Precontrast T1-weighted Images Research Articles

Predictive Performance of Radiomic Features Extracted from Breast MR Imaging in Postoperative Upgrading of Ductal Carcinoma in Situ to Invasive Carcinoma.

To investigate the predictive performance of radiomic features extracted from breast MRI for upgrade of ductal carcinoma in situ (DCIS) to invasive carcinoma. This retrospective study included 71 women with DCIS lesions diagnosed preoperatively by biopsy. All women underwent breast dynamic contrast-enhanced (DCE) MRI of the breast, which included pre-contrast and five post-contrast phases continuously with a time resolution of 60s. Lesion segmentation was performed manually, and 144 radiomic features of the lesions were extracted from T2-weighted images (T2WI), pre-contrast T1-weighted images (T1WI), and post-contrast 1st, 2nd, and 5th phase subtraction images on DCE-MRI. Qualitative features of mammography, ultrasound, and MRI were also assessed. Clinicopathological features were evaluated using medical records. The least absolute shrinkage and selection operator (LASSO) algorithm was applied for features selection and model building. The predictive performance of postoperative upgrade to invasive carcinoma was assessed using the area under the receiver operating characteristic curve. Surgical specimens revealed 13 lesions (18.3%) that were upgraded to invasive carcinoma. Among clinicopathological and qualitative features, age was the only significant predictive variable. No significant radiomic features were observed on T2WI and post-contrast 2nd phase subtraction images on DCE-MRI. The area under the curves (AUCs) of the LASSO radiomics model integrated with age were 0.915 for pre-contrast T1WI, 0.862 for post-contrast 1st phase subtraction images, and 0.833 for post-contrast 5th phase subtraction images. The AUCs of the 200-times bootstrap internal validations were 0.885, 0.832, and 0.775. A radiomics approach using breast MRI may be a promising method for predicting the postoperative upgrade of DCIS. The present study showed that the radiomic features extracted from pre-contrast T1WI and post-contrast subtraction images in the very early phase of DCE-MRI were more predictable.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. Retrospective. 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. 3 TECHNICAL EFFICACY: Stage 3.

Clinical Significance of Background Parenchymal Enhancement in Breast Cancer Risk Stratification.

Background parenchymal enhancement (BPE) is an established breast cancer risk factor. However, the relationship between BPE levels and breast cancer risk stratification remains unclear. To evaluate the clinical relationship between BPE levels and breast cancer risk with covariate adjustments for age, ethnicity, and hormonal status. Retrospective. 954 screening breast MRI datasets representing 721 women divided into four cohorts: women with pathogenic germline breast cancer (BRCA) mutations (Group 1, N = 211), women with non-BRCA germline mutations (Group 2, N = 60), women without high-risk germline mutations but with a lifetime breast cancer risk of ≥20% using the Tyrer-Cuzick model (Group 3, N = 362), and women with <20% lifetime risk (Group 4, N = 88). 3 T/axial non-fat-saturated T1, short tau inversion recovery, fat-saturated pre-contrast, and post-contrast T1-weighted images. Data on age, body mass index, ethnicity, menopausal status, genetic predisposition, and hormonal therapy use were collected. BPE levels were evaluated by two breast fellowship-trained radiologists independently in accordance with BI-RADS, with a third breast fellowship-trained radiologist resolving any discordance. Propensity score matching (PSM) was utilized to adjust covariates, including age, ethnicity, menopausal status, hormonal treatments, and prior bilateral oophorectomy. The Mann-Whitney U test, chi-squared test, and univariate and multiple logistic regression analysis were performed, with an odds ratio (OR) and corresponding 95% confidence interval. Weighted Kappa statistic was used to assess inter-reader variation. A P value <0.05 indicated a significant result. In the assessment of BPE, there was substantial agreement between the two interpreting radiologists (κ = 0.74). Patient demographics were not significantly different between patient groups after PSM. The BPE of Group 1 was significantly lower than that of Group 4 and Group 3 among premenopausal women. In estimating the BPE level, the OR of gene mutations was 0.35. Adjusting for potential confounders, the BPE level of premenopausal women with BRCA mutations was significantly lower than that of non-high-risk women. 3 TECHNICAL EFFICACY: Stage 3.

MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study

BackgroundThere are few prospective studies on the correlations between MRI features and whole RNA-sequencing data in breast cancer according to molecular subtypes. The purpose of our study was to explore the association between genetic profiles and MRI phenotypes of breast cancer and to identify imaging markers that influences the prognosis and treatment according to subtypes.MethodsFrom June 2017 to August 2018, MRIs of 95 women with invasive breast cancer were prospectively analyzed, using the breast imaging-reporting and data system and texture analysis. Whole RNA obtained from surgical specimens was analyzed using next-generation sequencing. The association between MRI features and gene expression profiles was analyzed in the entire tumor and subtypes. Gene networks, enriched functions, and canonical pathways were analyzed using Ingenuity Pathway Analysis. The P value for differential expression was obtained using a parametric F test comparing nested linear models and adjusted for multiple testing by reporting Q value.ResultsIn 95 participants (mean age, 53 years ± 11 [standard deviation]), mass lesion type was associated with upregulation of CCL3L1 (sevenfold) and irregular mass shape was associated with downregulation of MIR421 (sixfold). In estrogen receptor-positive cancer with mass lesion type, CCL3L1 (21-fold), SNHG12 (11-fold), and MIR206 (sevenfold) were upregulated, and MIR597 (265-fold), MIR126 (12-fold), and SOX17 (fivefold) were downregulated. In triple-negative breast cancer with increased standard deviation of texture analysis on precontrast T1-weighted imaging, CLEC3A (23-fold), SRGN (13-fold), HSPG2 (sevenfold), KMT2D (fivefold), and VMP1 (fivefold) were upregulated, and IGLC2 (73-fold) and PRDX4 (sevenfold) were downregulated (all, P < 0.05 and Q < 0.1). Gene network and functional analysis showed that mass type estrogen receptor-positive cancers were associated with cell growth, anti-estrogen resistance, and poor survival.ConclusionMRI characteristics are associated with the different expressions of genes related to metastasis, anti-drug resistance, and prognosis, depending on the molecular subtypes of breast cancer.

Software for brain tumor diagnosis on magnetic resonance imaging

BACKGROUND: The main reason for the development and implementation of artificial intelligence (AI) technologies in neuro-oncology is the high prevalence of brain tumors reaching up to 200 cases per 100,000 population. The incidence of a primary focus in the brain is 5%10%; however, 60%70% of those who die from malignant neoplasms have metastases in the brain. Magnetic resonance imaging (MRI) is the most common method for primary non-invasive diagnosis of brain tumors and monitoring disease progression. One of the challenges is the classification of tumor types and determination of clinical parameters (size and volume) for the conduct, diagnosis, and treatment procedures, including surgery.&#x0D; AIM: To develope a software module for the differential diagnosis of brain neoplasms on MRI images.&#x0D; METHODS: The software module is based on the developed Siberian Brain Tumor Dataset (SBT), which contains information on over 1000 neurosurgical patients with fully verified (histologically and immunohistochemically) postoperative diagnoses. The data for research and development was presented by the Federal Neurosurgical Center (Novosibirsk). The module uses two- and three-dimensional computer vision models with pre-processed MRI sequence data included in the following packages: pre-contrast T1-weighted image (WI), post-contrast T1-WI, T2-WI, and T2-WI with fluid-attenuated inversion-recovery technique. The models allow to detect and recognize with high accuracy 4 types of neoplasms, such as meningioma, neurinoma, glioblastoma, and astrocytoma, and segment and distinguish components and sizes: ET (tumor core absorbing Gd-containing contrast), TC (tumor core) = ET + Necr (necrosis) + NenTu, and WT (whole tumor) = TC + Ed (peritumoral edema).&#x0D; RESULTS: The developed software module shows high segmentation results on SBT by Dice metric for ET 0.846, TC 0.867, WT 0.9174, Sens 0.881, and Spec 1.000 areas. The testing and validation were done at the international BraTS Challenge 2021 competition. The test dataset yielded DiceET 0.86588, DiceTC 0.86932, and DiceWT 0.921 values, placing the developed software module in the top ten. According to the classification, the results demonstrate high accuracy rates of up to 92% in patient analysis (up to 89% in slice analysis), a very high potential, and a perspective for future research in this area.&#x0D; CONCLUSIONS: The developed software module may be used for training specialists and in clinical diagnostics.

