Morphological and regional spontaneous functional aberrations in the brain associated with Crohn's disease: a systematic review and coordinate-based meta-analyses.

Crohn's disease is an acknowledged "brain-gut" disorder with unclear physiopathology. This study aims to identify potential neuroimaging biomarkers of Crohn's disease. Gray matter volume, cortical thickness, amplitude of low-frequency fluctuations, and regional homogeneity were selected as indices of interest and subjected to analyses using both activation likelihood estimation and seed-based d mapping with permutation of subject images. In comparison to healthy controls, Crohn's disease patients in remission exhibited decreased gray matter volume in the medial frontal gyrus and concurrently increased regional homogeneity. Furthermore, gray matter volume reduction in the medial superior frontal gyrus and anterior cingulate/paracingulate gyri, decreased regional homogeneity in the median cingulate/paracingulate gyri, superior frontal gyrus, paracentral lobule, and insula were observed. The gray matter changes of medial frontal gyrus were confirmed through both methods: decreased gray matter volume of medial frontal gyrus and medial superior frontal gyrus were identified by activation likelihood estimation and seed-based d mapping with permutation of subject images, respectively. The meta-regression analyses showed a positive correlation between regional homogeneity alterations and patient age in the supplementary motor area and a negative correlation between gray matter volume changes and patients' anxiety scores in the medial superior frontal gyrus. These anomalies may be associated with clinical manifestations including abdominal pain, psychiatric disorders, and possibly reflective of compensatory mechanisms.

Choline metabolism in regulating inflammatory bowel disease-linked anxiety disorders: A multi-omics exploration of the gut-brain axis.

Anxiety and depression caused by inflammatory bowel disease (IBD) negatively affect the mental health of patients. Emerging studies have demonstrated that the gut-brain axis (GBA) mediates IBD-induced mood disorders, but the underlying mechanisms of these findings remain unknown. Therefore, it's vital to conduct comprehensive research on the GBA in IBD. Multi-omics studies can provide an understanding of the pathological mechanisms of the GBA in the development of IBD, helping to uncover the mechanisms underlying the onset and progression of the disease. Thus, we analyzed the prefrontal cortex (PFC) of Dextran Sulfate Sodium Salt (DSS)-induced IBD mice using transcriptomics and metabolomics. We observed increased mRNA related to acetylcholine synthesis and secretion, along with decreased phosphatidylcholine (PC) levels in the PFC of DSS group compared to the control group. Fecal metagenomics also revealed abnormalities in the microbiome and lipid metabolism in the DSS group. Since both acetylcholine and PC are choline metabolites, we posited that the DSS group may experience choline deficiency and choline metabolism disorders. Subsequently, when we supplemented CDP-choline, IBD mice exhibited improvements, including decreased anxiety-like behaviors, reduced PC degradation, and increased acetylcholine synthesis in the PFC. In addition, administration of CDP-choline can restore imbalances in the gut microbiome and disruptions in lipid metabolism caused by DSS treatment. This study provides compelling evidence to suggest that choline metabolism plays a crucial role in the development and treatment of mood disorders in IBD. Choline and its metabolites appear to have a significant role in maintaining the stability of the GBA.

Assessment of Right Atrial Function Measured with Cardiac MRI Feature Tracking for Predicting Outcomes in Patients with Dilated Cardiomyopathy.

