC were 2.9 ± 0.1%, 0.677 ± 0.027 × 10 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.

Improved differentiation between stage I-II endometrial carcinoma and endometrial polyp with combination of APTw and IVIM MR imaging

To assess the value of amide proton transfer weighted (APTw) combined with intra-voxel incoherent motion (IVIM) imaging in differential diagnosis of stage I-II endometrial carcinoma (EC) and endometrial polyp (EP). A total of 53 female patients (37 cases with EC and 16 cases with EP) confirmed by surgical resection or biopsy from June 2019 to Jan. 2022 were retrospectively reviewed. All patients underwent 3.0 T magnetic resonance imaging (MRI) examination including diffusion weighted imaging (DWI), APTw and IVIM scans. The pure diffusion coefficient (D), pseudo-diffusion coefficient (D There was no significant difference in clinical manifestations between the two groups (P > 0.05). APT and D Both APT and IVIM parameters showed statistical differences between EC and EP. With combination of APT and IVIM parameters, the diagnostic accuracy between EC and EP can be significantly improved.

Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists' decisions of deep myometrial invasion

To evaluate radiomic machine learning (ML) classifiers based on multiparametric magnetic resonance images (MRI) in pretreatment assessment of endometrial cancer (EC) risk factors and to examine effects on radiologists' interpretation of deep myometrial invasion (dMI). This retrospective study examined 200 consecutive patients with EC during January 2004 -March 2017, divided randomly to Discovery (n = 150) and Test (n = 50) datasets. Radiomic features of tumors were extracted from T2-weighted images, apparent diffusion coefficient map, and contrast enhanced T1-weighed images. Using the Discovery dataset, feature selection and hyperparameter tuning for XGBoost were performed. Ten classifiers were built to predict dMI, histological grade, lymphovascular invasion (LVI), and pelvic/paraaortic lymph node metastasis (PLNM/PALNM), respectively. Using the Test dataset, the diagnostic performances of ten classifiers were assessed by the area under the receiver operator characteristic curve (AUC). Next, four radiologists assessed dMI independently using MRI with a Likert scale before and after referring to inference of the ML classifier for the Test dataset. Then, AUCs obtained before and after reference were compared. In the Test dataset, mean AUC of ML classifiers for dMI, histological grade, LVI, PLNM, and PALNM were 0.83, 0.77, 0.81, 0.72, and 0.82. AUCs of all radiologists for dMI (0.83-0.88) were better than or equal to mean AUC of the ML classifier, which showed no statistically significant difference before and after the reference. Radiomic classifiers showed promise for pretreatment assessment of EC risk factors. Radiologists' inferences outperformed the ML classifier for dMI and showed no improvement by review.