Muscle mass loss in breast cancer patients of reproductive age (≤ 45years) undergoing neoadjuvant chemotherapy.

Loss of muscle mass is associated with negative clinical outcome in breast cancer (BC) patients. Therefore, the aim of the study is to evaluate if there is pectoralis muscle area (PMA) depletion, reflecting loss of muscle mass, in breast cancer patients of reproductive age (≤ 45years) undergoing neoadjuvant chemotherapy (NAC) and to correlate PMA with clinical and histopathological data. This monocentric study, approved by our institutional review board, enrolled a total of 52 consecutive patients (mean age 37 ± 4.96years) with histologically proven primary breast cancer between January 2019 and September 2021, treated with NAC and in whom tumor response and PMA were assessed with breast MRI. Two radiologists calculated PMA before and after NAC independently and blindly on axial 3D FLASH pre-contrast T1-weighted images. Wilcoxon-Mann-Whitney U test compared median values and percentage changes of pectoralis muscle area at the beginning and at the end of NAC (158 ± 25.5days). Multivariate regression analysis on ΔPMA (difference between PMA pre-NAC and PMA post-NAC) was done according to clinical and histopathological data. Inter-reader and intra-reader agreement was estimated with K statistics. Pre-NAC PMA mean value was larger than post-NAC PMA mean value (9.6 ± 2.6cm2 vs. 8.7 ± 2.2cm2, p < 0.001, delta value 1.41). According to the RECIST criteria, no significant differences between complete and partial response were found. Multivariate regression analysis did not show any significant relationships between ΔPMA and age, time between MRI examinations, estrogen and progesterone receptor status, human epidermal growth factor receptor 2 status, Ki-67 expression, lymph node involvement, RECIST criteria, histological type, and different regimes of NAC. Inter-reader (k = 0.74) and intra-reader agreement (0.67 and 0.73) in PMA assessment was good. PMA variation in BC young patients, directly estimated on breast MRI, could be a potential tool to monitor body composition during NAC with potential implications in improving outcome.

Diffusion-Weighted MR Imaging of the Optic Nerve Can Improve the Detection of Post-Laminar Optic Nerve Invasion from Retinoblastoma.

Post-laminar optic nerve invasion (PLONI) is a high-risk factor for the metastasis of retinoblastoma (RB). Unlike conventional MRI, diffusion-weighted imaging (DWI) reflects histopathological features, and may aid the assessment of PLONI. To determine the value of conventional MRI plus DWI in detecting PLONI in RB patients. Retrospective. Eighty-three RB patients, including 28 with histopathologically proven PLONI and 55 without PLONI. 3.0 T, precontrast axial T1-weighted and T2-weighted imaging, DWI, and postcontrast axial, coronal, and oblique-sagittal T1-weighted imaging. PLONI was assessed using post-enucleation histology and preoperative MRI features (optic nerve signal intensity, enlargement, and enhancement on conventional MRI, and apparent diffusion coefficient [ADC] of the optic nerve on DWI) evaluated by three observers. MRI features suggesting the presence of PLONI were identified using univariable and multivariable analyses. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to analyze diagnostic performance. Optic nerve enhancement and low ADC of the optic nerve were significant indicators of PLONI. ROC curve analysis showed that the AUC of the combination of these two features for detecting PLONI was 0.87 (95% confidence interval [CI]: 0.78-0.93). The diagnostic performance of this model was significantly superior to that of optic nerve enhancement alone (0.76, 95% CI: 0.65-0.85) and marginally superior to that of the ADC of the affected optic nerve (0.78, 95% CI: 0.68-0.87, P=0.051). Conventional MRI combined with DWI can improve the detection of PLONI in RB patients over conventional MRI alone. 3 Technical Efficacy: Stage 2.

MRI-Based Radiomics Nomogram for Preoperative Differentiation Between Ocular Adnexal Lymphoma and Idiopathic Orbital Inflammation.

Ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI) are malignant and benign lesions for which radiotherapy and corticosteroids are indicated, but similar clinical manifestations make their differentiation difficult. To develop and validate an MRI-based radiomics nomogram for individual diagnosis of OAL vs. IOI. Retrospective. A total of 103 patients (46.6% female) with mean age of 56.4 ± 16.3 years having OAL (n=58) or IOI (n=45) were divided into an independent training (n=82) and a testing dataset (n=21). A 3-T, precontrast T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and postcontrast T1WI (T1 + C). Radiomics features were extracted and selected from segmented tumors and peritumoral regions in MRI before-and-after filtering. These features, alone or combined with clinical characteristics, were used to construct a radiomics or joint signature to differentiate OAL from IOI, respectively. A joint nomogram was built to show the impact of the radiomics signature and clinical characteristics on individual risk of developing OAL. Area under the curve (AUC) and accuracy (ACC) were used for performance evaluation. Mann-Whitney U and Chi-square tests were used to analyze continuous and categorical variables. Decision curve analysis, kappa statistics, DeLong and Hosmer-Lemeshow tests were also conducted. P < 0.05 was considered statistically significant. The joint signature achieved an AUC of 0.833 (95% confidence interval [CI]: 0.806-0.870), slightly better than the radiomics signature with an AUC of 0.806 (95% CI: 0.767-0.838) (P=0.778). The joint and radiomics signatures were comparable to experienced radiologists referencing to clinical characteristics (ACC=0.810 vs. 0.796-0.806, P > 0.05) or not (AUC=0.806 vs. 0.753-0.791, P > 0.05), respectively. The joint nomogram gained more net benefits than the radiomics nomogram, despite both showing good calibration and discriminatory efficiency (P > 0.05). The developed radiomics-based analysis might help to improve the diagnostic performance and reveal the association between radiomics features and individual risk of developing OAL. 3 TECHNICAL EFFICACY: 3.

The Influence of Gd-EOB-DTPA on T2 Signal Behavior: An Example from Clinical Routine.

In the literature, it has repeatedly been stated that the introduction of hepatospecific contrast agents in Magnetic Resonance Imaging prolongs the acquisition time due to the hepatobiliary phase, normally acquired 15–20 min after injection. Many efforts have been made to shorten the time-consuming protocols, and it was demonstrated that T2-Weighted Images (T2WI) and Diffusion-Weighted Images (DWI) acquired after Gd-EOB-DTPA show a comparable diagnostic capability to pre-contrast T2WI and DWI in the detection and characterization of hepatic tumors. Therefore, T2WI and DWI are usually acquired after the acquisition of vascular phases, in the dead time until the acquisition of the hepatobiliary phase. Unfortunately, contrast agents, especially Gd-EOB-DTPA, reduce the hydrogen nuclei’s relaxation time and modify signal intensity. We report a case in which, due to these limitations of the acquisition protocol, two hemangiomas showed an inhomogeneous, low signal on T2WI and DWI that was not visible in a follow-up scan a few days later. In conclusion, when liver lesions of unknown nature must be characterized, and there is a lack of previous radiological investigations, it could be useful to acquire pre-contrast T2WI and DWI to avoid diagnostic confusion, especially in non-tertiary centers.

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.

Multiparametric Large Field of View Rheumatology Imaging for Axial Spondyloarthropathy Detects Enthesitis in Setting of Inactive Sacroiliac Joint Disease and Impacts Clinical Diagnosis.

To test the diagnostic efficacy of a multiparametric rheumatology lumbosacral magnetic resonance (MR) imaging protocol in detection and characterization of axial spondylarthritis (SpA) and compare it with serology and clinical findings. A consecutive series of multiparametric rheumatology lumbosacral MR imaging examinations performed on 3T MR scanner. Three-dimensional inversion recovery turbo spin echo, precontrast and postcontrast fat-suppressed T1-weighted images, as well as diffusion-weighted images were used to detect active erosions and enthesitis using established criteria. Pearson χ2 was used for categorical variables. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were measured for magnetic resonance imaging (MRI) and serology, based on the final diagnosis from rheumatologists. An alpha error below 0.05 was considered statistically significant. The final study sample included 130 consecutive patients (80 women and 50 men; mean ± SD 44 ± 13 and 45 ± 14 years, respectively). Seventy-eight subjects were diagnosed with axial SpA and 52 with non-SpA arthropathy. In the non-SpA group, 27 patients were diagnosed with osteoarthritis, 6 had unremarkable imaging, whereas 19 were considered as clinically undetermined. There was positive correlation between positive MRI results and SpA diagnosis (P < 0.00001). No correlation existed between positive serology alone and SpA diagnosis (P = 0.0634). Although MRI and serology proved equally sensitive in detecting SpA, the specificity and overall accuracy of MRI were significantly higher. Inflammatory activity was detected in 45 (57.7%) cases, in the pelvic enthesis in 29 (37.2%) cases, in the lumbosacral spine in 16 (20.5%) cases, in the hip joints in 15 (19.2%) cases, and in the pubic symphysis in 5 (6.4%). Inactive sacral disease was seen in 7 of 35 enthesitis patients (20.0%), and in 2 SpA cases, there were no sacral lesions. The results suggest that in patients with suspected SpA, MRI should not be limited to the sacroiliac joints, but also include enthesitis sites and other joints of the axial skeleton. The multiparametric rheumatology protocol increases the efficacy of MRI in detecting enthesitis and joint inflammatory disease, thereby offering additional information to the clinician and assisting in the early diagnosis/detecting disease activity.

MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas.

To evaluate the diagnostic performance of MRI-based radiomics model for differentiating phyllodes tumors of the breast from fibroadenomas. This retrospective study included 88 patients (32 with phyllodes tumors and 56 with fibroadenomas) who underwent MRI. Radiomic features were extracted from T2-weighted image, pre-contrast T1-weighted image, and the first-phase and late-phase dynamic contrast-enhanced MRIs. To create stable machine learning models and balanced classes, data augmentation was performed. A least absolute shrinkage and selection operator (LASSO) regression was performed to select features and build the radiomics model. A radiological model was constructed from conventional MRI features evaluated by radiologists. A combined model was constructed using both radiomics features and radiological features. Machine learning classifications were done using support vector machine, extreme gradient boosting, and random forest. The area under the receiver operating characteristic (ROC) curve (AUC) was computed to assess the performance of each model. Among 1070 features, the LASSO logistic regression selected 35 features. Among three machine learning classifiers, support vector machine had the best performance. Compared to the radiological model (AUC: 0.77 ± 0.11), the radiomics model (AUC: 0.96 ± 0.04) and combined model (0.97 ± 0.03) had significantly improved AUC values (both p < 0.01) in the validation set. The combined model had a relatively higher AUC than that of the radiomics model in the validation set, but this was not significantly different (p = 0.391). Radiomics analysis based on MRI showed promise for discriminating phyllodes tumors from fibroadenomas. • The radiomics model and the combined model were superior to the radiological model for differentiating phyllodes tumors from fibroadenomas. • The SVM classifier performed best in the current study. • MRI-based radiomics model could help accurately differentiate phyllodes tumors from fibroadenomas.

Precision of MRI radiomics features in the liver and hepatocellular carcinoma.

To assess the precision of MRI radiomics features in hepatocellular carcinoma (HCC) tumors and liver parenchyma. The study population consisted of 55 patients, including 16 with untreated HCCs, who underwent two repeat contrast-enhanced abdominal MRI exams within 1month to evaluate: (1) test-retest repeatability using the same MRI system (n = 28, 10 HCCs); (2) inter-platform reproducibility between different MRI systems (n = 27, 6 HCCs); (3) inter-observer reproducibility (n = 16, 16 HCCs). Shape and 1st- and 2nd-order radiomics features were quantified on pre-contrast T1-weighted imaging (WI), T1WI portal venous phase (pvp), T2WI, and ADC (apparent diffusion coefficient), on liver regions of interest (ROIs) and HCC volumes of interest (VOIs). Precision was assessed by calculating intraclass correlation coefficient (ICC), concordance correlation coefficient (CCC), and coefficient of variation (CV). There was moderate to excellent test-retest repeatability of shape and 1st- and 2nd-order features for all sequences in HCCs (ICC: 0.53-0.99; CV: 3-29%), and moderate to good test-retest repeatability of 1st- and 2nd-order features for T1WI sequences, and 2nd-order features for T2WI in the liver (ICC: 0.53-0.73; CV: 12-19%). There was poorinter-platform reproducibility for all features and sequences, except for shape and 1st-order features on T1WI in HCCs (CCC: 0.58-0.99; CV: 3-15%). Good to excellent inter-observer reproducibility was found for all features and sequences in HCCs (CCC: 0.80-0.99; CV: 4-15%) and moderate to good for liver (CCC: 0.45-0.86; CV: 6-25%). MRI radiomics features have acceptable repeatability in the liver and HCC when using the same MRI system and across readers but have low reproducibility across MR systems, except for shape and 1st-order features on T1WI. Data must be interpreted with caution when performing multiplatform radiomics studies. • MRI radiomics features have acceptable repeatability when using the same MRI system but less reproducible when using different MRI platforms. • MRI radiomics features extracted from T1 weighted-imaging show greater stability across exams than T2 weighted-imaging and ADC. • Inter-observer reproducibility of MRI radiomics features was found to be good in HCC tumors and acceptable in liver parenchyma.

