The contrast-free diffusion MRI multiple index for the early prediction of pathological response to neoadjuvant chemotherapy in breast cancer.

Early tumor response prediction can help avoid overtreatment with unnecessary chemotherapy sessions. It is important to determine whether multiple apparent diffusion coefficient indices (S index, ADC-diff) are effective in the early prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer (BC). Patients with stage II and III BCs who underwent T1WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI using a 3 T system were included. They were divided into two groups: major histological responders (MHRs, Miller-Payne G4/5) and nonmajor histological responders (nMHRs, Miller-Payne G1-3). Three b values were used for DWI to derive the S index; ADC-diff values were obtained using b = 0 and 1000 s/mm2. The different interquartile ranges of percentile S-index and ADC-diff values after treatment were calculated and compared. The assessment was performed at baseline and after two and four NAC cycles. A total of 59 patients were evaluated. There are some correlations of interquartile ranges of S-index parameters and ADC-diff values with histopathological prognostic factors (such as estrogen receptor and human epidermal growth factor receptor 2 expression, all p < 0.05), but no significant differences were found in some other interquartile ranges of S-index parameters or ADC-diff values between progesterone receptor positive and negative or for Ki-67 tumors (all P > 0.05). No differences were found in the dynamic contrast-enhanced MRI characteristics between the two groups. HER-2 expression and kurtosis of the S-index distribution were screened out as independent risk factors for predicting MHR group (p < 0.05, area under the curve (AUC) = 0.811) before NAC. After early NAC (two cycles), only the 10th percentile S index was statistically significant between the two groups (p < 0.05, AUC = 0.714). No significant differences were found in ADC-diff value at any time point of NAC between the two groups (P > 0.1). These findings demonstrate that the S-index value may be used as an early predictor of pathological response to NAC in BC; the value of ADC-diff as an imaging biomarker of NAC needs to be further confirmed by ongoing multicenter prospective trials.

Association Between MRI Radiomics and Intratumoral Tertiary Lymphoid Structures in Intrahepatic Cholangiocarcinoma and Its Prognostic Significance.

BACKGROUND: Tertiary lymphoid structures (TLSs) have prognostic value in intrahepatic cholangiocarcinoma (ICC) patients. Noninvasive tool to preoperatively evaluate TLSs is still lacking. PURPOSE: To explore the association between TLSs status of ICC and preoperative MRI radiomics analysis. STUDY TYPE: Retrospective. SUBJECTS: One hundred and ninety-two patients with ICC, divided into training (T = 105), internal validation groups (V1 = 46), and external validation group (V2 = 41). SEQUENCE: Coronal and axial single-shot fast spin-echo T2-weighted, diffusion-weighted imaging, T1-weighted, and T1WI fat-suppressed spoiled gradient-recall echo LAVA sequence at 3.0 T. ASSESSMENT: The VOIs were drawn manually within the visible borders of the tumors using ITK-SNAP version 3.8.0 software in the axial T2WI, DWI, and portal vein phase sequences. Radiomics features were subjected to least absolute shrinkage and selection operator regression to select the associated features of TLSs and construct the radiomics model. Univariate and multivariate analyses were used to identify the clinical radiological variables associated with TLSs. The performances were evaluated by the area under the receiver operator characteristic curve (AUC). STATISTICAL TESTS: Logistic regression analysis, ROC and AUC, Hosmer-Lemeshow test, Kaplan-Meier method with the log-rank test, calibration curves, and decision curve analysis. P < 0.05 was considered statistically significant. RESULTS: The AUCs of arterial phase diffuse hyperenhancement were 0.59 (95% confidence interval [CI], 0.50-0.67), 0.52 (95% CI, 0.43-0.61), and 0.66 (95% CI, 0.52-0.80) in the T, V1, and V2 cohorts. The AUCs of Rad-score were 0.85 (95% CI, 0.77-0.92), 0.81 (95% CI, 0.67-0.94), and 0.84 (95% CI, 0.71-0.96) in the T, V1, and V2 cohorts, respectively. In cohort T, low-risk group showed significantly better median recurrence-free survival (RFS) than that of the high-risk group, which was also confirmed in cohort V1 and V2. DATA CONCLUSION: A preoperative MRI radiomics signature is associated with the intratumoral TLSs status of ICC patients and correlate significantly with RFS. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Cardiac magnetic resonance radiomics for disease classification.

