Brain imaging: Comparison of T1W FLAIR BLADE with conventional T1W SE

IntroductionAlthough T1 weighted spin echo (T1W SE) images are widely used to study anatomical details and pathologic abnormalities of the brain, its role in delineation of lesions and reduction of artifacts has not been thoroughly investigated. BLADE is a fairly new technique that has been reported to reduce motion artifacts and improve image quality.ObjectiveThe primary objective of this study is to compare the quality of T1-weighted fluid attenuated inversion recovery (FLAIR) images with BLADE technique (T1W FLAIR BLADE) and the quality of T1W SE images in the MR imaging of the brain. The goal is to highlight the advantages of the two sequences as well as which one can better reduce flow and motion artifacts so that the imaging of the lesions will not be impaired.Materials and methodsBrain examinations with T1W FLAIR BLADE and T1W SE sequences were performed on 48 patients using a 1.5 T scanner. These techniques were evaluated by two radiologists based on: a) a qualitative analysis i.e. overall image quality, presence of artifacts, CSF nulling; and b) a quantitative analysis of signal-to-noise ratios (SNR), contrast-to-noise ratios (CNR) and Relative Contrast. The statistical analysis was performed using the Kruskal-Wallis non-parametric system.ResultsIn the qualitative analysis, BLADE sequences had a higher scoring than the conventional sequences in all the cases. The overall image quality was better on T1W FLAIR BLADE. Motion and flow-related artifacts were lower in T1W FLAIR BLADE. Regarding the SNR measurements, T1W SE appeared to have higher values in the majority of cases, whilst T1W-FLAIR BLADE had higher values in the CNR and Relative Contrast measurements.ConclusionT1W FLAIR BLADE sequence appears to be superior to T1W SE in overall image quality and reduction of motion and flow-pulsation artifacts as well as in nulling CSF and has been preferred by the clinicians. T1W FLAIR BLADE may be an alternative approach in brain MRI imaging.     

Superficial fibromatosis: MRI radiomics and T2 mapping correlate with treatment response

BackgroundSuperficial fibromatosis exhibits variable MR signal intensity due to collagenous and fibroproliferative components. Quantifying this signal heterogeneity using image texture analysis and T2-mapping could have prognostic and therapeutic implications.MethodsThis IRB-approved retrospective study included 13 patients with superficial fibromatosis, managed by observation, electron beam radiotherapy (EBT), or pentoxifylline/vitamin E. Two-dimensional regions of interest (ROIs) were drawn on proton-density or T2-weighted MRI for radiomics feature analysis, and corresponding T2-maps. Comparisons were made between baseline and follow-up T2 relaxation times and radiomics features: Shannon's entropy, kurtosis, skewness, mean of positive pixels (MPP), and uniformity of distribution of positive gray-level pixel values (UPP).ResultsThere were 19 nodules in 13 subjects. Mean patient age was 60 years; 62% (8/13) were female; mean follow-up was 9.7 months. Nodule diameter at baseline averaged 18.2 mm (std dev 16.2 mm) and decreased almost 10% to 16.6 mm (p = 0.1, paired t-test). Normalized T2 signal intensity decreased 23% from 0.71 to 0.55 (p = 0.03, paired t-test). T2 relaxation time decreased 16% from 46.5 to 39.1 ms (p < 0.001, paired t-test). Among radiomics features, skewness increased to 0.71 from 0.41 (p = 0.03, paired t-test), and entropy decreased from 8.37 to 8.03 (p = 0.05, paired t-test); differences in other radiomics features were not significant.ConclusionsRadiomics analysis and T2-mapping of superficial fibromatosis is feasible; robust decreases in absolute T2 relaxation time, and changes in image textural features (increased skewness and decreased entropy) offer novel imaging biomarkers of nodule collagenization and maturation.     