Pre-contrast T1-weighted imaging of the spinal cord may be unnecessary in patients with multiple sclerosis

ObjectivesMultiple sclerosis (MS) is an inflammatory disease frequently involving the spinal cord, which can be assessed by magnetic resonance imaging (MRI). Here, we hypothesize that pre-contrast T1-w imaging does not add diagnostic value to routine spinal MRI for the follow-up of patients with MS.Methods3-T MRI scans including pre- and post-contrast T1-w as well as T2-w images of 265 consecutive patients (mean age: 40 ± 13 years, 169 women) with (suspected) MS were analyzed retrospectively. Images were assessed in two separate reading sessions, first excluding and second including pre-contrast T1-w images. Two independent neuroradiologists rated the number of contrast-enhancing (ce) lesions as well as diagnostic confidence (1 = unlikely to 5 = very high), overall image quality, and artifacts. Results were compared using Wilcoxon matched-pairs signed-rank tests and weighted Cohen’s kappa (κ).ResultsFifty-six ce lesions were found in 43 patients. There were no significant differences in diagnostic confidence between both readings for both readers (reader 1: p = 0.058; reader 2: p = 0.317). Inter-rater concordance was both moderate regarding artifacts (κ = 0.418) and overall image quality (κ = 0.504). Thirty-one black holes were found in 25 patients with high diagnostic confidence (reader 1: 4.04 ± 0.81; reader 2: 3.80 ± 0.92) and substantial inter-rater concordance (κ = 0.700).ConclusionsAvailability of pre-contrast T1-w images did not significantly increase diagnostic confidence or detection rate of ce lesions in the spinal cord in patients with MS. Thus, pre-contrast T1-w sequences might be omitted in routine spinal MRI for follow-up exams, however not in special unclear clinical situations in which certainty on contrast enhancement is required.Key PointsAvailability of pre-contrast T1-w images does not increase diagnostic confidence or detection rate of contrast-enhancing lesions in the spinal cord of MS patients.Excluding pre-contrast T1-w sequences reduces scan time, thus providing more time for other sequences or increasing the patients’ compliance.

High-resolution vessel wall magnetic resonance imaging for depicting imaging features of unruptured intracranial vertebrobasilar dissecting aneurysms.

ObjectiveTo demonstrate the application value of high-resolution vessel wall magnetic resonance imaging (HR-VW-MRI) for depicting the imaging features of unruptured intracranial vertebrobasilar dissecting aneurysms (VBDAs).MethodsHR-VW-MRI data of 49 patients with suspected unruptured VBDAs were retrospectively analyzed. The presence of intramural hematomas (IMH), double lumens, intimal flaps, and outer diameter enlargements were recorded. Specificity and sensitivity were calculated for both two-dimensional (2D) and three-dimensional (3D) sequences. Additionally, IMH volumes were measured and posterior inferior cerebellar artery (PICA) involvement was analyzed.ResultsThirty-five VBDAs were confirmed in 34 patients. The overall sensitivity and specificity were 0.889 (95% confidence interval [CI]: 0.730–0.964) and 0.769 (95% CI: 0.460–0.938) for 2D sequences, and 0.917 (95% CI: 0.764–0.978) and 0.846 (95% CI: 0.537–0.973) for 3D sequences, respectively. Intimal flaps were detected in 57.1%, 87.5%, and 71.4% of all cases on 2D pre-contrast T1-weighted, contrast-enhanced T1-weighted, and 3D T1-weighted black-blood (BB) images, respectively. There was no significant difference in IMH volume between 3D T1-weighted BB and magnetization-prepared rapid gradient-echo sequences. PICA involvement was best visualized using 3D T1 sequences.Conclusion3D T1-weighted BB MRI provided good visualization of VBDA features, with large coverage, and was useful for detecting dissection flaps.