OBJECTIVES: This study investigated the discriminability of quantitative radiomics features extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and healthy (NOR) patients. METHODS: The data of two hundred and eighty-three patients with HCM (n = 48) or DCM (n = 52) and NOR (n = 123) were extracted from two publicly available datasets. Ten feature selection methods were first performed on twenty-one different sets of radiomics features extracted from the left ventricle, right ventricle, and myocardium segmented from CMR images in the end-diastolic frame, end-systolic frame, and a combination of both; then, nine classical machine learning methods were trained with the selected radiomics features to distinguish HCM, DCM, and NOR. Ninety classification models were constructed based on combinations of the ten feature selection methods and nine classifiers. The classification models were evaluated, and the optimal model was selected. The diagnostic performance of the selected model was also compared to that of state-of-the-art methods. RESULTS: The random forest minimum redundancy maximum relevance model with features based on LeastAxisLength, Maximum2DDiameterSlice, Median, MinorAxisLength, Sphericity, VoxelVolume, Kurtosis, Flatness, and Skewness was the highest performing model, achieving 91.2% classification accuracy. The cross-validated areas under the curve on the test dataset were 0.938, 0.966, and 0.936 for NOR, DCM, and HCM, respectively. Furthermore, compared with those of the state-of-the-art methods, the sensitivity and accuracy of this model were greatly improved. CONCLUSIONS: A predictive model was proposed based on CMR radiomics features for classifying HCM, DCM, and NOR patients. The model had good discriminability. KEY POINTS: • The first-order features and the features extracted from the LOG-filtered images have potential in distinguishing HCM patients from DCM patients. • The features extracted from the RV play little role in distinguishing DCM from HCM. • The VoxelVolume of the myocardium in the ED frame is important in the recognition of DCM.

Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors in nasopharyngeal carcinoma.

OBJECTIVES: To evaluate the correlation between histogram parameters derived from synthetic magnetic resonance imaging (SyMRI) and prognostically relevant factors in nasopharyngeal carcinoma (NPC). METHODS: Fifty-nine consecutive NPC patients were prospectively enrolled. Quantitative parameters (T1, T2, and proton density (PD)) were obtained by outlining the three-dimensional volume of interest (VOI) of all lesions. Then, histogram analysis of these quantitative parameters was performed and the correlations with prognostically relevant factors were assessed. By choosing appropriate cutoff, we divided the sample into two groups. Independent-samples t test/Mann-Whitney U test was used and ROC curve analysis was further processed. RESULTS: Histogram parameters of the T1, T2, and PD maps were positively correlated with the Ki-67 expression levels, and PD_mean was the most representative parameter (AUC: 0.861). The PD map exhibited good performance in differentiating epidermal growth factor receptor (EGFR) expression levels (AUC: 0.706~0.732) and histological type (AUC: 0.650~0.660). T2_minimum was highest correlated with Epstein-Barr virus (EBV) DNA levels (r = - 0.419), and PD_75th percentile exhibited the highest performance in distinguishing positive and negative EBV DNA groups (AUC: 0.721). T1_minimum was statistically correlated with EA-IgA expression (r = - 0.313). Additionally, several histogram parameters were negatively correlated with tumor stage (T stage: r = - 0.259 ~ - 0.301; N stage: r = - 0.348 ~ - 0.456; clinical stage: r = - 0.419). CONCLUSIONS: Histogram parameters of SyMRI could reflect tissue intrinsic characteristics and showed potential value in assessing the Ki-67 and EGFR expression levels, histological type, EBV DNA level, EA-IgA, and tumor stage. KEY POINTS: • SyMRI combined with histogram analysis may help clinicians to assess different prognostic factor statuses in nasopharyngeal carcinoma. • The PD map exhibited good discriminating performance in the Ki-67 and EGFR expression levels. • Histogram parameters of SyMRI were negatively correlated with EBV-related blood biomarkers and TNM stage.

Predicting pathological complete response of neoadjuvant radiotherapy and targeted therapy for soft tissue sarcoma by whole-tumor texture analysis of multisequence MRI imaging.

