Coronary Artery Wall Contrast Enhancement Imaging Impact on Disease Activity Assessment in IgG4-RD a direct marker of coronary involvement.

BACKGROUND: Coronary artery wall contrast enhancement (CE) has been applied to non-invasive visualization of changes to the coronary artery wall in systemic lupus erythematosus (SLE). This study investigated the feasibility of quantifying CE to detect coronary involvement in IgG4-related disease (IgG4-RD), as well as the influence on disease activity assessment. METHODS: A total of 93 subjects (31 IgG4-RD; 29 SLE; 33 controls) were recruited in the study. Coronary artery wall imaging was performed in a 3.0T MRI scanner. Serological markers and IgG4-RD Responder Index (IgG4-RD-RI) scores were collected for correlation analysis. RESULTS: Coronary wall CE was observed in 29 (94%) IgG4-RD patients and 22 (76%) SLE patients. Contrast-to-noise ratio (CNR) and total CE area were significantly higher in patient groups compared to controls (CNR: 6.1 ± 2.7 [IgG4-RD] v. 4.2 ± 2.3 [SLE] v. 1.9 ± 1.5 [control], P < 0.001; Total CE area: 3.0 [3.0-6.6] v. 1.7 [1.5-2.6] v. 0.3 [0.3-0.9], P < 0.001). In the IgG4-RD group, CNR and total CE area were correlated with the RI (CNR: r =0.55, P =0.002; total CE area: r = 0.39, P = 0.031). RI´ scored considering coronary involvement by CE, differed significantly from RI scored without consideration of CE (RI v. RI´: 15 ± 6v. 16 ± 6, P < 0.001). CONCLUSIONS: Visualization and quantification of CMR coronary CE by CNR and total CE area could be utilized to detect subclinical and clinical coronary wall involvement, which is prevalent in IgG4-RD. The potential inclusion of small and medium-sized vessel involvements in the assessment of disease activity in IgG4-RD is worthy of further investigation.

Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking.

PURPOSE: To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework. METHODS: A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach. RESULTS: In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time. CONCLUSION: The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.

High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance.

PURPOSE: Majority of men with low-risk prostate cancer can be managed with active surveillance (AS). This study evaluates a high-resolution diffusion-weighted imaging (HR-DWI) technique to predict adverse biopsy histology (AH), defined as Gleason score ≥7 on any biopsy or ≥3 increase in number of positive biopsy cores on systematic biopsies. We test the hypothesis that high-grade disease and progressing disease undergo subtle changes during even short intervals that can be detected by HR-DWI. EXPERIMENTAL DESIGN: In a prospective clinical trial, serial multiparametric MRIs, incorporating HR-DWI and standard DWI (S-DWI) were performed approximately 12 months apart prior to prostate biopsy (n = 59). HR-DWI, which uses reduced field-of-view and motion compensation techniques, was compared with S-DWI. RESULTS: HR-DWI had a 3-fold improvement in spacial resolution compared with S-DWI as confirmed using imaging phantoms. For detecting AH, multiparametric MRI using HR-DWI had a sensitivity of 75% and specificity of 83.9%, and MRI using S-DWI had a sensitivity of 71.4% and specificity of 54.8%. The AUC for HR-DWI was significantly higher (0.794 vs. 0.631, P = 0.014). Secondary analyses of univariable predictors of AH showed tumor size increase [OR 16.8; 95% confidence interval (CI): 4.06-69.48; P < 0.001] and apparent diffusion coefficient (ADC) decrease (OR 5.06; 95% CI: 1.39-18.38; P = 0.014) on HR-DWI were significant predictors of AH. CONCLUSION: HR-DWI outperforms S-DWI in predicting AH. Patient with AH have tumors that change in size and ADC that could be detected using HR-DWI. Future studies with longer follow-up should assess HR-DWI for predicting disease progression during AS. SIGNIFICANCE: We report on a prospective clinical trial using a MRI that has three times the resolution of standard MRI. During AS for prostate cancer, two high-resolution MRIs performed approximately a year apart can detect tumor changes that predict the presence of aggressive cancers that should be considered for curative therapy such as prostatectomy or radiation.

Perioperative chemotherapy with docetaxel plus oxaliplatin and S-1 (DOS) versus oxaliplatin plus S-1 (SOX) for the treatment of locally advanced gastric or gastro-esophageal junction adenocarcinoma (MATCH): an open-label, randomized, phase 2 clinical trial.

