Dynamic Contrast-enhanced Imaging Research Articles

Role of multiparametric magnetic resonance imaging in the diagnosis and staging of urinary bladder cancer.

ObjectivesTo assess the role of multiparametric magnetic resonance imaging (mp-MRI) in the diagnosis and staging of urinary bladder cancer (BC).Materials and methodsFifty patients diagnosed with bladder masses underwent mp-MRI study. The results of 3 image sets were analyzed and compared with the histopathological results as a reference standard: T2-weighted image (T2WI) plus dynamic contrast-enhanced (DCE), T2WI plus diffusion-weighted images (DWI), and mp-MRI, including T2WI plus DWI and DCE. The diagnostic accuracy of mp-MRI was evaluated using receiver operating characteristic curve analysis.ResultsThe accuracy of T2WI plus DCE for detecting muscle invasion of BC was 79.5% with a fair agreement with histopathological examination (κ = 0.59); this percentage increased up to 88.6% using T2WI plus DWI, with good agreement with histopathological examination (κ = 0.74), whereas mp-MRI had the highest overall accuracy (95.4%) and excellent agreement with histopathological data (κ = 0.83). Multiparametric MRI can differentiate between low- and high-grade bladder tumors with a high sensitivity and specificity of 93.3% and 98.3%, respectively.ConclusionsMultiparametric MRI is an acceptable method for the preoperative detection and accurate staging of BC, with reasonable accuracy in differentiating between low- and high-grade BC.

Contribution of Dynamic Contrast-enhanced and Diffusion MRI to PI-RADS for Detecting Clinically Significant Prostate Cancer.

Background Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 requires multiparametric MRI of the prostate, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging sequences; however, the contribution of DCE imaging remains unclear. Purpose To assess whether DCE imaging in addition to apparent diffusion coefficient (ADC) and normalized T2 values improves PI-RADS version 2.0 for prediction of clinically significant prostate cancer (csPCa). Materials and Methods In this retrospective study, clinically reported PI-RADS lesions in consecutive men who underwent 3-T multiparametric MRI (T2-weighted, DWI, and DCE MRI) from May 2015 to September 2016 were analyzed quantitatively and compared with systematic and targeted MRI-transrectal US fusion biopsy. The normalized T2 signal (nT2), ADC measurement, mean early-phase DCE signal (mDCE), and heuristic DCE parameters were calculated. Logistic regression analysis indicated the most predictive DCE parameters for csPCa (Gleason grade group ≥2). Receiver operating characteristic parameter models were compared using the Obuchowski test. Recursive partitioning analysis determined ADC and mDCE value ranges for combined use with PI-RADS. Results Overall, 260 men (median age, 64 years [IQR, 58-69 years]) with 432 lesions (csPCa [n = 152] and no csPCa [n = 280]) were included. The mDCE parameter was predictive of csPCa when accounting for the ADC and nT2 parameter in the peripheral zone (odds ratio [OR], 1.76; 95% CI: 1.30, 2.44; P = .001) but not the transition zone (OR, 1.17; 95% CI: 0.81, 1.69; P = .41). Recursive partitioning analysis selected an ADC cutoff of 0.897 × 10-3 mm2/sec (P = .04) as a classifier for peripheral zone lesions with a PI-RADS score assessed on the ADC map (hereafter, ADC PI-RADS) of 3. The mDCE parameter did not differentiate ADC PI-RADS 3 lesions (P = .11), but classified lesions with ADC PI-RADS scores greater than 3 with low ADC values (less than 0.903 × 10-3 mm2/sec, P < .001) into groups with csPCa rates of 70% and 97% (P = .008). A lesion size cutoff of 1.5 cm and qualitative DCE parameters were not defined as classifiers according to recursive partitioning (P > .05). Conclusion Quantitative or qualitative dynamic contrast-enhanced MRI was not relevant for Prostate Imaging Reporting and Data System (PI-RADS) 3 lesion risk stratification, while quantitative apparent diffusion coefficient (ADC) values were helpful in upgrading PI-RADS 3 and PI-RADS 4 lesions. Quantitative ADC measurement may be more important for risk stratification than current methods in future versions of PI-RADS. © RSNA, 2022 Online supplemental material is available for this article See also the editorial by Goh in this issue.

Diagnostic Performance of Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted Imaging in Differentiating Recurrence From Radiation Injury in Postoperative Glioma: A Meta-analysis.