OBJECTIVES: To construct effective prediction models for neoadjuvant radiotherapy (RT) and targeted therapy based on whole-tumor texture analysis of multisequence MRI for soft tissue sarcoma (STS) patients. METHODS: Thirty patients with STS of the extremities or trunk from a prospective phase II trial were enrolled for this analysis. All patients underwent pre- and post-neoadjuvant RT MRI examinations from which whole-tumor texture features were extracted, including T1-weighted with fat saturation and contrast enhancement (T1FSGd), T2-weighted with fat saturation (T2FS), and diffusion-weighted imaging (DWI) sequences and their corresponding apparent diffusion coefficient (ADC) maps. According to the postoperative pathological results, the patients were divided into pathological complete response (pCR) and non-pCR (N-pCR) groups. pCR was defined as less than 5% of residual tumor cells by postoperative pathology. Delta features were defined as the percentage change in a texture feature from pre- to post-neoadjuvant RT MRI. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. RESULTS: Five of 30 patients (16.7%) achieved pCR. The Delta_Model (AUC 0.92) had a better predictive ability than the Pre_Model (AUC 0.78) and Post_Model (AUC 0.76) and was better than AJCC staging (AUC 0.52) and RECIST 1.1 criteria (AUC 0.52). The Combined_Model (pre, post, and delta features) had the best predictive performance (AUC 0.95). CONCLUSION: Whole-tumor texture analysis of multisequence MRI can well predict pCR status after neoadjuvant RT and targeted therapy in STS patients, with better performance than RECIST 1.1 and AJCC staging. KEY POINTS: • MRI multisequence texture analysis could predict the efficacy of neoadjuvant RT and targeted therapy for STS patients. • Texture features showed incremental value beyond routine clinical factors. • The Combined_Model with features at multiple time points showed the best performance.

The utility of texture analysis based on quantitative synthetic magnetic resonance imaging in nasopharyngeal carcinoma: a preliminary study.

BACKGROUND: Magnetic resonance imaging (MRI) is commonly used for the diagnosis of nasopharyngeal carcinoma (NPC) and occipital clivus (OC) invasion, but a proportion of lesions may be missed using non-enhanced MRI. The purpose of this study is to investigate the diagnostic performance of synthetic magnetic resonance imaging (SyMRI) in differentiating NPC from nasopharyngeal hyperplasia (NPH), as well as evaluating OC invasion. METHODS: Fifty-nine patients with NPC and 48 volunteers who underwent SyMRI examination were prospectively enrolled. Eighteen first-order features were extracted from VOIs (primary tumours, benign mucosa, and OC). Statistical comparisons were conducted between groups using the independent-samples t-test and the Mann-Whitney U test to select significant parameters. Multiple diagnostic models were then constructed using multivariate logistic analysis. The diagnostic performance of the models was calculated by receiver operating characteristics (ROC) curve analysis and compared using the DeLong test. Bootstrap and 5-folds cross-validation were applied to avoid overfitting. RESULTS: The T1, T2 and PD map-derived models had excellent diagnostic performance in the discrimination between NPC and NPH in volunteers, with area under the curves (AUCs) of 0.975, 0.972 and 0.986, respectively. Besides, SyMRI models also showed excellent performance in distinguishing OC invasion from non-invasion (AUC: 0.913-0.997). Notably, the T1 map-derived model showed the highest diagnostic performance with an AUC, sensitivity, specificity, and accuracy of 0.997, 96.9%, 97.9% and 97.5%, respectively. By using 5-folds cross-validation, the bias-corrected AUCs were 0.965-0.984 in discriminating NPC from NPH and 0.889-0.975 in discriminating OC invasion from OC non-invasion. CONCLUSIONS: SyMRI combined with first-order parameters showed excellent performance in differentiating NPC from NPH, as well as discriminating OC invasion from non-invasion.

Evaluation of late gadolinium enhancement cardiac MRI using deep learning reconstruction.

BACKGROUND: Deep learning (DL)-based methods have been used to improve the imaging quality of magnetic resonance imaging (MRI) by denoising. PURPOSE: To assess the effects of DL-based MR reconstruction (DLR) method on late gadolinium enhancement (LGE) image quality. MATERIAL AND METHODS: A total of 85 patients who underwent cardiovascular magnetic resonance (CMR) examination, including LGE imaging using conventional construction and DLR with varying levels of noise reduction (NR) levels, were included. Both magnitude LGE (MLGE) and phase-sensitive LGE (PSLGE) images were reviewed independently by double-blinded observers who used a 5-point Likert scale for multiple measures regarding image quality. Meanwhile, the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness of images were calculated and compared between conventional LGE imaging and DLR LGE imaging. RESULTS: Both MLGE and PSLGE with DLR at 50% and 75% noise reduction levels received significantly higher scores than conventional imaging for overall imaging quality (all P < 0.01). In addition, the SNR, CNR, and edge sharpness of all DLR LGE imaging are higher than conventional imaging (all P < 0.01). The highest subjective score and best image quality is obtained when the DLR noise reduction level is at 75%. CONCLUSION: DLR reduced image noise while improving image contrast and sharpness in the cardiovascular LGE imaging.

Soft tissue sarcoma: IVIM and DKI parameters correlate with Ki-67 labeling index on direct comparison of MRI and histopathological slices.