Background Right atrial (RA) function strain is increasingly acknowledged as an important predictor of adverse events in patients with diverse cardiovascular conditions. However, the prognostic value of RA strain in patients with dilated cardiomyopathy (DCM) remains uncertain. Purpose To evaluate the prognostic value of RA strain derived from cardiac MRI (CMR) feature tracking (FT) in patients with DCM. Materials and Methods This multicenter, retrospective study included consecutive adult patients with DCM who underwent CMR between June 2010 and May 2022. RA strain parameters were obtained using CMR FT. The primary end points were sudden or cardiac death or heart transplant. Cox regression analysis was used to determine the association of variables with outcomes. Incremental prognostic value was evaluated using C indexes and likelihood ratio tests. Results A total of 526 patients with DCM (mean age, 51 years ± 15 [SD]; 381 male) were included. During a median follow-up of 41 months, 79 patients with DCM reached the primary end points. At univariable analysis, RA conduit strain was associated with the primary end points (hazard ratio [HR], 0.82 [95% CI: 0.76, 0.87]; P < .001). In multivariable Cox analysis, RA conduit strain was an independent predictor for the primary end points (HR, 0.83 [95% CI: 0.77, 0.90]; P < .001). A model combining RA conduit strain with other clinical and conventional imaging risk factors (C statistic, 0.80; likelihood ratio, 92.54) showed improved discrimination and calibration for the primary end points compared with models with clinical variables (C statistic, 0.71; likelihood ratio, 37.12; both P < .001) or clinical and imaging variables (C statistic, 0.75; likelihood ratio, 64.69; both P < .001). Conclusion CMR FT-derived RA conduit strain was an independent predictor of adverse outcomes among patients with DCM, providing incremental prognostic value when combined in a model with clinical and conventional CMR risk factors. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Convolutional neural network applied to preoperative venous-phase CT images predicts risk category in patients with gastric gastrointestinal stromal tumors.

OBJECTIVE: The risk category of gastric gastrointestinal stromal tumors (GISTs) are closely related to the surgical method, the scope of resection, and the need for preoperative chemotherapy. We aimed to develop and validate convolutional neural network (CNN) models based on preoperative venous-phase CT images to predict the risk category of gastric GISTs. METHOD: A total of 425 patients pathologically diagnosed with gastric GISTs at the authors' medical centers between January 2012 and July 2021 were split into a training set (154, 84, and 59 with very low/low, intermediate, and high-risk, respectively) and a validation set (67, 35, and 26, respectively). Three CNN models were constructed by obtaining the upper and lower 1, 4, and 7 layers of the maximum tumour mask slice based on venous-phase CT Images and models of CNN_layer3, CNN_layer9, and CNN_layer15 established, respectively. The area under the receiver operating characteristics curve (AUROC) and the Obuchowski index were calculated to compare the diagnostic performance of the CNN models. RESULTS: In the validation set, CNN_layer3, CNN_layer9, and CNN_layer15 had AUROCs of 0.89, 0.90, and 0.90, respectively, for low-risk gastric GISTs; 0.82, 0.83, and 0.83 for intermediate-risk gastric GISTs; and 0.86, 0.86, and 0.85 for high-risk gastric GISTs. In the validation dataset, CNN_layer3 (Obuchowski index, 0.871) provided similar performance than CNN_layer9 and CNN_layer15 (Obuchowski index, 0.875 and 0.873, respectively) in prediction of the gastric GIST risk category (All P >.05). CONCLUSIONS: The CNN based on preoperative venous-phase CT images showed good performance for predicting the risk category of gastric GISTs.

Comparison of Reduced and Full Field of View in Diffusion-Weighted MRI on Image Quality: A Meta-Analysis.

BACKGROUND: Reduced field of view (rFOV) diffusion-weighted imaging (DWI) in MRI shows potential for enhanced image quality compared with traditional full field of view (fFOV) DWI. Evaluating rFOV DWI's impact on image quality is important for clinical adoption. OBJECTIVE: To assess the efficacy of rFOV DWI in improving image quality, focusing on artifact reduction, signal-to-noise ratio (SNR) improvement, and lesion detectability. STUDY TYPE: Meta-analysis. POPULATION: Systematic literature search was conducted in PubMed, Embase, the Cochrane Library, and Web of Science ending in January 2024. Thirteen studies with 765 participants focusing on DWI quality using rFOV was analyzed. FIELD STRENGTH/SEQUENCE: SS-EPI, Rtr-SS-EPI, 2D-SS-EPI at 3.0 T. ASSESSMENT: Two investigators performed the data extraction. QUADAS-2 assessed bias. The image quality assessment of rFOV and fFOV DWI were compared. STATISTICAL TESTS: Standardized mean difference (SMD) was utilized to evaluate and standardize MRI image quality. Heterogeneity was assessed using the I2 statistic and publication bias was evaluated with Egger's test. RESULTS: The QUADAS-2 analysis revealed that most studies exhibited a low risk of bias and minimal concerns regarding applicability. Statistical analysis indicated that rFOV DWI yielded higher subjective image quality scores (SMD = 0.535, 95% CI: 0.339, 0.731, I2 = 45.7%) compared with fFOV DWI and was more effective in reducing artifacts (SMD = 0.44, 95% CI: 0.209, 0.672, I2 = 42.3%) than fFOV DWI. However, a decrease in SNR was noted with rFOV DWI (SMD = -0.670, 95% CI: -1.187 to -0.152, I2 = 87.9%). Additionally, rFOV DWI demonstrated enhancements in lesion visibility (SMD = 0.432, 95% CI: -1.187, -0.152, I2 = 53.1%) and anatomical details (SMD = 0.598, 95% CI: 0.121, 1.075, I2 = 90.8%). DATA CONCLUSION: rFOV DWI enhances MRI image quality by reducing artifacts and improving lesion visibility with a SNR trade-off. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Age- and Sex-Specific MR-Feature Tracking Reference Values of Right Atrial Deformation in Healthy Adults.