It is important to differentiate between radiation injury (RI) and tumor recurrence (TR) in patients with glioma after surgery and radiotherapy. Our objective was to evaluate the use of dynamic susceptibility contrast-enhanced perfusion-weighted imaging to distinguish between TR and RI in patients with glioma. Relevant studies published until October 2021 were identified in the PubMed, Embase, and Cochrane Library databases. Stata v12.0 and RevMan 5.3 were used for meta-analysis. In total, the meta-analysis incorporated 13 retrospective studies that included 513 patients with 522 lesions. Among the 522 lesions, 329 lesions were TRs and 193 lesions were RIs. The pooled relative cerebral blood volume value was significantly greater in the TR group ( P < 0.00001) with significant heterogeneity ( I2 = 88%). The pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 83% (95 confidence interval [CI], 77%-88%), 85% (95 CI, 77%-91%), 5.60 (95 CI, 3.61-8.70), and 0.20 (95% CI, 0.14-0.27), respectively. The heterogeneity of sensitivity ( I2 = 33.18%), specificity ( I2 = 24.01%), PLR ( I2 = 0.00%), and NLR ( I2 = 6.68%) is not significant. The area under the receiver operating characteristic curve was 0.91 (95% CI, 0.88-0.93). The 3.0 T magnetic resonance imaging, high-grade glioma, and Europe/America patient subgroups showed PLR greater than 5 and NLR less than 0.2. There was no significant indication of publication bias in the analysis ( P = 0.496). It is concluded that dynamic susceptibility contrast-enhanced perfusion-weighted imaging is effective for the diagnostic differentiation between TR and RI in patients with glioma.

An Overview of the Role of Multiparametric MRI in the Investigation of Testicular Tumors.

Simple SummaryAlthough conventional ultrasonography remains the primary imaging modality for the assessment of testicular tumors, multiparametric MRI of the scrotum, which combines morphologic and functional data, serves as a powerful adjunct. Based on the recommendations issued by the Scrotal and Penile Imaging Working Group of the European Society of Urogenital Radiology, scrotal MRI is strongly recommended after equivocal US findings. In cases of testicular masses, the main clinical indications are as follows: lesion characterization when sonographic findings are non-diagnostic, discrimination between germ-cell and non-germ-cell testicular tumors, local staging of testicular tumors in patients who are candidates for testis-sparing surgery, and preoperative histological characterization of testicular germ-cell tumors in selected cases. This article aims to provide an overview of the role of multiparametric MRI in the investigation of testicular tumors.Conventional ultrasonography represents the mainstay of testis imaging. In cases in which ultrasonography is inconclusive, scrotal MRI using a multiparametric protocol may be used as a useful problem-solving tool. MRI of the scrotum is primarily recommended for differentiating between benign and malignant testicular masses when sonographic findings are ambiguous. This technique is also accurate in the preoperative local staging of testicular tumors and, therefore, is recommended in patients scheduled for testis-sparing surgery. In addition, MRI may provide valuable information regarding the histological characterization of testicular germ-cell tumors, in selected cases. Scrotal MRI may also help in the differentiation between testicular germ-cell neoplasms and non-germ-cell neoplasms. Axial T1-weighted imaging, axial and coronal T2-weighted imaging, axial diffusion-weighted imaging, and coronal subtracted dynamic contrast-enhanced imaging are the minimum requirements for scrotal MRI. A variety of MRI techniques—including diffusion tensor imaging, magnetization transfer imaging, proton MR spectroscopy, volumetric apparent diffusion coefficient histogram analysis, and MRI-based radiomics—are being investigated for testicular mass characterization, providing valuable supplementary diagnostic information. In the present review, we aim to discuss clinical indications for scrotal MRI in cases of testicular tumors, along with MRI findings of common testicular malignancies.

Sterically Shielded Hydrophilic Analogs of Indocyanine Green.

A modular synthetic process enables two or four shielding arms to be appended strategically over the fluorochromes of near-infrared cyanine heptamethine dyes to create hydrophilic analogs of clinically approved indocyanine green. A key synthetic step is the facile substitution of a heptamethine 4'-Cl atom by a phenol bearing two triethylene glycol chains. The lead compound is a heptamethine dye with four shielding arms, and a series of comparative spectroscopy studies showed that the shielding arms (a) increased dye photostability and chemical stability and (b) inhibited dye self-aggregation and association with albumin protein. In mice, the dye cleared from the blood primarily through the renal pathway rather than the biliary pathway for ICG. This change in biodistribution reflects the much smaller hydrodynamic diameter of the shielded hydrophilic ICG analog compared to the 67 kDa size of the ICG/albumin complex. An attractive feature of versatile synthetic chemistry is the capability to systematically alter the dye's hydrodynamic diameter. The sterically shielded hydrophilic ICG dye platform is well-suited for immediate incorporation into dynamic contrast-enhanced (DCE) spectroscopy or imaging protocols using the same cameras and detectors that have been optimized for ICG.