Prognosticating brain tumor patient survival after laser thermotherapy: Comparison between neuroradiological reading and semi-quantitative analysis of MRI data

Background and purposeGiven increasing interest in laser interstitial thermotherapy (LITT) to treat brain tumor patients, we explored if examining multiple MRI contrasts per brain tumor patient undergoing surgery can impact predictive accuracy of survival post-LITT.Materials and methodsMRI contrasts included fluid-attenuated inversion recovery (FLAIR), T1 pre-gadolinium (T1pre), T1 post-gadolinium (T1Gd), T2, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), susceptibility weighted images (SWI), and magnetization-prepared rapid gradient-echo (MPRAGE). The latter was used for MRI data registration across preoperative to postoperative scans. Two ROIs were identified by thresholding preoperative FLAIR (large ROI) and T1Gd (small ROI) images. For each MRI contrast, a numerical score was assigned based on changing image intensity of both ROIs (vs. a normal ROI) from preoperative to postoperative stages. The fully-quantitative method was based on changing image intensity across scans at different stages without any human intervention, whereas the semi-quantitative method was based on subjective criteria of cumulative trends across scans at different stages. A fully-quantitative/semi-quantitative score per patient was obtained by averaging scores for each MRI contrast. A standard neuroradiological reading score per patient was obtained from radiological interpretation of MRI data. Scores from all 3 methods per patient were compared against patient survival, and re-examined for comorbidity and pathology effects.ResultsPatient survival correlated best with semi-quantitative scores obtained from T1Gd, ADC, and T2 data, and these correlations improved when biopsy and comorbidity were included.ConclusionThese results suggest interfacing neuroradiological readings with semi-quantitative image analysis can improve predictive accuracy of patient survival.     

基于影像组学的直肠癌术前T分期预测

直肠癌T分期对患者的术前评估有重要作用.然而,传统的放射科医生根据患者磁共振图像直接判断分期的方法效果欠佳.本文提出使用影像组学的方法预测直肠癌T分期,首先获取105例直肠癌患者影像数据,根据病理报告中的T分期结果将T1、T2期患者划分为未突破肌层组,将T3、T4期患者分为突破肌层组,整理数据得到未突破肌层组31例,突破肌层组74例.在患者的轴向位T2WI图像中勾画病灶区域,并在病灶上使用pyradiomics工具包提取影像组学特征,使用最小绝对值收敛和选择算子（LASSO）对高维特征做特征选择,得到与T分期高度相关的特征数据,使用随机森林、支持向量机（SVM）、逻辑回归、梯度提升树（GBDT）分别建模,进行交叉验证调参,评估模型性能.每层图像提取100维特征,经LASSO特征选择后得到7个与T分期高度相关的特征,使用4种模型分别建模,其中SVM算法表现最优,平均受试者操作特征曲线下面积（AUC）、准确率、灵敏度、特异度分别为0.968 5、0.886 4、0.962 5、0.899 2,测试集准确率达到了0.904 7.结果表明,使用影像组学方法可以提高直肠癌T分期预测的准确率.     

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions

PurposeTo assess a radiomic machine learning (ML) model in classifying solid adrenal lesions (ALs) without fat signal drop on chemical shift (CS) as benign or malignant.Method55 indeterminate ALs (21 lipid poor adenomas, 15 benign pheocromocytomas, 1 oncocytoma, 12 metastases, 6 primary tumors) showing no fat signal drop on CS were retrospectively included. Manual 3D segmentation on T2-weighted and CS images was performed for subsequent radiomic feature extraction. After feature stability testing and an 80–20% train-test split, the train set was balanced via oversampling. Following a multi-step feature selection, an Extra Trees model was tuned with 5-fold stratified cross-validation in the train set and then tested on the hold-out test set.ResultsA total of 3396 features were extracted from each AL, of which 133 resulted unstable while none had low variance (< 0.01). Highly correlated (r > 0.8) features were also excluded, leaving 440 parameters. Among these, Support Vector Machine 5-fold stratified cross-validated recursive feature elimination selected a subset of 6 features. ML obtained a cross-validation accuracy of 0.94 on the train and 0.91 on the test sets. Precision, recall and F1 score were respectively 0.92, 0.91 and 0.91.ConclusionsOur MRI handcrafted radiomics and ML pipeline proved useful to characterize benign and malignant solid indeterminate adrenal lesions.     