BACKGROUND: Although right atrial (RA) myocardial deformation has important implications for patient diagnosis, prognosis, and risk stratification, its implementation in clinical practice has been hampered by limited normal reference values, especially in Asian populations. PURPOSE: To establish age- and sex-specific reference values for RA strain, strain rate (SR), and displacement based on a large sample of healthy Chinese adults using MR-feature tracking (MR-FT). STUDY TYPE: Retrospective. POPULATION: 524 healthy Chinese adults (287 male; mean age 43.7 ± 11.9 years). FIELD STRENGTH/SEQUENCE: 1.5T/balanced steady-state free precession. ASSESSMENT: RA deformation parameters, including reservoir, conduit, and booster strain (εs, εe, and εa), peak positive, early negative, and late negative SR (SRs, SRe, and SRa), and total, passive, and active displacement (Ds, De, and Da), were assessed using MR-FT. STATISTICAL TESTS: Student's t-test, one-way ANOVA, coefficients of determination (r2 ), intraclass correlation coefficients (ICC), and Bland-Altman plots. A P value <0.05 was considered significant. RESULTS: Women demonstrated significantly greater magnitudes of RA deformation parameters than men: εs (57.4% ± 15.1% vs. 44.3% ± 12.6%), εe (37.5% ± 13.4% vs. 27.4% ± 10.9%), εa (19.9% ± 5.7% vs. 16.9% ± 5.0%), SRs (2.62 ± 0.88 sec-1 vs. 2.00 ± 0.63 sec-1 ), SRe (-2.98 ± 1.26 sec-1 vs. -2.16 ± 0.92 sec-1 ), SRa (-2.28 ± 0.75 sec-1 vs. -1.84 ± 0.62 sec-1 ), Ds (-7.80 ± 1.90 mm vs. -7.46 ± 1.70 mm), and De (-4.84 ± 1.31 mm vs. -4.49 ± 1.21 mm). For both sexes, aging was significantly associated with decreased RA reservoir and conduit function (εs, SRs, Ds, εe, SRe, and De), and with increased εa and Da. RA deformation measurements had good to excellent intraobserver and interobserver reproducibility, with ICCs ranging from to 0.790 to 0.972. DATA CONCLUSION: This study provides age- and sex-specific reference values of RA strain, SR, and displacement based on a large cohort of healthy Chinese adults using MR-FT. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Shear-wave elastography in renal stiffness in children with hematuria and/or proteinuria.