Evaluation of radiation exposure for patients undergoing computed tomography perfusion procedure for acute ischemic stroke

Computed tomography perfusion (CTP) is contrast-enhanced dynamic imaging of the brain utilized to assess the blood flow volume and transmit time to the brain's parenchyma. Stroke is the world's second greatest cause of mortality, accounting for 11% of all deaths, and a major cause of disability. Previous studies reported a high radiation dose per procedure with an eye lens equivalent dose (mGy) ranging from 81.0 mGy to 348.0 mGy. This study's objective is to evaluate patients exposure during CTP procedures. Materials and methods. Three hundred twenty patients with ischemic stroke underwent CTP were examined at King Fahad Medical City (KFMC). The imaging protocol consists of plain CT, CT angiography (CTA), CTP, Angiography. The volume CT Dose Index (CTDIvol, mGy) and dose length product (DLP, mGy.cm) were registered from the PACS system. The effective dose calculation (E, mSv) was extrapolated using computer software. For the CTP procedure, the mean and range of DLP (mGy.cm) for the complete procedure, CT brain, CTA, CTA digital subtraction angiography (DSA), and CTP were 1045 (105–3072), 843 (277–1530), 470.5 (210–1356), 238.8 (105–439), 2712(2012–3072) examinations, respectively. The mean and range of the effective dose (mSv) and CTDIvol (mGy) were 13.2 (10.6–17.1), 80.5 (6.11–256), in that order. The patients' doses showed wide variation due to the selection of multiple phases of acquisition and exposure factors. The dose is higher than most previous studies. Radiation dose optimization is recommended by establishing diagnostic reference level (DRL), proper CT machine setting, and increasing operators' awareness regarding radiation risks.

Predictive Value of Preoperative Dynamic Contrast-Enhanced MRI Imaging Features in Breast Cancer Patients with Postoperative Recurrence Time.

Although the implementation of surgery has reduced the mortality of breast cancer, postoperative recurrence is still an important problem bothering patients. DCE-GMRI can not only clearly display the morphological characteristics of breast lesions but also dynamically observe the blood perfusion of the lesions. On account of this, we explored the predictive value of preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) imaging features in breast cancer patients on postoperative recurrence time of breast cancer. The results showed that DCE-MRI images can clearly show the hemodynamic characteristics and morphological characteristics of tumor lesions, and have important value in predicting the recurrence time of breast cancer after surgery. The prognosis of early recurrence of breast cancer is worse. DCE-MRI can predict the time of postoperative recurrence and provide important clinical references.

Comparing Radiologist Performance in Diagnosing Clinically Significant Prostate Cancer with Multiparametric versus Hybrid Multidimensional MRI.

Background Variability of acquisition and interpretation of prostate multiparametric MRI (mpMRI) persists despite implementation of the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 due to the range of reader experience and subjectivity of lesion characterization. A quantitative method, hybrid multidimensional MRI (HM-MRI), may introduce objectivity. Purpose To compare performance, interobserver agreement, and interpretation time of radiologists using mpMRI versus HM-MRI to diagnose clinically significant prostate cancer. Materials and Methods In this retrospective analysis, men with prostatectomy or MRI-fused transrectal US biopsy-confirmed prostate cancer underwent mpMRI (triplanar T2-weighted, diffusion-weighted, and dynamic contrast-enhanced imaging) and HM-MRI (with multiple echo times and b value combinations) from August 2012 to February 2020. Four readers with 1-20 years of experience interpreted mpMRI and HM-MRI examinations independently, with a 4-week washout period between interpretations. PI-RADS score, lesion location, and interpretation time were recorded. mpMRI and HM-MRI interpretation time, interobserver agreement (Cronbach alpha), and performance of area under the receiver operating characteristic curve (AUC) analysis were compared for each radiologist with use of bootstrap analysis. Results Sixty-one men (mean age, 61 years ± 8 [SD]) were evaluated. Per-patient AUC was higher for HM-MRI for reader 4 compared with mpMRI (AUCs for readers 1-4: 0.61, 0.71, 0.59, and 0.64 vs 0.66, 0.60, 0.50, and 0.46; P = .57, .20, .32, and .04, respectively). Per-patient specificity was higher for HM-MRI for readers 2-4 compared with mpMRI (specificity for readers 1-4: 48%, 78%, 48%, and 46% vs 37%, 26%, 0%, and 7%; P = .34, P < .001, P < .001, and P < .001, respectively). Diagnostic performance improved for the reader least experienced with HM-MRI, reader 4 (AUC, 0.64 vs 0.46; P = .04). HM-MRI interobserver agreement (Cronbach alpha = 0.88 [95% CI: 0.82, 0.92]) was higher than that of mpMRI (Cronbach alpha = 0.26 [95% CI: 0.10, 0.52]; α > .60 indicates reliability; P = .03). HM-MRI mean interpretation time (73 seconds ± 43 [SD]) was shorter than that of mpMRI (254 seconds ± 133; P = .03). Conclusion Radiologists had similar or improved diagnostic performance, higher interobserver agreement, and lower interpretation time for clinically significant prostate cancer with hybrid multidimensional MRI than multiparametric MRI. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Turkbey in this issue.