Machine learning-based radiomic,clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma

IntroductionSurvival varies in patients with glioblastoma due to intratumoral heterogeneity and radiomics/imaging biomarkers have potential to demonstrate heterogeneity. The objective was to combine radiomic, semantic and clinical features to improve prediction of overall survival (OS) and O⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylation status from pre-operative MRI in patients with glioblastoma.MethodsA retrospective study of 181 MRI studies (mean age 58 ± 13 years, mean OS 497 ± 354 days) performed in patients with histopathology-proven glioblastoma. Tumour mass, contrast-enhancement and necrosis were segmented from volumetric contrast-enhanced T1-weighted imaging (CE-T1WI). 333 radiomic features were extracted and 16 Visually Accessible Rembrandt Images (VASARI) features were evaluated by two experienced neuroradiologists. Top radiomic, VASARI and clinical features were used to build machine learning models to predict MGMT status, and all features including MGMT status were used to build Cox proportional hazards regression (Cox) and random survival forest (RSF) models for OS prediction.ResultsThe optimal cut-off value for MGMT promoter methylation index was 12.75%; 42 radiomic features exhibited significant differences between high and low-methylation groups. However, model performance accuracy combining radiomic, VASARI and clinical features for MGMT status prediction varied between 45 and 67%. For OS predication, the RSF model based on clinical, VASARI and CE radiomic features achieved the best performance with an average iAUC of 96.2 ± 1.7 and C-index of 90.0 ± 0.3.ConclusionsVASARI features in combination with clinical and radiomic features from the enhancing tumour show promise for predicting OS with a high accuracy in patients with glioblastoma from pre-operative volumetric CE-T1WI.     

APIR4EMC: Autocalibrated parallel imaging reconstruction for extended multi-contrast imaging

PurposeTo improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC).MethodsAPIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment.ResultsCompared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm³ isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results.ConclusionCompared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging.     

In vivo multiparametric magnetic resonance imaging study for differentiating the severity of hepatic warm ischemia-reperfusion injury in a rabbit model

ObjectivesTo assess the value of multiparametric magnetic resonance imaging including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI) and blood oxygen level dependent (BOLD) MRI in differentiating the severity of hepatic warm ischemia-reperfusion injury (WIRI) in a rabbit model.MethodsFifty rabbits were randomly divided into a sham-operation group and four test groups (n = 10 for each group) according to different hepatic warm ischemia times. IVIM, DTI and BOLD MRI were performed on a 3 T MR scanner with 11 b values (0 to 800 s/mm²), 2 b values (0 and 500 s/mm²) on 12 diffusion directions, multiple-echo gradient echo (GRE) sequences (TR/TE, 75/2.57–24.25 ms), respectively. IVIM, DTI and BOLD MRI parameters, hepatic biochemical and histopathological parameters were compared. Pearson and Spearman correlation methods were performed to assess the correlation between these MRI parameters and laboratory parameters. Furthermore, receiver operating characteristic (ROC) curves were compiled to determine diagnostic efficacies.ResultsTrue diffusion (Dslow), pseudodiffusion (Dfast), perfusion fraction (PF), mean diffusivity (MD) significantly decreased, while R2* significantly increased with prolonged warm ischemia times, and significant differences were found in all of biochemical and histopathological parameters (all P < 0.05). Dslow, PF, and R2* correlated significantly with all of biochemical and histopathological parameters (all |r| = 0.381–0.746, all P < 0.05). ROC analysis showed that the area under the ROC curve (AUC) of IVIM across hepatic WIRI groups was the largest among IVIM, DTI and BOLD.ConclusionsMultiparametric MRI may be helpful with characterization of early changes and determination of severity of hepatic WIRI in a rabbit model.     