OBJECTIVES: To explore the correlation of parameters derived from intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) with Ki-67 labeling index (LI) in soft tissue sarcoma (STS). METHODS: Forty-one patients with STS underwent IVIM and DKI imaging at 3.0 MRI. The standard apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), mean kurtosis (MK), and mean diffusivity (MD) were compared between Ki-67 low- and high-expression groups by two independent observers. A novel method was used to ensure the topographic correlation of histologic sections and magnetic resonance imaging slices. Receiver operating characteristic (ROC), intraclass correlation coefficient (ICC), and Spearman's rank correlations were performed for statistical analysis. RESULTS: The high-expression group displayed lower standard ADC, D, and MD values and a higher MK value than the low-expression group. No significant differences were found for D∗ and f values. The areas under the curve for standard ADC, D, MD, and MK when discriminating between low- and high-expression groups were 0.736, 0.745, 0.848, and 0.894, respectively. MK was positively correlated with Ki-67 LI (r = 0.809, p < 0.001). Standard ADC, D, and MD were negatively correlated with Ki-67 LI (r = -0.541, -0.556, -0.702, respectively, p < 0.001). CONCLUSIONS: IVIM and DKI parameters are correlated with Ki-67 LI. MK may be the optimal imaging biomarker for assessing the Ki-67 expression of STS. KEY POINTS: • IVIM and DKI parameters are correlated with the expression of Ki-67 in STS. • The MRI-pathology control method ensured a strong correlation between MRI slices and histologic sections, resulting in a robust radiological-pathological correlation.

Biocompatible Au-Fe3O4 Nanoparticle-based Magnetic Resonance Imaging in the Diagnosis of Liver Tumor.

The study aimed to analyze the value and significance of magnetic resonance imaging (MRI) using biocompatible Au-Fe3O4 nanomaterials in the diagnosis of liver tumors. In this study, 124 patients with liver tumors were selected to perform MRI scanning based on Au-Fe3O4 nanoparticles. The coincidence rate between MRI scanning results and pathological examination results, the detection rate of tumor focus between MRI scanning results and CT detection results were analyzed, to evaluate the diagnostic performance of MRI scanning based on Au-Fe3O4 nanoparticles. The results showed that in the detection of primary tumors, secondary tumors, and focal nodular hyperplasia, the coincidence rate between MRI scanning results and pathological examination results was high, and Kappa values were all greater than 0.8. Compared with the results of CT, the detection rate of tumor lesions smaller than 1 cm by MRI was significantly higher (P<0.05), but there was no significant difference between the two diagnostic methods for tumor lesions larger than 1 cm (P>0.05). Additionally, the accuracy, specificity, and sensitivity of MRI in diagnosing liver tumors were 86.81, 84.29, and 77.27, respectively. Clinically, MRI scanning based on Au-Fe3O4nanoparticles can provide a practical and effective reference for the differential diagnosis of liver tumors.

Whole-lesion apparent diffusion coefficient (ADC) histogram as a quantitative biomarker to preoperatively differentiate stage IA endometrial carcinoma from benign endometrial lesions.

BACKGROUND: To assess the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in differentiating stage IA endometrial carcinoma (EC) from benign endometrial lesions (BELs) and characterizing histopathologic features of stage IA EC preoperatively. METHODS: One hundred and six BEL and 126 stage IA EC patients were retrospectively enrolled. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. The Mann-Whitney U or Student's t-test was used to compare the differences between the two groups. Models based on clinical parameters and histogram features were established using multivariate logistic regression. Receiver operating characteristic (ROC) analysis and calibration curves were used to assess the models. RESULTS: Stage IA EC showed lower ADC10th, ADC90th, ADCmin, ADCmax, ADCmean, ADCmedian, interquartile range, mean absolute deviation, robust mean absolute deviation (rMAD), root mean squared, energy, total energy, entropy, variance, and higher skewness, kurtosis and uniformity than BELs (all p < 0.05). ADCmedian yielded the highest area under the ROC curve (AUC) of 0.928 (95% confidence interval [CI] 0.895-0.960; cut-off value = 1.161 × 10-3 mm2/s) for differentiating stage IA EC from BELs. Moreover, multivariate analysis demonstrated that ADC-score (ADC10th + skewness + rMAD + total energy) was the only significant independent predictor (OR = 2.641, 95% CI 2.045-3.411; p < 0.001) for stage IA EC when considering clinical parameters. This ADC histogram model (ADC-score) achieved an AUC of 0.941 and a bias-corrected AUC of 0.937 after bootstrap resampling. The model performed well for both premenopausal (accuracy = 0.871) and postmenopausal (accuracy = 0.905) patients. Besides, ADCmin and ADC10th were significantly lower in Grade 3 than in Grade 1/2 stage IA EC (p = 0.022 and 0.047). At the same time, no correlation was found between ADC histogram parameters and the expression of Ki-67 in stage IA EC (all p > 0.05). CONCLUSIONS: Whole-lesion ADC histogram analysis could serve as an imaging biomarker for differentiating stage IA EC from BELs and assisting in tumor grading of stage IA EC, thus facilitating personalized clinical management for premenopausal and postmenopausal patients.

Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: a preliminary study.

OBJECTIVES: To evaluate the diagnostic potential of diffusion kurtosis imaging (DKI) functional maps with whole-tumor texture analysis in differentiating cervical cancer (CC) subtype and grade. METHODS: Seventy-six patients with CC were enrolled. First-order texture features of the whole tumor were extracted from DKI and DWI functional maps, including apparent kurtosis coefficient averaged over all directions (MK), kurtosis along the axial direction (Ka), kurtosis along the radial direction (Kr), mean diffusivity (MD), fractional anisotropy (FA), and ADC maps, respectively. The Mann-Whitney U test and ROC curve were used to select the most representative texture features. Models based on each individual and combined functional maps were established using multivariate logistic regression analysis. Conventional parameters-the average values of ADC and DKI parameters derived from the conventional ROI method-were also evaluated. RESULTS: The combined model based on Ka, Kr, MD, and FA maps yielded the best diagnostic performance in discrimination of cervical squamous cell cancer (SCC) and cervical adenocarcinoma (CAC) with the highest AUC (0.932). Among individual functional map derived models, Kr map-derived model showed the best performance when differentiating tumor subtypes (AUC = 0.828). MK_90th percentile was useful for distinguishing high-grade and low-grade in SCC tumors with an AUC of 0.701. The average values of MD, FA, and ADC were significantly different between SCC and CAC, but no conventional parameters were useful for tumor grading. CONCLUSIONS: The whole-tumor texture analysis applied to DKI functional maps can be used for differential diagnosis of cervical cancer subtypes and grading SCC. KEY POINTS: • The whole-tumor texture analysis applied to DKI functional maps allows accurate differential diagnosis of CC subtype and grade. • The combined model derived from multiple functional maps performs significantly better than the single models when differentiating tumor subtypes. • MK_90th percentile was useful for distinguishing poorly and well-/moderately differentiated SCC tumors with an AUC of 0.701.

Quantification of Hepatic Fat Fraction in Patients With Nonalcoholic Fatty Liver Disease: Comparison of Multimaterial Decomposition Algorithm and Fat (Water)-Based Material Decomposition Algorithm Using Single-Source Dual-Energy Computed Tomography.

METHODS: Hepatic fat fractions were quantified by noncontrast (HFFnon-CE) and contrast-enhanced single-source dual-energy computed tomography in arterial phase (HFFAP), portal venous phase (HFFPVP) and equilibrium phase (HFFEP) using MMD in 19 nonalcoholic fatty liver disease patients. The fat concentration was measured on fat (water)-based images. As the standard of reference, magnetic resonance iterative decomposition of water and fat with echo asymmetry and least-squares estimation-iron quantification images were reconstructed to obtain HFF (HFFIDEAL-IQ). RESULTS: There was a strong correlation between HFFnon-CE, HFFAP, HFFPVP, HFFEP, fat concentration and HFFIDEAL-IQ (r = 0.943, 0.923, 0.942, 0.952, and 0.726) with HFFs having better correlation with HFFIDEAL-IQ. Hepatic fat fractions did not significantly differ across scanning phases. The HFFs of 3-phase contrast-enhanced computed tomography had a good consistency with HFFnon-CE. CONCLUSIONS: Hepatic fat fraction using MMD has excellent correlation with that of magnetic resonance imaging, is independent of the computed tomography scanning phases, and may be used as a routine technique for quantitative assessment of HFF.

Relationship between the sectional area of the rectus capitis posterior minor and the to be named ligament from 3D MR imaging.

