Echo time optimization for in-vivo measurement of unsaturated lipid resonances using J-difference-edited MRS.

PURPOSE: Measuring lipid composition provides more information than just total lipid content. Hence, the non-invasive measurement of unsaturated lipid protons with both high efficiency and precision is of pressing need. This study was to optimize echo time (TE) for the best resolving of J-difference editing of unsaturated lipid resonances. METHODS: The TE dependence of J-difference-edited (JDE) MRS was verified in the density-matrix simulation, soybean oil phantom, in-vivo experiments of white adipose tissue (WAT), and skeletal muscles using single-voxel MEGA-PRESS sequence at 3T. The peak SNRs and Cramér-Rao lower bounds (CRLBs) acquired at the proposed TE of 45 ms and previously published TE of 70 ms were compared (eight pairs) in WAT, extramyocelluar lipids (EMCLs), and intramyocellular lipids (IMCLs). The lipid composition in skeletal muscles was compared between healthy males (n = 7) and females (n = 7). RESULTS: The optimal TE was suggested as 45 ms. Compared to 70 ms, the mean signal gains at TE of 45 ms were 151% in WAT, 168% in EMCL, 204% in IMCL for allylic resonance, and 52% in EMCL for diallylic resonance. CRLBs were significantly reduced at TE of 45 ms in WAT, EMCL, IMCL for allylic resonance and in EMCL for diallylic resonance. With TE of 45 ms, significant gender differences were found in the lipid composition in EMCL pools, while no difference in IMCL pools. CONCLUSION: The JDE-MRS protocol with TE of 45 ms allows improved quantification of unsaturated lipid resonances in vivo and future lipid metabolism investigations.

Automated Measurement of Pancreatic Fat Deposition on Dixon MRI Using nnU-Net.

BACKGROUND: Pancreatic fat accumulation may cause or aggravate the process of acute pancreatitis, ß-cell dysfunction, T2DM disease, and even be associated with pancreatic tumors. The pathophysiology of fatty pancreas remains overlooked and lacks effective imaging diagnostics. PURPOSE: To automatically measure the distribution of pancreatic fat deposition on Dixon MRI in multicenter/population datasets using nnU-Net models. STUDY TYPE: Retrospective. POPULATION: A total of 176 obese/nonobese subjects (90 males, 86 females; mean age, 27.2 ± 19.7) were enrolled, including a training set (N = 132) and a testing set (N = 44). FIELD STRENGTH/SEQUENCE: A 3 T and 1.5 T/gradient echo T1 dual-echo Dixon. ASSESSMENT: The segmentation results of four types of nnU-Net models were compared using dice similarity coefficient (DSC), positive predicted value (PPV), and sensitivity. The ground truth was the manual delineation by two radiologists according to in-phase (IP) and opposed-phase (OP) images. STATISTICAL TESTS: The group difference of segmentation results of four models were assessed by the Kruskal-Wallis H test with Dunn-Bonferroni comparisons. The interobserver agreement of pancreatic fat fraction measurements across three observers and test-retest reliability of human and machine were assessed by intragroup correlation coefficient (ICC). P < 0.05 was considered statistically significant. RESULTS: The three-dimensional (3D) dual-contrast model had significantly improved performance than 2D dual-contrast (DSC/sensitivity) and 3D one-contrast (IP) models (DSC/PPV/sensitivity) and had less errors than 3D one-contrast (OP) model according to higher DSC and PPV (not significant), with a mean DSC of 0.9158, PPV of 0.9105 and sensitivity of 0.9232 in the testing set. The test-retest ICC of this model was above 0.900 in all pancreatic regions, exceeded human. DATA CONCLUSION: 3D Dual-contrast nnU-Net aided segmentation of pancreas on Dixon images appears to be adaptable to multicenter/population datasets. It fully automates the assessment of pancreatic fat distribution and has high reliability. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Mice harboring a R133L heterozygous mutation in LMNA exhibited ectopic lipid accumulation, aging, and mitochondrial dysfunction in adipose tissue.

The LMNA gene encodes for the nuclear envelope proteins lamin A and C (lamin A/C). A novel R133L heterozygous mutation in the LMNA gene causes atypical progeria syndrome (APS). However, the underlying mechanism remains unclear. Here, we used transgenic mice (LmnaR133L/+ mice) that expressed a heterozygous LMNA R133L mutation and 3T3-L1 cell lines with stable overexpression of LMNA R133L (by lentiviral transduction) as in vivo and in vitro models to investigate the mechanisms of LMNA R133L mutations that mediate the APS phenotype. We found that a heterozygous R133L mutation in LMNA induced most of the metabolic disturbances seen in patients with this mutation, including ectopic lipid accumulation, limited subcutaneous adipose tissue (SAT) expansion, and insulin resistance. Mitochondrial dysfunction and senescence promote ectopic lipid accumulation and insulin resistance. In addition, the FLAG-mediated pull-down capture followed by mass spectrometry assay showed that p160 Myb-binding protein (P160 MBP; Mybbp1 a $$ a $$ ), the critical transcriptional repressor of PGC-1α, was bound to lamin A/C. Increased Mybbp1 a $$ a $$ levels in tissues and greater Mybbp1 a $$ a $$ -lamin A/C binding in nuclear inhibit PGC-1α activity and promotes mitochondrial dysfunction. Our findings confirm that the novel R133L heterozygous mutation in the LMNA gene caused APS are associated with marked mitochondrial respiratory chain impairment, which were induced by decreased PGC-1α levels correlating with increased Mybbp1a levels in nuclear, and a senescence phenotype of the subcutaneous fat.