Quantitative MRI texture analysis in chronic active multiple sclerosis lesions

ObjectiveRecently, there has been an increasing interest in “chronic enlarging” or “chronic active” multiple sclerosis (MS) lesions that are associated with clinical disability. However, investigation of dynamic lesion volume changes requires longitudinal MRI data from two or more time points. The aim of this study was to investigate the application of texture analysis (TA) on baseline T1-weighted 3D magnetization-prepared rapid acquisition gradient-echo (MPRAGE) images to differentiate chronic active from chronic stable MS lesions.Material and methodsTo identify chronic active lesions as compared to non-enhancing stable lesions, two MPRAGE datasets acquired on a 3 T MRI at baseline and after 12 months follow-up were applied to the Voxel-Guided Morphometry (VGM) algorithm. TA was performed on the baseline MPRAGE images, 36 texture features were extracted for each lesion.ResultsOverall, 374 chronic MS lesions (155 chronic active and 219 chronic stable lesions) from 60 MS patients were included in the final analysis. Multiple texture features including “DISCRETIZED_HISTO_Energy”, “GLCM_Energy”, “GLCM_Contrast” and “GLCM_Dissimilarity” were significantly higher in chronic active as compared to chronic stable lesions. Partial least squares regression yielded an area under the curve of 0.7 to differentiate both lesion types.ConclusionOur results suggest that multiple texture features extracted from MPRAGE images indicate higher intralesional heterogeneity, however they demonstrate only a fair accuracy to differentiate chronic active from chronic stable MS lesions.     

Should abbreviated breast MRI be compliant with American College of Radiology requirements for MRI accreditation?

ObjectiveTo evaluate non-inferiority and diagnostic performance of an American College of Radiology compliant abbreviated MRI protocol (AB-MRI) compared with standard-of-care breast MRI (SOC-BMRI) in patients with increased breast cancer risk.Material and methodsWomen with increased lifetime breast cancer risk by American Cancer Society guidelines underwent breast MRI at a single institution between October 2015 and February 2018. AB-MRI was acquired at 3.0 T with T2-weighted extended fast spin echo triple-echo Dixon and pre- and post-contrast 3D dual-echo fast spoiled gradient echo two-point Dixon sequences with an 8-channel breast coil 1–7 days after SOC-BMRI. Three readers independently reviewed AB-MRI and assigned BI-RADS categories for maximum intensity projection images (AB1), dynamic contrast-enhanced (DCE) images (AB2), and DCE and non-contrast T2 and fat-only images (AB3). These scores were compared to those from SOC-BMRI.ResultsCancer yield was 14 per 1000 (women-years) in 73 women aged 26–75 years (mean 53.5 years). AB-MRI acquisition times (mean 9.63 min) and table times (mean 15.07 min) were significantly shorter than those of SOC-BMRI (means 19.46 and 36.3 min, respectively) (p < .001). Accuracy, sensitivity, specificity, and positive and negative predictive values were identical for AB3 and SOC-BMRI (93%, 100%, 93%, 16.7%, and 100%, respectively). AB-MRI with AB1 and AB2 had significantly lower specificity (AB1 = 73.6%, AB2 = 77.8%), positive predictive values (AB1 = 5%, AB2 = 5.9%), and accuracy (AB1 = 74%, AB2 = 78%) than those of SOC-BMRI (p = .002 for AB1, p = .01 for AB2).ConclusionAB-MRI was acquired significantly faster than SOC-BMRI and its diagnostic performance was non-inferior. Inclusion of T2 and fat-only images was necessary to achieve non-inferiority by multireader evaluation.     

Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning

BackgroundMagnetic resonance images with multiple contrasts or sequences are commonly used for segmenting brain tissues, including lesions, in multiple sclerosis (MS). However, acquisition of images with multiple contrasts increases the scan time and complexity of the analysis, possibly introducing factors that could compromise segmentation quality.ObjectiveTo investigate the effect of various combinations of multi-contrast images as input on the segmented volumes of gray (GM) and white matter (WM), cerebrospinal fluid (CSF), and lesions using a deep neural network.MethodsU-net, a fully convolutional neural network was used to automatically segment GM, WM, CSF, and lesions in 1000 MS patients. The input to the network consisted of 15 combinations of FLAIR, T1-, T2-, and proton density-weighted images. The Dice similarity coefficient (DSC) was evaluated to assess the segmentation performance. For lesions, true positive rate (TPR) and false positive rate (FPR) were also evaluated. In addition, the effect of lesion size on lesion segmentation was investigated.ResultsHighest DSC was observed for all the tissue volumes, including lesions, when the input was combination of all four image contrasts. All other input combinations that included FLAIR also provided high DSC for all tissue classes. However, the quality of lesion segmentation showed strong dependence on the input images. The DSC and TPR values for inputs with the four contrast combination and FLAIR alone were very similar, but FLAIR showed a moderately higher FPR for lesion size <100 μl. For lesions smaller than 20 μl all image combinations resulted in poor performance. The segmentation quality improved with lesion size.ConclusionsBest performance for segmented tissue volumes was obtained with all four image contrasts as the input, and comparable performance was attainable with FLAIR only as the input, albeit with a moderate increase in FPR for small lesions. This implies that acquisition of only FLAIR images provides satisfactory tissue segmentation. Lesion segmentation was poor for very small lesions and improved rapidly with lesion size.     

In vivo hypoxia characterization using blood oxygen level dependent magnetic resonance imaging in a preclinical glioblastoma mouse model

PurposeHypoxia measurements can provide crucial information regarding tumor aggressiveness, however current preclinical approaches are limited. Blood oxygen level dependent (BOLD) Magnetic Resonance Imaging (MRI) has the potential to continuously monitor tumor pathophysiology (including hypoxia). The aim of this preliminary work was to develop and evaluate BOLD MRI followed by post-image analysis to identify regions of hypoxia in a murine glioblastoma (GBM) model.MethodsA murine orthotopic GBM model (GL261-luc2) was used and independent images were generated from multiple slices in four different mice. Image slices were randomized and split into training and validation cohorts. A 7 T MRI was used to acquire anatomical images using a fast-spin-echo (FSE) T2-weighted sequence. BOLD images were taken with a T2*-weighted gradient echo (GRE) and an oxygen challenge. Thirteen images were evaluated in a training cohort to develop the MRI sequence and optimize post-image analysis. An in-house MATLAB code was used to evaluate MR images and generate hypoxia maps for a range of thresholding and ΔT2* values, which were compared against respective pimonidazole sections to optimize image processing parameters. The remaining (n = 6) images were used as a validation group. Following imaging, mice were injected with pimonidazole and collected for immunohistochemistry (IHC). A test of correlation (Pearson's coefficient) and agreement (Bland-Altman plot) were conducted to evaluate the respective MRI slices and pimonidazole IHC sections.ResultsFor the training cohort, the optimized parameters of “thresholding” (20 ≤ T2* ≤ 35 ms) and ΔT2* (±4 ms) yielded a Pearson's correlation of 0.697. These parameters were applied to the validation cohort confirming a strong Pearson's correlation (0.749) when comparing the respective analyzed MR and pimonidazole images.ConclusionOur preliminary study supports the hypothesis that BOLD MRI is correlated with pimonidazole measurements of hypoxia in an orthotopic GBM mouse model. This technique has further potential to monitor hypoxia during tumor development and therapy.     