BACKGROUND: To evaluate the maximal sectional area (SA) of the rectus capitis posterior minor (RCPmi) muscle and its potential correlation with to be named ligament (TBNL) in the suboccipital area using 3D MR imaging. METHODS: A total of 365 subjects underwent sagittal 3D T2WI MR imaging of the RCPmi and TBNL. Among them, 45 subjects were excluded due to a particular clinical history or poor image quality. Finally, 320 subjects met the inclusion criteria, including 138 men and 182 women. The 624 RCPmi muscles were classified into positive and negative groups according to their attachment to the TBNL. Two experienced radiologists manually measured the maximum SA of the RCPmi muscle on the parasagittal image with a 30° deviation from the median sagittal plane. The correlations between the SA and the subject's age, height, BMI, gender, handedness, and age-related disc degeneration were tested by Spearman analysis. The SA differences between different groups were compared using independent samples t-test. RESULTS: A total of 123 RCPmi-TBNL attachments were identified in the positive group, while 501 RCPmi muscles were identified in the negative group. The SA of the 624 RCPmi muscles was 62.71 ± 28.72 mm2 and was poorly correlated with the subject's age, BMI, or handedness, with no correlation with age-related disc degeneration. A fair correlation was found between the SA and the body height in the whole group, and poor correlation in each male/female group. The SA of the RCPmi muscle in males was significantly bigger than that in women ([75.54 ± 29.17] vs. [52.74 ± 24.07] mm2). The SA of RCPmi muscle in the positive group was significantly smaller than that in the negative group ([55.95 ± 26.76] mm2 vs. [64.37 ± 28.97] mm2). CONCLUSIONS: Our results revealed a significantly smaller SA of the RCPmi in subjects with RCPmi-TBNL attachment. Besides, a larger SA of the RCPmi was correlated with the male gender. These findings suggest that the SA of the RCPmi ought to be interpreted with care for each patient since there could be considerable variations.

Intravoxel Incoherent Motion Combined With Dynamic Contrast-Enhanced Perfusion MRI of Early Cervical Carcinoma: Correlations Between Multimodal Parameters and HIF-1α Expression.

BACKGROUND: The identification of hypoxia inducible factor (HIF-1α) expression is helpful for the quantitative assessment of tumor hypoxia. The application of multimodal imaging techniques may play a part in the assessment of HIF-1α expression of cervical carcinoma. PURPOSE: To investigate the correlations between multiple imaging parameters and HIF-1α expression of early cervical carcinoma and to determine whether tumor hypoxia can be predicted using multisequence imaging parameters. STUDY TYPE: Prospective observational. POPULATION: One hundred patients with early cervical carcinoma. FIELD STRENGTH/SEQUENCES: 3.0 T MRI including intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) perfusion MRI sequences. ASSESSMENT: DCE-MRI and IVIM DWI were performed for all patients. The imaging parameters included volume transfer constant (Ktrans ), rate constant (Kep ), extravascular extracellular volume fraction (Ve ), D, D*, and f. STATISTICAL TESTS: The comparisons of imaging parameters between two independent groups were performed using the Mann-Whitney U-test. Multiple linear regression analysis was performed to determine the correlation between multiple imaging parameters and HIF-1α expression. The diagnostic ability of DCE-MRI, IVIM DWI, and the combination of two techniques for discriminating high-expression and low-expression groups were analyzed. RESULTS: The high-expression group had a lower Ktrans or Kep value than the low-expression group (P = 0.03; 0.02), while the high-expression group had a higher Ve value than the low-expression group (P = 0.03). The high-expression group had a higher D or f value than the low-expression group (P = 0.02; 0.02). Ktrans , Kep , D, Ve , and f values were independently correlated with HIF-1α expression. The sensitivity or accuracy of a combined method was higher than that of DCE-MRI or IVIM DWI individually (P = 0.03, 0.02; 0.04, 0.03). DATA CONCLUSION: The combination of DCE-MRI and IVIM DWI can improve the diagnostic ability of discriminating different HIF-1α expression levels for early cervical tumors. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:918-929.

Blood Oxygen Level-Dependent Imaging and Intravoxel Incoherent Motion MRI of Early Contrast-Induced Acute Kidney Injury in a Rabbit Model.

BACKGROUND: To evaluate the application of blood oxygenation level-dependent (BOLD)imaging and intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) on assessing early contrast-induced acute kidney injury (CIAKI). MATERIALS: Sixty rabbits were randomly chosen to undergo iohexol (1.0, 2.5, and 5.0 [gI/kg], respectively; n = 15 for each group) or saline injection (n = 15). In each group, 6 rabbits underwent MRI at 24 h before injection and after injection of iohexol or saline (1 h and 1, 2, 3, and 4 days); meanwhile, out of the remaining 9 rabbits, 3 were chosen for MRI acquisition, and then they were killed at specific time points (1 h, 1 day, and 3 days, respectively). RESULTS: The strong attenuation of pure molecular diffusion (D), apparent diffusion coefficient (ADC), and perfusion fraction (f) was observed at 1 day, while pseudodiffusion coefficient (D*) showed a significant decrease at 1 h after iohexol injection. A distinct elevation of apparent transverse relaxation rate (R2*) reached the maximum levels on day 1, which was consistent with the expression of hypoxia-inducible factor-1α and vascular endothelial growth factor. ADC, D, and R2* correlated well with histopathological parameters and biochemical parameters. CONCLUSION: BOLD combined with IVIM is effective to monitor renal pathophysiology associated with CIAKI.

Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on precontrast magnetic resonance images.

BACKGROUND: To evaluate the feasibility of using radiomics with precontrast magnetic resonance imaging for classifying hepatocellular carcinoma (HCC) and hepatic haemangioma (HH). METHODS: This study enrolled 369 consecutive patients with 446 lesions (a total of 222 HCCs and 224 HHs). A training set was constituted by randomly selecting 80% of the samples and the remaining samples were used to test. On magnetic resonance (MR) images of HCC and HH obtained with in-phase, out-phase, T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) sequences, we outlined the target lesions and extracted 1029 radiomics features, which were classified as first-, second-, higher-order statistics and shape features. Then, the variance threshold, select k best, and least absolute shrinkage and selection operator algorithms were explored for dimensionality reduction of the features. We used four classifiers (decision tree, random forest, K nearest neighbours, and logistic regression) to identify HCC and HH on the basis of radiomics features. Two abdominal radiologists also performed the conventional qualitative analysis for classification of HCC and HH. Diagnostic performances of radiomics and radiologists were evaluated by receiver operating characteristic (ROC) analysis. RESULTS: Valuable radiomics features for building a radiomics signature were extracted from in-phase (n = 22), out-phase (n = 24), T2WI (n = 34) and DWI (n = 24) sequences. In comparison, the logistic regression classifier showed better predictive ability by combining four sequences. In the training set, the area under the ROC curve (AUC) was 0.86 (sensitivity: 0.76; specificity: 0.78), and in the testing set, the AUC was 0.89 (sensitivity: 0.822; specificity: 0.714). The diagnostic performance for the optimal radiomics-based combined model was significantly higher than that for the less experienced radiologist (2-years experience) (AUC = 0.702, p < 0.05), and had no statistic difference with the experienced radiologist (10-years experience) (AUC = 0.908, p>0.05). CONCLUSIONS: We developed and validated a radiomics signature as an adjunct tool to distinguish HCC and HH by combining in-phase, out-phase, T2W, and DW MR images, which outperformed the less experienced radiologist (2-years experience), and was nearly equal to the experienced radiologist (10-years experience).

Intravoxel incoherent motion MR imaging of early cervical carcinoma: correlation between imaging parameters and tumor-stroma ratio.

OBJECTIVES: To investigate if intravoxel incoherent motion (IVIM) MR imaging can predict the tumour-stroma ratio (TSR) in patients with early cervical carcinoma. METHODS: Fifty-four patients with early cervical carcinoma were prospectively enrolled into this study. All patients underwent IVIM imaging and parameters including D, D* and f value were measured. The tumours were classified into stroma-rich and stroma-poor group according to TSR, and comparisons of IVIM parameters between two groups were performed. The relationships between IVIM parameters and TSR were analysed by using a multivariate multi-regression analysis. RESULTS: D and f values were significantly lower in stroma-poor tumours than in stroma-rich tumours (p=0.02, 0.04), while the difference in D* value between two groups didn't achieve statistical significance (p=0.09). The areas under ROC curves of D and f values in discriminating stroma-rich and stroma-poor tumours were 0.835 (95%CI=0.616~0.905) and 0.686 (95%CI=0.575~0.798). In multiple linear regression analysis, D value, pathologic type, histologic grade, tumour size and f value were independently correlated with TSR of cervical carcinoma. CONCLUSIONS: D and f values are independently correlated with TSR of cervical carcinoma and have the potential for quantitative measurement of TSR. KEY POINTS: • TSR is a recognized independent prognostic factor in many solid tumours. • D and f values measured by IVIM MRI are independently correlated with TSR while D* is not. • IVIM offers the potential to predict TSR.

Comparison between types I and II epithelial ovarian cancer using histogram analysis of monoexponential, biexponential, and stretched-exponential diffusion models.