BACKGROUND: We sought to evaluate renal stiffness in children with hematuria and/or proteinuria using shear wave elastography (SWE) and to investigate the clinical value of renal stiffness in children with hematuria and/or proteinuria. METHODS: According to the results of urinary occult blood and urinary protein tests, 349 pediatric patients were categorized into one of four groups: pure hematuria (HU), pure proteinuria (PU), concomitant hematuria and proteinuria (HUPU), or control (non-HUPU). Patient demographic data, laboratory test results, and renal ultrasound data were collected. RESULTS: There were significant differences in cortical/medullary elasticity among the four groups (the most sensitive cutoff value between HU and PU was 1.72) (P < 0.05). We found that hematuria and proteinuria interacted with renal cortical elasticity (P < 0.05) but that hematuria and proteinuria did not interact with renal medullary elasticity or cortical/medullary elasticity (P > 0.05). Renal elasticity values correlated with sex, age, body surface area, body mass index, qualitative urinary protein, urine N-acetyl-ß-D-glucosaminidase, 24-hour urinary protein quantity, renal volume, and renal cortical thickness (P < 0.05). CONCLUSIONS: SWE can be used to detect changes in renal stiffness in children with hematuria and/or proteinuria. SWE is beneficial for the early detection of glomerular disease in children with abnormal urine test results. IMPACT: This study evaluated the utility of shear wave elastography for the assessment of renal elasticity in pediatric patients presenting with hematuria and/or proteinuria. Children with pure proteinuria had significantly higher renal cortical/medullary elasticity values than those with pure hematuria. An interaction effect between hematuria and proteinuria on renal cortical stiffness was observed. Shear wave elastography can be used as a tool to assess early renal injury in children with urinalysis abnormalities.

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic.

BACKGROUND AND PURPOSE: The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features. METHODS: A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley's Additive interpretation (SHAP) illustrated the importance of CT features. RESULTS: A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group's radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models. CONCLUSION: Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes.

Magnetic resonance imaging-based radiomics was used to evaluate the level of prognosis-related immune cell infiltration in breast cancer tumor microenvironment.

PURPOSE: The tumor immune microenvironment is a valuable source of information for predicting prognosis in breast cancer (BRCA) patients. To identify immune cells associated with BRCA patient prognosis from the Cancer Genetic Atlas (TCGA), we established an MRI-based radiomics model for evaluating the degree of immune cell infiltration in breast cancer patients. METHODS: CIBERSORT was utilized to evaluate the degree of infiltration of 22 immune cell types in breast cancer patients from the TCGA database, and both univariate and multivariate Cox regressions were employed to determine the prognostic significance of immune cell infiltration levels in BRCA patients. We identified independent prognostic factors for BRCA patients. Additionally, we obtained imaging features from the Cancer Imaging Archive (TCIA) database for 73 patients who underwent preoperative MRI procedures, and used the Least Absolute Shrinkage and Selection Operator (LASSO) to select the best imaging features for constructing an MRI-based radiomics model for evaluating immune cell infiltration levels in breast cancer patients. RESULTS: According to the results of Cox regression analysis, M2 macrophages were identified as an independent prognostic factor for BRCA patients (HR = 32.288, 95% CI: 3.100-357.478). A total of nine significant features were selected to calculate the radiomics-based score. We established an intratumoral model with AUCs (95% CI) of 0.662 (0.495-0.802) and 0.678 (0.438-0.901) in the training and testing cohorts, respectively. Additionally, a peritumoral model was created with AUCs (95% CI) of 0.826 (0.710-0.924) and 0.752 (0.525-0.957), and a combined model was established with AUCs (95% CI) of 0.843 (0.723-0.938) and 0.744 (0.491-0.965). The peritumoral model demonstrated the highest diagnostic efficacy, with an accuracy, sensitivity, and specificity of 0.773, 0.727, and 0.818, respectively, in its testing cohort. CONCLUSION: The MRI-based radiomics model has the potential to evaluate the degree of immune cell infiltration in breast cancer patients, offering a non-invasive imaging biomarker for assessing the tumor microenvironment in this disease.

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.

BACKGROUND: The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. METHODS: Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. RESULTS: Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively. CONCLUSIONS: Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. TRIAL REGISTRATION: CRD42022375712.

Evaluating Upstaging in Ductal Carcinoma In Situ Using Preoperative MRI-Based Radiomics.