BACKGROUND: It remains unclear whether addition of docetaxel to the combination of a platinum and fluoropyrimidine could provide more clinical benefits than doublet chemotherapies in the perioperative treatment for locally advanced gastric/gastro-esophageal junction (LAG/GEJ) cancer in Asia. In this randomized, phase 2 study, we assessed the efficacy and safety of perioperative docetaxel plus oxaliplatin and S-1 (DOS) versus oxaliplatin plus S-1 (SOX) in LAG/GEJ adenocarcinoma patients. METHODS: Patients with cT3-4 Nany M0 G/GEJ adenocarcinoma were randomized (1:1) to receive 4 cycles of preoperative DOS or SOX followed by D2 gastrectomy and another 4 cycles of postoperative chemotherapy. The primary endpoint was major pathological response (MPR). RESULTS: From Aug, 2015 to Dec, 2019,154 patients were enrolled and 147 patients included in final analysis, with a median age of 60 (26-73) years. DOS resulted in significantly higher MPR (25.4 vs. 11.8%, P = 0.04). R0 resection rate, the 3-year PFS and 3-year OS rates were 78.9 vs. 61.8% (P = 0.02), 52.3 vs. 35% (HR 0.667, 95% CI: 0.432-1.029, Log rank P = 0.07) and 57.5 vs. 49.2% (HR 0.685, 95% CI: 0.429-1.095, Log rank P = 0.11) in the DOS and SOX groups, respectively. Patients who acquired MPR experienced significantly better survival. DOS had similar tolerance to SOX. CONCLUSIONS: Perioperative DOS improved MPR significantly and tended to produce longer PFS compared to SOX in LAG/GEJ cancer in Asia, and might be considered as a preferred option for perioperative chemotherapy and worth further investigation.

Rapid three-dimensional quantification of high-intensity plaques from coronary atherosclerosis T1-weighted characterization to predict periprocedural myocardial injury.

BACKGROUND: High-intensity plaque (HIP) on magnetic resonance imaging (MRI) has been documented as a powerful predictor of periprocedural myocardial injury (PMI) following percutaneous coronary intervention (PCI). Despite the recent proposal of three-dimensional HIP quantification to enhance the predictive capability, the conventional pulse sequence, which necessitates the separate acquisition of anatomical reference images, hinders accurate three-dimensional segmentation along the coronary vasculature. Coronary atherosclerosis T1-weighted characterization (CATCH) enables the simultaneous acquisition of inherently coregistered dark-blood plaque and bright-blood coronary artery images. We aimed to develop a novel HIP quantification approach using CATCH and to ascertain its superior predictive performance compared to the conventional two-dimensional assessment based on plaque-to-myocardium signal intensity ratio (PMR). METHODS: In this prospective study, CATCH MRI was conducted before elective stent implantation in 137 lesions from 125 patients. On CATCH images, dedicated software automatically generated tubular three-dimensional volumes of interest on the dark-blood plaque images along the coronary vasculature, based on the precisely matched bright-blood coronary artery images, and subsequently computed PMR and HIP volume (HIPvol). Specifically, HIPvol was calculated as the volume of voxels with signal intensity exceeding that of the myocardium, weighted by their respective signal intensities. PMI was defined as post-PCI cardiac troponin-T > 5 × the upper reference limit. RESULTS: The entire analysis process was completed within 3 min per lesion. PMI occurred in 44 lesions. Based on the receiver operating characteristic curve analysis, HIPvol outperformed PMR for predicting PMI (C-statistics, 0.870 [95% CI, 0.805-0.936] vs. 0.787 [95% CI, 0.706-0.868]; p = 0.001). This result was primarily driven by the higher sensitivity HIPvol offered: 0.886 (95% CI, 0.754-0.962) vs. 0.750 for PMR (95% CI, 0.597-0.868; p = 0.034). Multivariable analysis identified HIPvol as an independent predictor of PMI (odds ratio, 1.15 per 10-µL increase; 95% CI, 1.01-1.30, p = 0.035). CONCLUSIONS: Our semi-automated method of analyzing coronary plaque using CATCH MRI provided rapid HIP quantification. Three-dimensional assessment using this approach had a better ability to predict PMI than conventional two-dimensional assessment.

Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning.

Purpose: To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images. Methods: Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis. Results: The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010). Conclusion: A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.