PURPOSE: To evaluate the utility of histogram analysis of monoexponential, biexponential, and stretched-exponential models to a dualistic model of epithelial ovarian cancer (EOC). MATERIALS AND METHODS: Fifty-two patients with histopathologically proven EOC underwent preoperative magnetic resonance imaging (MRI) (including diffusion-weighted imaging [DWI] with 11 b-values) using a 3.0T system and were divided into two groups: types I and II. Apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α) histograms were obtained based on solid components of the entire tumor. The following metrics of each histogram were compared between two types: 1) mean; 2) median; 3) 10th percentile and 90th percentile. Conventional MRI morphological features were also recorded. RESULTS: Significant morphological features for predicting EOC type were maximum diameter (P = 0.007), texture of lesion (P = 0.001), and peritoneal implants (P = 0.001). For ADC, D, f, DDC, and α, all metrics were significantly lower in type II than type I (P < 0.05). Mean, median, 10th, and 90th percentile of D* were not significantly different (P = 0.336, 0.154, 0.779, and 0.203, respectively). Most histogram metrics of ADC, D, and DDC had significantly higher area under the receiver operating characteristic curve values than those of f and α (P < 0.05) CONCLUSION: It is feasible to grade EOC by morphological features and three models with histogram analysis. ADC, D, and DDC have better performance than f and α; f and α may provide additional information. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1797-1809.

Molecular subtypes of breast cancer identified by dynamically enhanced MRI radiomics: the delayed phase cannot be ignored.

OBJECTIVES: To compare the diagnostic performance of intratumoral and peritumoral features from different contrast phases of breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building radiomics models for differentiating molecular subtypes of breast cancer. METHODS: This retrospective study included 377 patients with pathologically confirmed breast cancer. Patients were divided into training set (n = 202), validation set (n = 87) and test set (n = 88). The intratumoral volume of interest (VOI) and peritumoral VOI were delineated on primary breast cancers at three different DCE-MRI contrast phases: early, peak, and delayed. Radiomics features were extracted from each phase. After feature standardization, the training set was filtered by variance analysis, correlation analysis, and least absolute shrinkage and selection (LASSO). Using the extracted features, a logistic regression model based on each tumor subtype (Luminal A, Luminal B, HER2-enriched, triple-negative) was established. Ten models based on intratumoral or/plus peritumoral features from three different phases were developed for each differentiation. RESULTS: Radiomics features extracted from delayed phase DCE-MRI demonstrated dominant diagnostic performance over features from other phases. However, the differences were not statistically significant. In the full fusion model for differentiating different molecular subtypes, the most frequently screened features were those from the delayed phase. According to the Shapley additive explanation (SHAP) method, the most important features were also identified from the delayed phase. CONCLUSIONS: The intratumoral and peritumoral radiomics features from the delayed phase of DCE-MRI can provide additional information for preoperative molecular typing. The delayed phase of DCE-MRI cannot be ignored. CRITICAL RELEVANCE STATEMENT: Radiomics features extracted and radiomics models constructed from the delayed phase of DCE-MRI played a crucial role in molecular subtype classification, although no significant difference was observed in the test cohort. KEY POINTS: The molecular subtype of breast cancer provides a basis for setting treatment strategy and prognosis. The delayed-phase radiomics model outperformed that of early-/peak-phases, but no differently than other phases or combinations. Both intra- and peritumoral radiomics features offer valuable insights for molecular typing.

Atrophy patterns in hippocampus and amygdala subregions of depressed patients with Parkinson's disease.

We aimed to explore the subregional atrophy patterns of the amygdala and hippocampus in Parkinson's disease (PD) with depression and their correlation with the severity of the depressive symptom. MRI scans were obtained for 34 depressed PD patients (DPD), 22 nondepressed PD patients (NDPD), and 28 healthy controls (HC). Amygdala and hippocampal subregions were automatically segmented, and the intergroup volume difference was compared. The relationships between the volumes of the subregions and depression severity were investigated. Logistic analysis and Receiver operator characteristic curve were used to find independent predictors of DPD. Compared with the HC group, atrophy of the bilateral lateral nucleus, left accessory basal nucleus, right cortical nucleus, right central nucleus, and right medial nucleus subregions of the amygdala were visible in the DPD group, while the right lateral nucleus subregion of the amygdala was smaller in the DPD group than in the NDPD group. The DPD group showed significant atrophy in the left molecular layer, left GC-DG, left CA3, and left CA4 subregions compared with the HC group for hippocampal subregion volumes. Also, the right lateral nuclei volume and disease duration were independent predictors of DPD. To sum up, DPD patients showed atrophy in multiple amygdala subregions and left asymmetric hippocampal subregions. The decreased amygdala and hippocampal subregion volumes were correlated with the severity of depressive symptoms. The volume of right lateral nuclei and disease duration could be used as a biomarker to detect DPD.