Triple-negative breast cancer: Pretreatment magnetic resonance imaging features and clinicopathological factors associated with recurrence

PurposeWe aimed to investigate the magnetic resonance imaging (MRI) features and clinicopathologic factors with recurrence of triple-negative breast cancer (TNBC).Patients and methodsWe identified 281 patients with 288 surgically confirmed TNBC lesions who underwent pretreatment MRI between 2009 and 2015. The presence of intratumoral high signal on T2-weighted images, high-signal rim on diffusion-weighted images (DWI), and rim enhancement on the dynamic contrast-enhanced MRI and clinicopathological data were collected. Cox proportional analysis was performed.ResultsOf the 288 lesions, 36 (12.5%) recurred after a median follow-up of 18 months (range, 3.6–68.3 months). Rim enhancement (hazard ratio [HR] = 3.15; 95% confidence interval [CI] = 1.01, 9.88; p = .048), and lymphovascular invasion (HR = 2.73, 95% CI = 1.20, 6.23; p = .016) were independently associated with disease recurrence. While fibroglandular volume, background parenchymal enhancement, intratumoral T2 high signal, and high-signal rim on DWI, were not found to be risk factors for recurrence.ConclusionPretreatment MRI features may help predict a high risk of recurrence in patients with TNBC.     

A fast screening protocol for carotid plaques imaging using 3D multi-contrast MRI without contrast agent

PurposeTo implement a fast (~ 15 min) MRI protocol for carotid plaque screening using 3D multi-contrast MRI sequences without contrast agent on a 3 Tesla MRI scanner.Materials and methods7 healthy volunteers and 25 patients with clinically confirmed transient ischemic attack or suspected cerebrovascular ischemia were included in this study. The proposed protocol, including 3D T1-weighted and T2-weighted SPACE (variable-flip-angle 3D turbo spin echo), and T1-weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) was performed first and was followed by 2D T1-weighted and T2-weighted turbo spin echo, and post-contrast T1-weighted SPACE sequences. Image quality, number of plaques, and vessel wall thicknesses measured at the intersection of the plaques were evaluated and compared between sequences.ResultsAverage examination time of the proposed protocol was 14.6 min. The average image quality scores of 3D T1-weighted, T2-weighted SPACE, and T1-weighted magnetization prepared rapid acquisition gradient echo were 3.69, 3.75, and 3.48, respectively. There was no significant difference in detecting the number of plaques and vulnerable plaques using pre-contrast 3D images with or without post-contrast T1-weighted SPACE. The 3D SPACE and 2D turbo spin echo sequences had excellent agreement (R = 0.96 for T1-weighted and 0.98 for T2-weighted, p < 0.001) regarding vessel wall thickness measurements.ConclusionThe proposed protocol demonstrated the feasibility of attaining carotid plaque screening within a 15-minute scan, which provided sufficient anatomical coverage and critical diagnostic information. This protocol offers the potential for rapid and reliable screening for carotid plaques without contrast agent.     

Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques

The aim of this study was to evaluate the contribution of diffusion and perfusion MR metrics in the discrimination of intracranial brain lesions at 3T MRI, and to investigate the potential diagnostic and predictive value that pattern recognition techniques may provide in tumor characterization using these metrics as classification features. Conventional MRI, diffusion weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic-susceptibility contrast imaging (DSCI) were performed on 115 patients with newly diagnosed intracranial tumors (low-and- high grade gliomas, meningiomas, solitary metastases). The Mann–Whitney U test was employed in order to identify statistical differences of the diffusion and perfusion parameters for different tumor comparisons in the intra-and peritumoral region. To assess the diagnostic contribution of these parameters, two different methods were used; the commonly used receiver operating characteristic (ROC) analysis and the more sophisticated SVM classification, and accuracy, sensitivity and specificity levels were obtained for both cases. The combination of all metrics provided the optimum diagnostic outcome. The highest predictive outcome was obtained using the SVM classification, although ROC analysis yielded high accuracies as well. It is evident that DWI/DTI and DSCI are useful techniques for tumor grading. Nevertheless, cellularity and vascularity are factors closely correlated in a non-linear way and thus difficult to evaluate and interpret through conventional methods of analysis. Hence, the combination of diffusion and perfusion metrics into a sophisticated classification scheme may provide the optimum diagnostic outcome. In conclusion, machine learning techniques may be used as an adjunctive diagnostic tool, which can be implemented into the clinical routine to optimize decision making.     