An improvement in the reconstruction of digestive tract after total gastrectomy: ultra-short cecum.

Aim: This study aimed to evaluate the utility and complications of ultra-short cecum (USC) in the reconstruction of digestive tract after total gastrectomy (TG) for the alleviation of reflux esophagitis and to determine its effect on long-term nutritional status. Methods: Patients who underwent TG with USC or normal cecum (NC) at a single institution between June 2018 and December 2020 were included in this study. The inclusion and exclusion criteria were defined, and the primary endpoints were reflux esophagitis, anastomotic leakage and postoperative nutritional status. The long-term nutritional status was evaluated by the change trend of laboratory blood tests, including total protein, prealbumin, hemoglobin, and total leukocytes. Results: Totally 240 cases were included in the final analysis out of 496 patients who received TG with USC or NC. Postoperative reflux esophagitis was significantly higher in the NC group than in the USC group (24.7% versus 7.7%, P = 0.001), and the NC group had a higher incidence of severe esophagitis symptoms compared to the USC group (13.6% versus 0.00%, P < 0.001), and the incidence of anastomotic leakage in the USC group was similar to that in the NC group (9.0% versus 6.2%, P = 0.6). There was no significant difference in long-term nutritional status between the USC and NC groups in the two years following the surgery (P > 0.05). Conclusion: Ultra-short cecum after total gastrectomy should be more actively recommended due to its significant reduction in reflux esophagitis and similar incidence of anastomotic leakage and nutritional status compared with normal cecum after total gastrectomy.

Retrospective quantification of clinical abdominal DCE-MRI using pharmacokinetics-informed deep learning: a proof-of-concept study.

Introduction: Dynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated. Method: Forty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well. Results: The pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant Ktrans, fractional extravascular extracellular volume ve, and rate constant kep) achieved intraclass correlation coefficient and R2 between 0.84-0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas. Discussion: Retrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer.

Physics-informed deep learning for T2-deblurred superresolution turbo spin echo MRI.

PURPOSE: Deep learning superresolution (SR) is a promising approach to reduce MRI scan time without requiring custom sequences or iterative reconstruction. Previous deep learning SR approaches have generated low-resolution training images by simple k-space truncation, but this does not properly model in-plane turbo spin echo (TSE) MRI resolution degradation, which has variable T2 relaxation effects in different k-space regions. To fill this gap, we developed a T2 -deblurred deep learning SR method for the SR of 3D-TSE images. METHODS: A SR generative adversarial network was trained using physically realistic resolution degradation (asymmetric T2 weighting of raw high-resolution k-space data). For comparison, we trained the same network structure on previous degradation models without TSE physics modeling. We tested all models for both retrospective and prospective SR with 3 × 3 acceleration factor (in the two phase-encoding directions) of genetically engineered mouse embryo model TSE-MR images. RESULTS: The proposed method can produce high-quality 3 × 3 SR images for a typical 500-slice volume with 6-7 mouse embryos. Because 3 × 3 SR was performed, the image acquisition time can be reduced from 15 h to 1.7 h. Compared to previous SR methods without TSE modeling, the proposed method achieved the best quantitative imaging metrics for both retrospective and prospective evaluations and achieved the best imaging-quality expert scores for prospective evaluation. CONCLUSION: The proposed T2 -deblurring method improved accuracy and image quality of deep learning-based SR of TSE MRI. This method has the potential to accelerate TSE image acquisition by a factor of up to 9.

Identification of novel serum autoantibody biomarkers for early esophageal squamous cell carcinoma and high-grade intraepithelial neoplasia detection.