Combining and analyzing novel multi-parametric magnetic resonance imaging metrics for predicting Gleason score.

Radiologists currently subjectively examine multi-parametric magnetic resonance imaging (MP-MRI) to determine prostate tumor aggressiveness using the Prostate Imaging Reporting and Data System scoring system (PI-RADS). Recent studies showed that modified signal to clutter ratio (SCR), tumor volume, and eccentricity (elongation or roundness) of prostate tumors correlated with Gleason score (GS). No previous studies have combined the prostate tumor's shape, SCR, tumor volume, in order to predict potential tumor aggressiveness and GS. MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were obtained, resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm [adaptive cosine estimator (ACE)]. Pixel-based blobbing, and labeling were applied to the threshold ACE images. Eccentricity calculation used moments of inertia from the blobs. Tumor volume was computed by counting pixels within multi parametric MRI blobs and tumor outlines based on pathologist assessment of whole mount histology. Pathology assessment of GS was performed on whole mount prostatectomy. The covariance matrix and mean of normal tissue background was computed from normal prostate. Using signatures and normal tissue statistics, the z-score, noise corrected SCR [principal component (PC), modified regularization] from each patient was computed. Eccentricity, tumor volume, and SCR were fitted to GS. Analysis of variance assesses the relationship among the variables. A multivariate analysis generated correlation coefficient (0.60 to 0.784) and P value (0.00741 to <0.0001) from fitting two sets of independent variates, namely, tumor eccentricity (the eccentricity for the largest blob, weighted average for the eccentricity) and SCR (removing 3 PCs, removing 4 PCs, modified regularization, and z-score) to GS. The eccentricity t-statistic exceeded the SCR t-statistic. The three-variable fit to GS using tumor volume (histology, MRI) yielded correlation coefficients ranging from 0.724 to 0.819 (P value <<0.05). Tumor volumes generated from histology yielded higher correlation coefficients than MRI volumes. Adding volume to eccentricity and SCR adds little improvement for fitting GS due to higher correlation coefficients among independent variables and little additional, independent information. Combining prostate tumors eccentricity with SCR relatively highly correlates with GS.

Development of the prediction model based on clinical-imaging omics: molecular typing and sentinel lymph node metastasis of breast cancer.

BackgroundImageology uses high-throughput and automatic computing methods to transform medical image data into quantitative data with feature space, and then makes accurate quantitative analysis, extracts features and builds models, which can intuitively observe the overall features of lesions and the surrounding tissues, and provide rich invisible information. At present, the research on the imaging features of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) to predict the molecular typing value has achieved results, but the imaging model based on DWI and DCE-magnetic resonance imaging (MRI) is not enough to predict the molecular subtypes, and the prediction value of the prediction model based on the three-dimensional volume of interest of the lesion to the four molecular subtypes of breast cancer has not been fully studied.MethodsThe clinical data of 202 breast cancer patients at our hospital from October 2020 to November 2021 were collected. All patients were examined with multimodal MRI before surgery. Base on immunohistochemical recombinant Ki-67 protein (Ki-67), estrogen receptor (ER), human epidermal growth factor receptor-2 (HER-2) and progesterone receptor (PR) results, the tumors were divided into four types According to the results of the sentinel lymph node (SLN) biopsies, the patients were divided into SLN (+) and SLN (−) groups. 3-dimensional (3D) Slicer software was used to outline the region of interest (ROI), and AMni-Kinetics software was used for feature extraction. The imaging characteristics were screened using least absolute shrinkage and selection operator (LASSO)-Logistic regression model using R statistical software, and the receiver operating characteristic (ROC) curve was drawn using “pROC” software package to evaluate the prediction efficiency of the model.ResultsThe most efficacious model at predicting SLN (+) in breast cancer patients with different molecular subtypes and SLN metastasis was the model based on the imageological characteristics of fat inhibition, and T2-weighted imaging (T2WI), T1-weighted imaging + C (T1WI-C), and DWI combined sequences at the tumor + 2 mm periphery. AUC (sensitivity, specificity) of the validation group were 0.831 (0.856, 0.891), 0.832 (0.660, 0.877), 0.801 (0.772, 0.765), 0.904 (0.769, 0.873), and 0.819 (0.810, 0.500) respectively when the tumor was 2 mm around the tumor.ConclusionsThe imaging features extracted from multi-parameter DWI, T1WI+C, and T2WI in breast cancer have certain value at predicting different molecular types and SLNs of breast cancer.