BACKGROUND: About 20%-40% of patients diagnosed with ductal carcinoma in situ (DCIS) by core needle biopsy (CNB) will develop invasive cancer at the time of excision. Improving the preoperative diagnosis of DCIS is important for surgical planning. PURPOSE: To establish an MRI-based radiomics nomogram for preoperatively evaluating the upstaging of DCIS patients and help with risk stratification. STUDY TYPE: Retrospective. POPULATION: A total of 227 patients (50.5 ± 9.7 years; 67 upstaged DCIS) were divided into training (n = 109), internal (n = 47), and external (n = 71) validation cohort. FIELD STRENGTH/SEQUENCE: 1.5-T or 3-T, dynamic contrast-enhanced (DCE) imaging, and diffusion-weighted imaging (DWI). ASSESSMENT: DCIS lesions were manually segmented using ITK-SNAP software and 1304 radiomic features were extracted from DCE, DWI, and apparent diffusion coef-ficient (ADC) maps, respectively. A radscore was calculated by a random forest algo-rithm based on DCIS upstaging-related radiomic features, which selected by a coarse-to-fine method including interclass correlation coefficient, single-factor anal-ysis, and the least absolute shrinkage and selection operator (LASSO) method. Uni-variate and multivariate logistic regression was used to analyze the independent risk factors, including age, location, lesion size, estrogen receptor (ER) status, and other clinico-pathologic factors. Finally, Mann-Whitney U tests were performed to com-pare the differences in radscore between low/intermediate and high nuclear grade groups for pure DCIS patients. STATISTICAL TESTS: Student's t-tests or Mann-Whitney U tests, chi-square-tests, or Fisher's-tests, univariate and multivariate logistic regression analysis, calibration curve, Youden index, the area under the curve (AUC), Delong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) analyses. RESULTS: Eight important radiomic features (two from ADC, three from DWI, and three from DCE) were selected for calculating radscore. Clinical model including age and ER was established with AUCs of 0.747 and 0.738 in the internal and external validation cohorts, respectively. A combined model integrating age, estrogen receptor (ER), and radscore were also constructed with AUCs of 0.887 and 0.881. Further subgroup analysis showed that pure DCIS patients with different nuclear grade have significant differences in radscore. DATA CONCLUSION: Multisequence MRI radiomics may preoperatively evaluate the upstaging of DCIS and might provide personalized image-based clinical decision support. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review.

OBJECTIVES: To provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies. METHODS: The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: Thirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4-19), while all of included studies were assessed as having a high risk of bias (ROB) overall. CONCLUSIONS: Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed. KEY POINTS: • Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer. • The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins. • The median Radiomics Quality Score of the included studies was 13 (range 4-19), and all of studies were considered to have a high risk of bias overall.

The long-term course of subsolid nodules and predictors of interval growth on chest CT: a systematic review and meta-analysis.

OBJECTIVES: To calculate the pooled incidence of interval growth after long-term follow-up and identify predictors of interval growth in subsolid nodules (SSNs) on chest CT. METHODS: A search of MEDLINE (PubMed), Cochrane Library, Web of Science Core Collection, and Embase was performed on November 08, 2021, for relevant studies. Patient information, CT scanner, and SSN follow-up information were extracted from each included study. A random-effects model was applied along with subgroup and meta-regression analyses. Study quality was assessed by the Newcastle-Ottawa scale, and publication bias was assessed by Egger's test. RESULTS: Of the 6802 retrieved articles, 16 articles were included and analyzed, providing a total of 2898 available SSNs. The pooled incidence of growth in the 2898 SSNs was 22% (95% confidence interval [CI], 15-29%). The pooled incidence of growth in the subgroup analysis of pure ground-glass nodules was 26% (95% CI: 12-39%). The incidence of SSN growth after 2 or more years of stability was only 5% (95% CI: 3-7%). An initially large SSN size was found to be the most frequent risk factor affecting the incidence of SSN growth and the time of growth. CONCLUSIONS: The pooled incidence of SSN growth was as high as 22%, with a 26% incidence reported for pure ground-glass nodules. Although the incidence of growth was only 5% after 2 or more years of stability, long-term follow-up is needed in certain cases. Moreover, the initial size of the SSN was the most frequent risk factor for growth. KEY POINTS: • Based on a meta-analysis of 2898 available subsolid nodules in the literature, the pooled incidence of growth was 22% for all subsolid nodules and 26% for pure ground-glass nodules. • After 2 or more years of stability on follow-up CT, the pooled incidence of subsolid nodule growth was only 5%. • Given the incidence of subsolid nodule growth, management of these lesions with long-term follow-up is preferred.