Background: Early diagnosis of esophageal squamous cell carcinoma (ESCC) is critical for effective treatment and optimal prognosis; however, less study on serum biomarkers for the early ESCC detection has been reported. The aim of this study was to identify and evaluate several serum autoantibody biomarkers in early ESCC. Methods: We initially screened candidate tumor-associated autoantibodies (TAAbs) associated with ESCC by serological proteome analysis (SERPA) combined with nanoliter-liquid chromatography combined with quadrupole time of flight tandem mass spectrometry (nano-LC-Q-TOF-MS/MS), and the TAAbs were further subjected to analysis by Enzyme-linked immunosorbent assay (ELISA) in a clinical cohort (386 participants, including 161 patients with ESCC, 49 patients with high-grade intraepithelial neoplasia [HGIN] and 176 healthy controls [HC]). Receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic performance. Results: The serum levels of CETN2 and POFUT1 autoantibodies which were identified by SERPA were statistically different between ESCC or HGIN patients and HC in ELISA analysis with the area under the curve (AUC) values of 0.709 (95%CI: 0.654-0.764) and 0.741 (95%CI: 0.689-0.793), 0.717 (95%CI: 0.634-0.800) and 0.703 (95%CI: 0.627-0.779) for detection of ESCC and HGIN, respectively. Combining these two markers, the AUCs were 0.781 (95%CI: 0.733-0.829), 0.754 (95%CI: 0.694-0.814) and 0.756 (95%CI: 0.686-0.827) when distinguishing ESCC, early ESCC and HGIN from HC, respectively. Meanwhile, the expression of CETN2 and POFUT1 was found to be correlated with ESCC progression. Conclusions: Our data suggest that CETN2 and POFUT1 autoantibodies have potential diagnostic value for ESCC and HGIN, which may provide novel insights for early ESCC and precancerous lesions detection.

Direct synthesis of multi-contrast brain MR images from MR multitasking spatial factors using deep learning.

PURPOSE: To develop a deep learning method to synthesize conventional contrast-weighted images in the brain from MR multitasking spatial factors. METHODS: Eighteen subjects were imaged using a whole-brain quantitative T1 -T2 -T1ρ MR multitasking sequence. Conventional contrast-weighted images consisting of T1 MPRAGE, T1 gradient echo, and T2 fluid-attenuated inversion recovery were acquired as target images. A 2D U-Net-based neural network was trained to synthesize conventional weighted images from MR multitasking spatial factors. Quantitative assessment and image quality rating by two radiologists were performed to evaluate the quality of deep-learning-based synthesis, in comparison with Bloch-equation-based synthesis from MR multitasking quantitative maps. RESULTS: The deep-learning synthetic images showed comparable contrasts of brain tissues with the reference images from true acquisitions and were substantially better than the Bloch-equation-based synthesis results. Averaging on the three contrasts, the deep learning synthesis achieved normalized root mean square error = 0.184 ± 0.075, peak SNR = 28.14 ± 2.51, and structural-similarity index = 0.918 ± 0.034, which were significantly better than Bloch-equation-based synthesis (p < 0.05). Radiologists' rating results show that compared with true acquisitions, deep learning synthesis had no notable quality degradation and was better than Bloch-equation-based synthesis. CONCLUSION: A deep learning technique was developed to synthesize conventional weighted images from MR multitasking spatial factors in the brain, enabling the simultaneous acquisition of multiparametric quantitative maps and clinical contrast-weighted images in a single scan.

IGFBP-3 promotes cachexia-associated lipid loss by suppressing insulin-like growth factor/insulin signaling.

BACKGROUND: Progressive lipid loss of adipose tissue is a major feature of cancer-associated cachexia. In addition to systemic immune/inflammatory effects in response to tumor progression, tumor-secreted cachectic ligands also play essential roles in tumor-induced lipid loss. However, the mechanisms of tumor-adipose tissue interaction in lipid homeostasis are not fully understood. METHODS: The yki -gut tumors were induced in fruit flies. Lipid metabolic assays were performed to investigate the lipolysis level of different types of insulin-like growth factor binding protein-3 (IGFBP-3) treated cells. Immunoblotting was used to display phenotypes of tumor cells and adipocytes. Quantitative polymerase chain reaction (qPCR) analysis was carried out to examine the gene expression levels such as Acc1 , Acly , and Fasn et al . RESULTS: In this study, it was revealed that tumor-derived IGFBP-3 was an important ligand directly causing lipid loss in matured adipocytes. IGFBP-3, which is highly expressed in cachectic tumor cells, antagonized insulin/IGF-like signaling (IIS) and impaired the balance between lipolysis and lipogenesis in 3T3-L1 adipocytes. Conditioned medium from cachectic tumor cells, such as Capan-1 and C26 cells, contained excessive IGFBP-3 that potently induced lipolysis in adipocytes. Notably, neutralization of IGFBP-3 by neutralizing antibody in the conditioned medium of cachectic tumor cells significantly alleviated the lipolytic effect and restored lipid storage in adipocytes. Furthermore, cachectic tumor cells were resistant to IGFBP-3 inhibition of IIS, ensuring their escape from IGFBP-3-associated growth suppression. Finally, cachectic tumor-derived ImpL2, the IGFBP-3 homolog, also impaired lipid homeostasis of host cells in an established cancer-cachexia model in Drosophila . Most importantly, IGFBP-3 was highly expressed in cancer tissues in pancreatic and colorectal cancer patients, especially higher in the sera of cachectic cancer patients than non-cachexia cancer patients. CONCLUSION: Our study demonstrates that tumor-derived IGFBP-3 plays a critical role in cachexia-associated lipid loss and could be a biomarker for diagnosis of cachexia in cancer patients.