Usefulness of subtraction images for accurate diagnosis of pituitary microadenomas in dynamic contrast-enhanced magnetic resonance imaging.

Dynamic contrast-enhanced magnetic resonance imaging (MRI) is the modality of choice for the diagnosis of pituitary microadenomas; however, it may be associated with a relatively high false-negative rate, especially in small lesions. To evaluate the usefulness of subtraction images for enhancing the visual detection of pituitary microadenomas. In total, 50 patients with clinically established diagnosis hyperprolactinemia, acromegaly, and Cushing's disease were enrolled. Ten patients referred for brain MRI for reasons other than pituitary abnormality were selected as control group. Routine dynamic MRI of the pituitary gland and obtained subtraction MRI scans were scrutinized separately on different sessions by an experienced radiologist blinded to the study design and patient's data. The investigator's opinion on the presence or absence of a lesion and lesion size were collected. In patients with pituitary microadenoma, dynamic MRI images were reported positive in 42 (84%) patients and negative in 8 (16%). Subtraction images were described as positive in all patients (100%)-including all patients with negative dynamic MRI-and the difference was statistically significant (P=0.016). Undetected lesions on dynamic MRI had a mean size of 2.84 ± 1.79 mm (median= 2.20 mm, interquartile range=1.62-4.62 mm) and a significant inverse correlation was noted between lesion size and negative report of dynamic MRI (P=0.018). Brain MRI scans in the control group were reported negative for pituitary microadenoma in both dynamic contrast-enhanced and subtraction images. Subtraction images can successfully identify all lesions detectable with conventional dynamic MRI as well as improving visualization of lesions undetected on dynamic MRI, especially in small lesions.

Image Features of Dynamic Enhanced Computed Tomography Scanning Combined with Digestive Endoscopy in the Treatment of Gastroesophageal Varices and Nursing of Esophagogastric Gastric Varices Bleeding.

The objective of this study was to investigate the application of dynamic contrast-enhanced CT images in the nursing of patients with gastroesophageal varices (GOV) treated by digestive endoscopy and its role in relieving bleeding symptoms. A total of 60 patients with liver cirrhosis and GOV were selected as the research objects. According to whether CT was used to evaluate the position of tissue adhesion embolism, the patients were divided into the control group (24 cases) and the observation group (36 cases). The treatment effect and bleeding situation of patients in the two groups were analyzed and compared. The results showed that the main portal vein pressure (17.24 ± 1.02 cmH2O), liver function recovery effect (2.84 ± 0.45 points), and total effective rate (100%) in observation group were better than those in control group (9.70 ± 1.22 cmH2O, 0.95 ± 0.72 points, and 79.17%, respectively) (P < 0.05). In addition, the bleeding rate in observation group (0%) was significantly lower than that in control group (16.67%) (P < 0.05). In conclusion, dynamically enhanced CT scan images combined with digestive endoscopy can help improve the therapeutic effect of GOV and reduce postoperative bleeding, which was worthy of clinical application and promotion.

Prediction for Distant Metastasis of Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Images under Deep Learning.

This research aimed to explore the effect of using magnetic resonance imaging (MRI) radiomic features to establish a model for predicting distant metastasis under dynamic contrast-enhanced MRI imaging with deep learning algorithms. The deep learning algorithm was used to segment the images. A total of 96 cases with 100 lesions were included in the metastatic group, including 2 cases of bifocal breast cancer and 2 cases of multifocal breast cancer. There were 192 cases in the nonmetastatic group, with 197 lesions, including 5 cases of multifocal breast cancer. After dynamic contrast-enhancement, the morphological features and grayscale statistical features were extracted from the lesions to establish a prediction model through sum-sum check and feature dimension reduction. The accuracy, sensitivity, specificity, and area under receiver operator characteristic curve (AUC) of prediction models based only on imaging features were compared with those created by combining radiomic features with clinical and pathological features. The created predictive model based on radiomic features for distant metastases in breast cancer showed a sensitivity of 66.7%, a specificity of 84.2%, an accuracy of 78.3%, and an AUC of 0.744. The sensitivity of the prediction model for distant metastasis of breast cancer was 67.7%, the specificity was 86.8%, the accuracy was 80.5%, and the AUC was 0.763. Bone, lung, and liver were the most common distant metastatic sites of breast cancer. Under the dynamic contrast-enhanced MRI of deep learning, the prediction model combining radiomic features with clinical and pathological features showed better predictive performance.

MRI-Based Radiomics for Preoperative Prediction of Lymphovascular Invasion in Patients With Invasive Breast Cancer.