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning

Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression. In this study, 43 biopsy-proven patient data identified as diffuse infiltrating glioma patients whose disease progressed/recurred were used. The dataset consists of five original MRI sequences; pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, FLAIR, and ADC images as well as two engineered sequences; T1post–T1pre and T2–FLAIR. Next, we used three CNN-LSTM models with a different set of sequences as input sequences to pass through CNN-LSTM layers. We performed threefold cross-validation in the training dataset and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dataset to evaluate models. The mean accuracy for VGG16 models ranged from 0.44 to 0.60 and the mean AUC ranged from 0.47 to 0.59. For CNN-LSTM model, the mean accuracy ranged from 0.62 to 0.75 and the mean AUC ranged from 0.64 to 0.81. The performance of the proposed CNN-LSTM with multiparametric sequence data was found to outperform the popular convolutional CNN with a single MRI sequence. In conclusion, incorporating all available MRI sequences into a sequence input for a CNN-LSTM model improved diagnostic performance for discriminating between pseudoprogression and true tumor progression.

MRI Features of Presumed Primary Extranodal Lymphoma of the Bulbospongiosus Muscle Causing Stranguria

ABSTRACT Hematopoietic neoplasia is common in dogs, with canine non-Hodgkin lymphomas representing more than 80% of all hematopoietic cancer. However, extranodal infiltration of the skeletal muscle by non-Hodgkin lymphoma is rare in humans and dogs. A 9 yr old neutered male English mastiff presented with a 3 wk history of recurrent stranguria, pelvic limb ataxia, and mild proprioceptive deficits bilaterally, worse in the right pelvic limb. MRI showed an expansile ill-defined lesion within the bulbospongiosus muscle. The lesion had intermediate signal intensity to muscle and fat on T2-weighted imaging and was isointense to unaffected muscle on precontrast T1-weighted imaging. Contrast enhancement was heterogeneous and there was digitate signal alteration within adjacent perilesional fat. Ultrasound examination confirmed a hypoechoic lesion infiltrating the muscle. Cytological examination yielded a diagnosis of high-grade lymphoma. This report provides the first description of MRI findings associated with cytologically confirmed lymphoma of the skeletal muscle in the dog. Although nonspecific, the imaging features strongly correlate with those in the medical literature and lymphoma should be considered a pertinent differential in cases presenting with similar imaging findings.

Primary melanotic tumors of the nervous system: a consecutive case series.

Primary melanotic tumors of the nervous system (PMTNS) are thought to be an exceedingly rare group of tumors not captured by tumor registries. We aimed to determine relative incidence, clinical presentation, diagnostic findings, patient management, and outcome. We retrospectively searched the database of the Section of Neuro-Oncology at the Yale Cancer Center for patients with primary or metastatic melanotic lesions of the nervous system. For patients with PMTNS, we recorded demographic data, clinical presentation, histopathological and imaging findings, therapy, and outcome. A total of 116 patients with melanotic lesions were identified, including four patients with PMTNS. The relative incidence of PMTNS was therefore calculated as 3.4%. Histology of PMTNS patients revealed melanocytoma in three patients and psammomatous melanotic schwannoma in one patient. Symptoms were non-specific and attributed to tumor mass effect. Magnetic resonance imaging showed hyperintensity on pre-contrast T1-weighted imaging, hypointensity on T2-weighted imaging, and homogenous contrast enhancement in all PMTNS patients. Definitive diagnosis was based on tissue analysis, with detection of melanin-containing cells on conventional histology and S100-positivity on immunohistochemistry. Molecular analysis for GNAQQ209L mutation assisted in establishing diagnosis when only small amounts of tissue were available. Aggressive surgical treatment showed favorable outcomes in all cases; radiation therapy was used for residual or relapsed disease. The median follow-up was 7.5±5years, and all patients were alive on the day of database closure. Primary melanotic tumors of the nervous system are rare nervous system tumors. Outcome appears excellent, and complete surgical resection may form the basis for favorable outcome. Radiation therapy may represent a therapeutic approach for residual or relapsed disease.

The switch from Gd-DTPA to Gd-DOTA is not associated with decrease of the T1 signal intensity of the pallidus and dentate in a pediatric population.