Identification and evaluation of novel serum autoantibody biomarkers for early diagnosis of gastric cancer and precancerous lesion.

PURPOSE: Early diagnosis is crucial for optimal prognosis of gastric cancer (GC). Hereby, we aimed to identify novel serum autoantibody-based biomarkers for precancerous lesion (PL) and early GC. METHODS: We performed serological proteome analysis (SERPA) combined with nanoliter-liquid chromatography combined with quadrupole time of flight tandem mass spectrometry (Nano-LC-Q-TOF-MS/MS) to screen for GC-associated autoantibodies. The identified autoantibodies were analyzed for potential detection value for PL and GC by enzyme-linked immunosorbent assay (ELISA). Receiver operating characteristic (ROC) curves analysis was conducted to evaluate the accuracy of the biomarkers. RESULTS: We identified seven candidates, such as mRNA export factor (RAE1), Nucleophosmin 1 (NPM1), phosphoglycerate kinase 1 (PGK1), and ADP-ribosylation factor 4 (ARF4). Antibodies against all seven proteins were present at higher levels in sera from 242 patients (51 PL, 78 early GC, 113 advanced GC) compared with sera from 122 healthy individuals. RAE1-specific autoantibody discriminated best between patients at different GC stages, with area under the curve (AUC) values of 0.710, 0.745, and 0.804 for PL, early GC, and advanced GC, respectively. Two predictive models composed of gender, RAE1, PGK1, NPM1, and ARF4 autoantibodies (Model 2 for PL) and of age, gender, RAE1, PGK1, and NPM1 autoantibodies (Model 3 for early GC) had improved diagnostic efficiencies, with AUCs of 0.803 and 0.857, sensitivities of 66.7% and 75.6%, and specificities of 78.7% and 87.7%, respectively. CONCLUSION: The identified serum tumor-associated autoantibodies (TAAbs) may have good potential for early detection of GC and PL.

Identification of critical prognosis signature associated with lymph node metastasis of stomach adenocarcinomas.

Lymph node metastasis (LNM) is an important factor affecting the prognosis of patients with gastric adenocarcinoma (STAD), which is the most common malignancy of the human digestive system. Current detection techniques have limited sensitivity and specificity, and there is a lack of effective biomarkers to screen for LNM. Therefore, it is critical to screen for biomarkers that predict LNM in STAD. Gene expression differential analysis (false discovery rate < 0.05, |log2Fold change| ≥1.5) was performed on 102 LNM samples, 224 non-LNM samples, and 29 normal gastric tissue samples from The Cancer Genome Atlas (TCGA) STAD dataset, and 269 LNM-specific genes (DEGs) were obtained. Enrichment analysis showed that LNM-specific genes functioned mainly in cytokine-cytokine receptor interactions, calcium signaling, and other pathways. Ten DEGs significantly associated with overall survival in STAD patients were screened by multivariate Cox regression, and an LNM-based 10-mRNA prognostic signature was established (Logrank P < 0.0001). This 10-mRNA signature was well predicted in both the TCGA training set and the Gene Expression Omnibus validation dataset (GSE84437) and was associated with survival in patients with LNM or advanced-stage STAD. Using Kaplan-Meier survival, receiver operating characteristic curve, C-index analysis, and decision curve analysis, the 10-mRNA signature was found to be a more effective predictor of prognosis in STAD patients than the other two reported models (P < 0.0005). Protein-protein interaction network and gene set enrichment analysis of the 10-mRNA signature revealed that the signature may affect the expression of multiple biological pathways and related genes. Finally, the expression levels of prognostic genes in STAD tissues and cell lines were verified using qRT-PCR, Western blot, and the Human Protein Atlas database. Taken together, the prognostic signature constructed in this study may become an indicator for clinical prognostic assessment of LNM-STAD and provide a new strategy for future targeted therapy.