ObjectivePreoperative identification of lymphovascular invasion (LVI) in patients with invasive breast cancer is challenging due to absence of reliable biomarkers or tools in clinical settings. We aimed to establish and validate multiparametric magnetic resonance imaging (MRI)-based radiomic models to predict the risk of lymphovascular invasion (LVI) in patients with invasive breast cancer.MethodsThis retrospective study included a total of 175 patients with confirmed invasive breast cancer who had known LVI status and preoperative MRI from two tertiary centers. The patients from center 1 was randomly divided into a training set (n=99) and a validation set (n = 26), while the patients from center 2 was used as a test set (n=50). A total of 1409 radiomic features were extracted from the T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC), respectively. A three-step feature selection including SelectKBest, interclass correlation coefficients (ICC), and least absolute shrinkage and selection operator (LASSO) was performed to identify the features most associated with LVI. Subsequently, a Support Vector Machine (SVM) classifier was trained to develop single-layer radiomic models and fusion radiomic models. Model performance was evaluated and compared by the area under the curve (AUC), sensitivity, and specificity.ResultsBased on one feature of wavelet-HLH_gldm_GrayLevelVariance, the ADC radiomic model achieved an AUC of 0.87 (95% confidence interval [CI]: 0.80–0.94) in the training set, 0.87 (0.70-1.00) in the validation set, and 0.77 (95%CI: 0.64-0.86) in the test set. However, the combination of radiomic features derived from other MR sequences failed to yield incremental value.ConclusionsADC-based radiomic model demonstrated a favorable performance in predicting LVI prior to surgery in patients with invasive breast cancer. Such model holds the potential for improving clinical decision-making regarding treatment for breast cancer.

Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors.

Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs). To develop and validate a clinical-radiomics model based on multiparametric MRI and clinical information for the pretreatment differential diagnosis of PTs. Retrospective. A total of 216 patients with PTs, 133 in the training cohort (55 benign PTs [BPTs] and 78 borderline/malignant PTs [BMPTs]) and 83 in the validation cohort (28 BPTs and 55 BMPTs). 1.5T and 3T; T2-weighted imaging (T2WI), precontrast T1-weighted imaging (T1WI) and dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI). A total of 3138 radiomics features were computed to decode the imaging phenotypes of PTs. To build the classification models, the following workflow was followed: minimum-maximum scaling normalization method, recursive feature elimination based on ridge regression (Ridge-RFE), synthetic minority oversampling technique, and support vector machine classifier. We established several models based on the statistically significant features (Ridge-RFE selected) of each sequence to distinguish BPTs from BMPTs, including precontrast T1WI model, DCE-T1WI phase 1 model, T1WI feature fusion model, T2WI model, T1WI + T2WI model, clinical feature model, conventional MRI characteristics model, and combined clinical-radiomics model. Univariate analysis was utilized to compare variables between the BPT and BMPT groups. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these models. In the training cohort, the clinical-radiomics model had excellent diagnostic efficiency, with an area under ROC (AUC) of 0.91 ± 0.02 (95% CI: 0.87-0.94). In the validation cohort, the AUCs were 0.79 ± 0.05 (95% CI: 0.70-0.87) for the combined model and 0.77 ± 0.05 (95% CI: 0.67-0.85) for the radiomics model. Compared with conventional MRI characteristics, radiomics features extracted from multiparametric MRI are helpful for improving the accuracy of differentiating the pathological grades of PTs preoperatively. The model based on radiomics and clinical information is expected to become a potential noninvasive tool for the assessment of PTs grades. 4 TECHNICAL EFFICACY: Stage 2.

MRI-derived cardiac washout is slowed in the left ventricle and associated with left ventricular non-compaction in young patients with cryptogenic ischemic stroke

To elucidate underlying disease mechanisms, we compared transition of gadolinium-based contrast agent bolus in cardiac chambers in magnetic resonance imaging between young patents with cryptogenic ischemic stroke and stroke-free controls. We included 30 patients aged 18–50 years with cryptogenic ischemic stroke from the prospective Searching for Explanations for Cryptogenic Stroke in the Young: Revealing the Etiology, Triggers and Outcome (NCT01934725) study and 30 age- and gender-matched stroke-free controls. Dynamic contrast-enhanced T1-weighted first-pass perfusion images were acquired at 1.5 T and analyzed for transit time variables, area under curves, relative blood flow, and maximum and minimum enhancement rates in left atrial appendage, left atrium, and left ventricle. These data were compared with previously published left ventricular non-compaction data of the same study population. Arrival time of contrast agent bolus in superior vena cava was similar in patients and controls (6.7[2.0] vs. 7.1[2.5] cardiac cycles, P = 0.626). Arrival and peak times showed comparable characteristics in patients and controls (P > 0.535). The minimum enhancement rate of the left ventricle was lower in patients than in controls (− 28 ± 11 vs. − 36 ± 13 1/(cardiac cycle), P = 0.012). Area under curves, relative blood flow, and other enhancement rates showed no significant differences between patients and controls (P > 0.107). Relative blood flow of cardiac chambers correlated with non-compacted left ventricular volume ratio (P < 0.011). Our results indicate slower washout of contrast agent and blood flow stagnation in the left ventricle of young patients with cryptogenic ischemic stroke. The washout was associated with left ventricular non-compaction, suggesting conditions favoring formation of intraventricular thrombosis.