The switch from the linear gadolinium-based contrast agent (GBCA) gadopentate dimeglumine (Gd_DTPA) to the macrocyclic GBCA gadobutrol is associated with a decrease of the T1 signal intensity (SI) in brain gray matter nuclei. The effects of the switch to other macrocyclic GBCAs are not yet established. To explore the effects of switching from Gd-DTPA to the macrocyclic GBCA gadoterate meglumine (Gd-DOTA) in pediatric patients. We measured the pallidus/middle cerebellar peduncle (MCP) SI ratio and the dentate/MCP SI ratio in pre-contrast sagittal T1-weighted spin-echo images in nine patients who had received ≥6 administrations of Gd-DTPA and then of Gd-DOTA, in 18 patients who had received ≥6 administrations of Gd-DOTA alone, and in nine age-matched controls without prior GBCA administrations. Serial assessment was performed in patients who switched from Gd-DTPA to Gd-DOTA. Finally, the rate of change of pallidal/MCP and dentate/MCP SI ratios between the first and last Gd-DOTA administrations was compared. The pallidal/MCP and dentate/MCP SI ratios were (P < 0.05) higher in patients with prior Gd-DTPA and Gd-DOTA administrations compared to the controls. After the switch, the pallidal/MCP SI ratio increased in nine patients and the dentate/MCP ratio in seven patients. The rate of change of pallidal/MCP SI ratio after Gd-DOTA was higher (P < 0.01) in patients who had previously received Gd-DTPA (mean 2.89 ± 2.6%) than in patients who had received Gd-DOTA alone (mean 0.53 ± 0.89%). T1 SI in gray matter nuclei does not decrease after switching from Gd-DTPA to Gd-DOTA. The switch effects from Gd-DTPA to each macrocyclic GBCA should be individually evaluated.

Radiologic-Pathologic Correlation of Hepatobiliary Phase Hypointense Nodules without Arterial Phase Hyperenhancement at Gadoxetic Acid-enhanced MRI: A Multicenter Study.

Background Hepatobiliary phase (HBP) hypointense nodules without arterial phase hyperenhancement (APHE) at gadoxetic acid-enhanced MRI may indicate hepatocellular carcinoma (HCC) or nonmalignant cirrhosis-associated nodules. Purpose To assess the distribution of pathologic diagnoses of HBP hypointense nodules without APHE at gadoxetic acid-enhanced MRI and to evaluate clinical and imaging features in differentiating their histologic grades. Materials and Methods This retrospective multicenter study included pathologic analysis-confirmed HBP hypointense nodules without APHE (≤30 mm) in patients with chronic liver disease or cirrhosis screened between January 2008 and June 2016. Central pathologic review by 10 pathologists determined final histologic grades as progressed HCC, early HCC, high-grade dysplastic nodule (DN), and low-grade DN or regenerative nodule. Gadoxetic acid-enhanced MRI features were analyzed by three radiologists. Multivariable logistic regression analyses with elastic net regularization were performed to identify clinical and imaging features for differentiating histologic grades. Results There were 298 patients (mean age, 59 years ± 10; 226 men) with 334 nodules evaluated, and progressed HCCs were diagnosed in 44.0% (147 of 334), early HCCs in 20.4% (68 of 334), high-grade DNs in 27.5% (92 of 334), and low-grade DNs or regenerative nodules in 8.1% (27 of 334). Serum α-fetoprotein level 100 ng/mL or greater (odds ratio, 2.7; P = .01) and MRI features including well-defined margin (odds ratio, 5.5; P = .003), hypointensity at precontrast T1-weighted imaging (odds ratio, 3.2; P < .001), intermediate hyperintensity at T2-weighted imaging (odds ratio, 3.4; P < .001), and restricted diffusion (odds ratio, 1.9; P = .04) were independent predictors for progressed HCC at multivariable analysis. Conclusion In patients at high risk for hepatocellular carcinoma (HCC), hepatobiliary phase hypointense nodules without arterial phase hyperenhancement at gadoxetic acid-enhanced MRI corresponded mainly to progressed HCCs, early HCCs, and high-grade dysplastic nodules. High α-fetoprotein level and some imaging features at MRI helped to differentiate progressed HCC from lower grade nodules. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Motosugi in this issue.