Relationship between coronary high-intensity plaques on T1-weighted imaging by cardiovascular magnetic resonance and vulnerable plaque features by near-infrared spectroscopy and intravascular ultrasound: a prospective cohort study.

BACKGROUND: This study aimed to compare the coronary plaque characterization by cardiovascular magnetic resonance (CMR) and near-infrared spectroscopy (NIRS)-intravascular ultrasound (IVUS) (NIRS-IVUS), and to determine whether pre-percutaneous coronary intervention (PCI) evaluation using CMR identifies high-intensity plaques (HIPs) at risk of peri-procedural myocardial infarction (pMI). Although there is little evidence in comparison with NIRS-IVUS findings, which have recently been shown to identify vulnerable plaques, we inferred that CMR-derived HIPs would be associated with vulnerable plaque features identified on NIRS-IVUS. METHODS: 52 patients with stable coronary artery disease who underwent CMR with non-contrast T1-weighted imaging and PCI using NIRS-IVUS were studied. HIP was defined as a signal intensity of the coronary plaque-to-myocardial signal intensity ratio (PMR) ≥ 1.4, which was measured from the data of CMR images. We evaluated whether HIPs were associated with the NIRS-derived maximum 4-mm lipid-core burden index (maxLCBI4mm) and plaque morphology on IVUS, and assessed the incidence and predictor of pMI defined by the current Universal Definition using high-sensitive cardiac troponin-T. RESULTS: Of 62 lesions, HIPs were observed in 30 lesions (48%). The HIP group had a significantly higher remodeling index, plaque burden, and proportion of echo-lucent plaque and maxLCBI4mm ≥ 400 (known as large lipid-rich plaque [LRP]) than the non-HIP group. The correlation between the maxLCBI4mm and PMR was significantly positive (r = 0.51). In multivariable logistic regression analysis for prediction of HIP, NIRS-derived large LRP (odds ratio [OR] = 5.41; 95% confidence intervals [CIs] 1.65-17.8, p = 0.005) and IVUS-derived echo-lucent plaque (OR = 5.12; 95% CIs 1.11-23.6, p = 0.036) were strong independent predictors. Furthermore, pMI occurred in 14 of 30 lesions (47%) with HIP, compared to only 5 of 32 lesions (16%) without HIP (p = 0.005). In multivariable logistic regression analysis for prediction of incidence of pMI, CMR-derived HIP (OR = 5.68; 95% CIs 1.53-21.1, p = 0.009) was a strong independent predictor, but not NIRS-derived large LRP and IVUS-derived echo-lucent plaque. CONCLUSIONS: There is an important relationship between CMR-derived HIP and NIRS-derived large LRP. We also confirmed that non-contrast T1-weighted CMR imaging is useful for characterization of vulnerable plaque features as well as for pre-PCI risk stratification. Trial registration The ethics committee of Juntendo Clinical Research and Trial Center approved this study on January 26, 2021 (Reference Number 20-313).

Free-breathing 3D CEST MRI of human liver at 3.0 T.

PURPOSE: To develop a novel 3D abdominal CEST MRI technique at 3 T using MR multitasking, which enables entire-liver coverage with free-breathing acquisition. METHODS: k-Space data were continuously acquired with repetitive steady-state CEST (ss-CEST) modules. The stack-of-stars acquisition pattern was used for k-space sampling. MR multitasking was used to reconstruct motion-resolved 3D CEST images of 53 frequency offsets with entire-liver coverage and 2.0 × 2.0 × 6.0 mm3 spatial resolution. The total scan time was 9 min. The sensitivity of amide proton transfer (APT)-CEST (magnetization transfer asymmetry [MTRasym ] at 3.5 ppm) and glycogen CEST (glycoCEST) (mean MTRasym around 1.0 ppm) signals generated with the proposed method were tested with fasting experiments. RESULTS: Both APT-CEST and glycoCEST signals showed high sensitivity between post-fasting and post-meal acquisitions. APT-CEST and glycoCEST MTRasym signals from post-mean scans were significantly increased (APT-CEST: -0.019 ± 0.017 in post-fasting scans, 0.014 ± 0.021 in post-meal scans, p < 0.01; glycoCEST: 0.003 ± 0.009 in post-fasting scans, 0.027 ± 0.021 in post-meal scans, p < 0.01). CONCLUSION: The proposed 3D abdominal steady-state CEST method using MR multitasking can generate CEST images of the entire liver during free breathing.