Four-Dimensional Machine Learning Radiomics for the Pretreatment Assessment of Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy in Dynamic Contrast-Enhanced MRI.

Breast cancer response to neoadjuvant chemotherapy (NAC) is typically evaluated through the assessment of tumor size reduction after a few cycles of NAC. In case of treatment ineffectiveness, this results in the patient suffering potentially severe secondary effects without achieving any actual benefit. To identify patients achieving pathologic complete response (pCR) after NAC by spatio-temporal radiomic analysis of dynamic contrast-enhanced (DCE) MRI images acquired before treatment. Single-center, retrospective. A total of 251 DCE-MRI pretreatment images of breast cancer patients. 1.5 T/3T, T1-weighted DCE-MRI. Tumor and peritumoral regions were segmented, and 348 radiomic features that quantify texture temporal variation, enhancement kinetics heterogeneity, and morphology were extracted. Based on subsets of features identified through forward selection, machine learning (ML) logistic regression models were trained separately with all images and stratifying on cancer molecular subtype and validated with leave-one-out cross-validation. Feature significance was assessed using the Mann-Whitney U-test. Significance of the area under the receiver operating characteristics (ROC) curve (AUC) of the ML models was assessed using the associated 95% confidence interval (CI). Significance threshold was set to 0.05, adjusted with Bonferroni correction. Nine features related to texture temporal variation and enhancement kinetics heterogeneity were significant in the discrimination of cases achieving pCR vs. non-pCR. The ML models achieved significant AUC of 0.707 (all cancers, n= 251, 59 pCR), 0.824 (luminal A, n= 107, 14 pCR), 0.823 (luminal B, n= 47, 15 pCR), 0.844 (HER2 enriched, n= 25, 11 pCR), 0.803 (triple negative, n= 72, 19 pCR). Differences in imaging phenotypes were found between complete and noncomplete responders. Furthermore, ML models trained per cancer subtype achieved high performance in classifying pCR vs. non-pCR cases. They may, therefore, have potential to help stratify patients according to the level of response predicted before treatment, pending further validation with larger prospective cohorts. 4 TECHNICAL EFFICACY: Stage 4.

Added Value of Viscoelasticity for MRI-Based Prediction of Ki-67 Expression of Hepatocellular Carcinoma Using a Deep Learning Combined Radiomics (DLCR) Model.

This study aimed to explore the added value of viscoelasticity measured by magnetic resonance elastography (MRE) in the prediction of Ki-67 expression in hepatocellular carcinoma (HCC) using a deep learning combined radiomics (DLCR) model. This retrospective study included 108 histopathology-proven HCC patients (93 males; age, 59.6 ± 11.0 years) who underwent preoperative MRI and MR elastography. They were divided into training (n = 87; 61.0 ± 9.8 years) and testing (n = 21; 60.6 ± 10.1 years) cohorts. An independent validation cohort including 43 patients (60.1 ± 11.3 years) was included for testing. A DLCR model was proposed to predict the expression of Ki-67 with cMRI, including T2W, DW, and dynamic contrast enhancement (DCE) images as inputs. The images of the shear wave speed (c-map) and phase angle (φ-map) derived from MRE were also fed into the DLCR model. The Ki-67 expression was classified into low and high groups with a threshold of 20%. Both c and φ values were ranked within the top six features for Ki-67 prediction with random forest selection, which revealed the value of MRE-based viscosity for the assessment of tumor proliferation status in HCC. When comparing the six CNN models, Xception showed the best performance for classifying the Ki-67 expression, with an AUC of 0.80 ± 0.03 (CI: 0.79-0.81) and accuracy of 0.77 ± 0.04 (CI: 0.76-0.78) when cMRI were fed into the model. The model with all modalities (MRE, AFP, and cMRI) as inputs achieved the highest AUC of 0.90 ± 0.03 (CI: 0.89-0.91) in the validation cohort. The same finding was observed in the independent testing cohort, with an AUC of 0.83 ± 0.03 (CI: 0.82-0.84). The shear wave speed and phase angle improved the performance of the DLCR model significantly for Ki-67 prediction, suggesting that MRE-based c and φ-maps can serve as important parameters to assess the tumor proliferation status in HCC.