An artificial intelligence model to predict survival and chemotherapy benefits for gastric cancer patients after gastrectomy development and validation in international multicenter cohorts.

BACKGROUND: Gastric cancer (GC) is a major health problem worldwide, with high prevalence and mortality. The present GC staging system provides inadequate prognostic information and does not reflect the chemotherapy benefit of GC. METHODS: Two hundred fifty-five patients who underwent surgical resection were enrolled in our study (training cohort = 212, internal validation cohort = 43). Nine clinicopathologic features were obtained to construct an support vector machine (SVM) model. The cohorts from 4 domestic centres and The Cancer Genome Atlas (TCGA) were used for external validation. RESULTS: In the training cohort, the AUCs were 0.773 (95% CI 0.708-0.838) for 5-year overall survival (OS) and 0.751 (95% CI 0.683-0.820) for 5-year disease-free survival (DFS); in the domestic validation cohort, the AUCs were 0.852 (95% CI 0.810-0.894) and 0.837 (95% CI 0.792-0.882), respectively. The model performed better than the TNM staging system according to the receiver operator characteristic(ROC) curve. GC patients were significantly divided into low, moderate and high risk based on the SVM. High-risk TNM stage Ⅱ and Ⅲ patients were more likely to benefit from adjuvant chemotherapy than low-risk patients. CONCLUSIONS: The SVM-based model may be used to predict OS and DFS in GC patients and the benefit of adjuvant chemotherapy in TNM stage Ⅱ and Ⅲ GC patients.

Contrast-Enhanced Computed Tomography-Based Radiogenomics Analysis for Predicting Prognosis in Gastric Cancer.

Objective: The aim of this study is to identify prognostic imaging biomarkers and create a radiogenomics nomogram to predict overall survival (OS) in gastric cancer (GC). Material: RNA sequencing data from 407 patients with GC and contrast-enhanced computed tomography (CECT) imaging data from 46 patients obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were utilized to identify radiogenomics biomarkers. A total of 392 patients with CECT images from the Nanfang Hospital database were obtained to create and validate a radiogenomics nomogram based on the biomarkers. Methods: The prognostic imaging features that correlated with the prognostic gene modules (selected by weighted gene coexpression network analysis) were identified as imaging biomarkers. A nomogram that integrated the radiomics score and clinicopathological factors was created and validated in the Nanfang Hospital database. Nomogram discrimination, calibration, and clinical usefulness were evaluated. Results: Three prognostic imaging biomarkers were identified and had a strong correlation with four prognostic gene modules (P < 0.05, FDR < 0.05). The radiogenomics nomogram (AUC = 0.838) resulted in better performance of the survival prediction than that of the TNM staging system (AUC = 0.765, P = 0.011; Delong et al.). In addition, the radiogenomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. Conclusions: The novel prognostic radiogenomics nomogram that was constructed achieved excellent correlation with prognosis in both the training and validation cohort of Nanfang Hospital patients with GC. It is anticipated that this work may assist in clinical preferential treatment decisions and promote the process of precision theranostics in the future.

A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine.

BACKGROUND: Recent studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in the induction of cancer through epigenetic regulation, transcriptional regulation, post-transcriptional regulation and other aspects, thus participating in various biological processes such as cell proliferation, differentiation and apoptosis. As a new nova of anti-tumor therapy, immunotherapy has been shown to be effective in many tumors of which PD-1/PD-L1 monoclonal antibodies has been proofed to increase overall survival rate in advanced gastric cancer (GC). Microsatellite instability (MSI) was known as a biomarker of response to PD-1/PD-L1 monoclonal antibodies therapy. The aim of this study was to identify lncRNAs signatures able to classify MSI status and create a predictive model associated with MSI for GC patients. METHODS: Using the data of Stomach adenocarcinoma from The Cancer Genome Atlas (TCGA), we developed and validated a lncRNAs model for automatic MSI classification using a machine learning technology - support vector machine (SVM). The C-index was adopted to evaluate its accuracy. The prognostic values of overall survival (OS) and disease-free survival (DFS) were also assessed in this model. RESULTS: Using the SVM, a lncRNAs model was established consisting of 16 lncRNA features. In the training cohort with 94 GC patients, accuracy was confirmed with AUC 0.976 (95% CI, 0.952 to 0.999). Veracity was also confirmed in the validation cohort (40 GC patients) with AUC 0.950 (0.889 to 0.999). High predicted score was correlated with better DFS in the patients with stage I-III and lower OS with stage I-IV. CONCLUSION: This study identify 16 LncRNAs signatures able to classify MSI status. The correlation between lncRNAs and MSI status indicates the potential roles of lncRNAs interacting in immunotherapy for GC patients. The pathway of these lncRNAs which might be a target in PD-1/PD-L1 immunotherapy are needed to be further study.