Histogram parameters derived from T1 and T2 weighted images correlate with tumor infiltrating lymphocytes and tumor-stroma ratio in head and neck squamous cell cancer

PurposeThe present study used histogram analysis values derived from T1- and T2- weighted (w) images to elucidate possible associations with Tumor-infiltrating lymphocytes (TIL) and Vimentin expression in head and neck squamous cell cancer (HNSCC).Materials and methodsOverall, 28 patients (n = 8 female patient, 28.6%) with primary HNSCC of different localizations were involved in the study. Magnetic resonance imaging (MRI) was obtained on a 3 T MRI. The images were analyzed with a whole lesion measurement using a histogram approach. TIL- and vimentin-expression was calculated on biopsy samples before any form of treatment.ResultsSeveral T1-derived parameters correlated with the expression of TIL within the stroma compartment: mean (r = 0.42, p = 0.025), p10 (r = 0.50, p = 0.007), p25 (r = 0.42, p = 0.025), median (r = 0.39, p = 0.036), and mode (r = 0.39, p = 0.04). No T2-derived parameter correlated with the TIL within the stroma compartment. Several T2-derived parameters correlated with the expression of TIL within the tumor compartment: mean (r = −0.52, p = 0.004), max (r = −0.43, p = 0.02), p10 (r = −0.38, p = 0.04), p25 (r = −0.53, p = 0.004), p75 (r = −0.52, p = 0.004), p90 (r = −0.48, p = 0.009), median (r = −0.52, p = 0.004), mode (r = −0.40, p = 0.03). Kurtosis derived from T2w images had significant higher values in tumor-rich tumors, compared to stroma-rich tumors, (mean 5.5 ± 0.5 versus 4.2 ± 1.2, p = 0.028).ConclusionsHistogram analysis parameters derived from T1w and T2w images might be able to reflect tumor compartments and TIL expression in HNSCC.     

Effect of hepatic steatosis on native T1 mapping of 3T magnetic resonance imaging in the assessment of T1 values for patients with non-alcoholic fatty liver disease

PurposeThis study investigated whether T1 values in native T1 mapping of 3T magnetic resonance imaging (MRI) of the liver were affected by the fatty component.MethodsThis prospective study involved 340 participants from a population-based cohort study between May 8, 2018 and August 8, 2019. Data obtained included: (1) hepatic stiffness according to magnetic resonance elastography (MRE); (2) T1 value according to T1 mapping; (3) fat fraction and iron concentration from multi-echo Dixon; and (4) clinical indices of hepatic steatosis including body mass index, waist circumference, history of diabetes, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transpeptidase, and triglycerides. The correlations between T1 value and fat fraction, and between T1 value and liver stiffness were assessed using Pearson's correlation coefficient. The independent two-sample t-test was used to evaluate the differences in T1 values according to the presence or absence of hepatic steatosis, and the one-way analysis of variance was used to evaluate the difference in T1 value by grading of hepatic steatosis according to MRI-based proton density fat fraction (PDFF). In addition, univariate and multivariate linear regression analyses were performed to determine whether other variables influenced the T1 value.ResultsT1 value showed a positive correlation with the fat fraction obtained from PDFF (r = 0.615, P < 0.001) and with the liver stiffness obtained from MRE (r = 0.370, P < 0.001). Regardless of the evaluation method, the T1 value was significantly increased in subjects with hepatic steatosis (P < 0.001). When comparing hepatic steatosis grades based on MRI-PDFF, the mean T1 values were significantly different in all grades, and the T1 value tended to increase as the grade increased (P < 0.001, P for trend <0.001). On multiple linear regression analysis, the T1 value was influenced by MRI-PDFF, calculated liver iron concentration, liver stiffness, and serum aspartate aminotransferase level.ConclusionThe T1 value obtained by current T1 mapping of 3T MRI was affected by the liver fat component and several other factors such as liver stiffness, iron concentration, and inflammation.