Choroid Plexus Volume and Permeability at Brain MRI within the Alzheimer Disease Clinical Spectrum.

Background Mounting evidence suggests that the choroid plexus (CP) plays an important role in the pathophysiology of Alzheimer disease (AD), but its imaging profile in cognitive impairment remains unclear. Purpose To evaluate CP volume, permeability, and susceptibility by using MRI in patients at various stages of cognitive impairment. Materials and Methods This retrospective study evaluated patients with cognitive symptoms who underwent 3.0-T MRI of the brain, including dynamic contrast-enhanced (DCE) imaging and quantitative susceptibility mapping (QSM), between January 2013 and May 2020. CP volume was automatically segmented using three-dimensional T1-weighted sequences; the volume transfer constant (ie, Ktrans) and fractional plasma volume (ie, Vp) were determined using DCE MRI, and susceptibility was assessed using QSM. The effects of CP volume, expressed as the ratio to intracranial volume, on cognition were evaluated using multivariable linear regression adjusted for age, sex, education, apolipoprotein E ε4 allele status, and volumetric measures. Results A total of 532 patients with cognitive symptoms (mean age, 72 years ± 9 [SD]; 388 women) were included: 78 with subjective cognitive impairment (SCI), 158 with early mild cognitive impairment (MCI), 149 with late MCI, and 147 with AD. Among these, 132 patients underwent DCE MRI and QSM. CP volume was greater in patients at more severe stages (ratio of intracranial volume × 103: 0.9 ± 0.3 for SCI, 1.0 ± 0.3 for early MCI, 1.1 ± 0.3 for late MCI, and 1.3 ± 0.4 for AD; P < .001). Lower Ktrans (r = -0.19; P = .03) and Vp (r = -0.20; P = .02) were negatively associated with CP volume; susceptibility was not (r = 0.15; P = .10). CP volume was negatively associated with memory (B = -0.67; standard error of the mean [SEM], 0.21; P = .01), executive function (B = -0.90; SEM, 0.31; P = .01), and global cognition (B = -0.82; SEM, 0.32; P = .01). Conclusion Among patients with cognitive symptoms, larger choroid plexus volume was associated with severity of cognitive impairment in the Alzheimer disease spectrum. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Chiang in this issue.

Clinical validity of non-contrast-enhanced VI-RADS: prospective study using 3-T MRI with high-gradient magnetic field

ObjectivesTo develop a modified Vesical Imaging Reporting and Data System (VI-RADS) without dynamic contrast-enhanced imaging (DCEI), termed “non-contrast-enhanced VI-RADS (NCE-VI-RADS)”, and to assess the additive impact of denoising deep learning reconstruction (dDLR) on NCE-VI-RADS.MethodsFrom January 2019 through December 2020, 163 participants who underwent high-gradient 3-T MRI of the bladder were prospectively enrolled. In total, 108 participants with pathologically confirmed bladder cancer by transurethral resection were analyzed. Tumors were evaluated based on VI-RADS (scores 1–5) by two readers independently: an experienced radiologist (reader 1) and a senior radiology resident (reader 2). Conventional VI-RADS assessment included all three imaging types (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI], and dynamic contrast-enhanced imaging [DCEI]). Also evaluated were NCE-VI-RADS comprising only non-contrast-enhanced imaging types (T2WI and DWI), and “NCE-VI-RADS with dDLR” comprising T2WI processed with dDLR and DWI. All systems were assessed using receiver-operating characteristic curve analysis and simple and/or weighted κ statistics.ResultsMuscle invasion was identified in 23/108 participants (21%). Area under the curve (AUC) values for diagnosing muscle invasion were as follows: conventional VI-RADS, 0.94 and 0.91; NCE-VI-RADS, 0.93 and 0.91; and “NCE-VI-RADS with dDLR”, 0.96 and 0.93, for readers 1 and 2, respectively. Simple κ statistics indicated substantial agreement for NCE-VI-RADS and almost perfect agreement for conventional VI-RADS and “NCE-VI-RADS with dDLR” between the two readers.ConclusionNCE-VI-RADS achieved predictive accuracy for muscle invasion comparable to that of conventional VI-RADS. Additional use of dDLR improved the diagnostic accuracy of NCE-VI-RADS.Key Points• Non-contrast-enhanced Vesical Imaging Reporting and Data System (NCE-VI-RADS) was developed to avoid risk related to gadolinium-based contrast agent administration.• NCE-VI-RADS had predictive accuracy for muscle invasion comparable to that of conventional VI-RADS.• The additional use of denoising deep learning reconstruction (dDLR) might further improve the diagnostic accuracy of NCE-VI-RADS.