MR Multitasking-based multi-dimensional assessment of cardiovascular system (MT-MACS) with extended spatial coverage and water-fat separation.

PURPOSE: To extend the MR MultiTasking-based Multidimensional Assessment of Cardiovascular System (MT-MACS) technique with larger spatial coverage and water-fat separation for comprehensive aortocardiac assessment. METHODS: MT-MACS adopts a low-rank tensor image model for 7D imaging, with three spatial dimensions for volumetric imaging, one cardiac motion dimension for cine imaging, one respiratory motion dimension for free-breathing imaging, one T2-prepared inversion recovery time dimension for multi-contrast assessment, and one T2*-decay time dimension for water-fat separation. Nine healthy subjects were recruited for the 3T study. Overall image quality was scored on bright-blood (BB), dark-blood (DB), and gray-blood (GB) contrasts using a 4-point scale (0-poor to 3-excellent) by two independent readers, and their interreader agreement was evaluated. Myocardial wall thickness and left ventricular ejection fraction (LVEF) were quantified on DB and BB contrasts, respectively. The agreement in these metrics between MT-MACS and conventional breath-held, electrocardiography-triggered 2D sequences were evaluated. RESULTS: MT-MACS provides both water-only and fat-only images with excellent image quality (average score = 3.725/3.780/3.835/3.890 for BB/DB/GB/fat-only images) and moderate to high interreader agreement (weighted Cohen's kappa value = 0.727/0.668/1.000/1.000 for BB/DB/GB/fat-only images). There were good to excellent agreements in myocardial wall thickness measurements (intraclass correlation coefficients [ICC] = 0.781/0.929/0.680/0.878 for left atria/left ventricle/right atria/right ventricle) and LVEF quantification (ICC = 0.716) between MT-MACS and 2D references. All measurements were within the literature range of healthy subjects. CONCLUSION: The refined MT-MACS technique provides multi-contrast, phase-resolved, and water-fat imaging of the aortocardiac systems and allows evaluation of anatomy and function. Clinical validation is warranted.

MR multitasking-based dynamic imaging for cerebrovascular evaluation (MT-DICE): Simultaneous quantification of permeability and leakage-insensitive perfusion by dynamic T 1 / T 2 * mapping.

PURPOSE: To develop an MR multitasking-based dynamic imaging for cerebrovascular evaluation (MT-DICE) technique for simultaneous quantification of permeability and leakage-insensitive perfusion with a single-dose contrast injection. METHODS: MT-DICE builds on a saturation-recovery prepared multi-echo fast low-angle shot sequence. The k-space is randomly sampled for 7.6 min, with single-dose contrast agent injected 1.5 min into the scan. MR multitasking is used to model the data into six dimensions, including three spatial dimensions for whole-brain coverage, a saturation-recovery time dimension, and a TE dimension for dynamic T 1 $$ {\mathrm{T}}_1 $$ and T 2 * $$ {\mathrm{T}}_2^{\ast } $$ quantification, respectively, and a contrast dynamics dimension for capturing contrast kinetics. The derived pixel-wise T 1 / T 2 * $$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ time series are converted into contrast concentration-time curves for calculation of kinetic metrics. The technique was assessed for its agreement with reference methods in T 1 $$ {\mathrm{T}}_1 $$ and T 2 * $$ {\mathrm{T}}_2^{\ast } $$ measurements in eight healthy subjects and, in three of them, inter-session repeatability of permeability and leakage-insensitive perfusion parameters. Its feasibility was also demonstrated in four patients with brain tumors. RESULTS: MT-DICE T 1 / T 2 * $$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ values of normal gray matter and white matter were in excellent agreement with reference values (intraclass correlation coefficients = 0.860/0.962 for gray matter and 0.925/0.975 for white matter ). Both permeability and perfusion parameters demonstrated good to excellent intersession agreement with the lowest intraclass correlation coefficients at 0.694. Contrast kinetic parameters in all healthy subjects and patients were within the literature range. CONCLUSION: Based on dynamic T 1 / T 2 * $$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ mapping, MT-DICE allows for simultaneous quantification of permeability and leakage-insensitive perfusion metrics with a single-dose contrast injection.