Bifico relieves irritable bowel syndrome by regulating gut microbiota dysbiosis and inflammatory cytokines.

PURPOSE: Gut microbiota dysbiosis, a core pathophysiology of irritable bowel syndrome (IBS), is closely related to immunological and metabolic functions. Gut microbiota-based therapeutics have been recently explored in several studies. Bifico is a probiotic cocktail widely used in gastrointestinal disorders which relate to the imbalance of gut microbiota. However, the efficacy and potential mechanisms of Bifico treatment in IBS remains incompletely understood. METHODS: Adopting a wrap restraint stress (WRS) -induced IBS mice model. Protective effect of Bifico in IBS mice was examined through abdominal withdrawal reflex (AWR) scores. 16S rDNA, 1H nuclear magnetic resonance (1H-NMR) and western blot assays were performed to analyze alterations of gut microbiota, microbiome metabolites and inflammatory cytokines, respectively. RESULTS: Bifico could decrease intestinal visceral hypersensitivity. Although gut microbiota diversity did not increase, composition of gut microbiota was changed after treatment of Bifico, which were characterized by an increase of Proteobacteria phylum and Actinobacteria phylum, Muribaculum genus, Bifidobacterium genus and a decrease of Parabacteroides genus, Sutterella genus and Lactobacillus genus. Moreover, Bifico elevated the concentration of short-chain fatty acids (SCFAs) and reduced protein levels of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α). From further Spearman's correlation analysis, Bifidobacterium genus were positively correlated with SCFAs including propionate, butyrate, valerate and negatively correlated with IL-6 and TNF-α. CONCLUSION: Bifico could alleviate symptoms of IBS mice through regulation of the gut microbiota, elevating production of SCFAs and reducing the colonic inflammatory response.

The improvement of intestinal dysbiosis and hepatic metabolic dysfunction in dextran sulfate sodium-induced colitis mice: effects of curcumin.

BACKGROUND AND AIM: Curcumin may have promising application in the prevention and amelioration of inflammatory bowel disease (IBD). However, the underlying mechanisms underpinning the ability of curcumin to interact with the gut and liver in IBD remains to be defined, which is the exploration aim of this study. METHODS: Mice with dextran sulfate sodium salt (DSS)-induced acute colitis were treated either with 100 mg/kg of curcumin or phosphate buffer saline (PBS). Hematoxylin-eosin (HE) staining, 16S rDNA Miseq sequencing, proton nuclear magnetic resonance (1 H NMR) spectroscopy, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were applied for analysis. Spearman's correlation coefficient (SCC) was utilized to assess the correlation between the modification of intestinal bacteria and hepatic metabolite parameters. RESULTS: Curcumin supplementation not only prevented further loss of body weight and colon length in IBD mice but also improved diseases activity index (DAI), colonic mucosal injury, and inflammatory infiltration. Meanwhile, curcumin restored the composition of the gut microbiota, significantly increased Akkermansia, Muribaculaceae_unclassified, and Muribaculum, and significantly elevated the concentration of propionate, butyrate, glycine, tryptophan, and betaine in the intestine. For hepatic metabolic disturbances, curcumin intervention altered 14 metabolites, including anthranilic acid and 8-amino-7-oxononanoate while enriching pathways related to the metabolism of bile acids, glucagon, amino acids, biotin, and butanoate. Furthermore, SCC analysis revealed a potential correlation between the upregulation of intestinal probiotics and alterations in liver metabolites. CONCLUSION: The therapeutic mechanism of curcumin against IBD mice occurs by improving intestinal dysbiosis and liver metabolism disorders, thus contributing to the stabilization of the gut-liver axis.

High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4.

BACKGROUND: BI-RADS 4 breast lesions are suspicious for malignancy with a range from 2 to 95%, indicating that numerous benign lesions are unnecessarily biopsied. Thus, we aimed to investigate whether high-temporal-resolution dynamic contrast-enhanced MRI (H_DCE-MRI) would be superior to conventional low-temporal-resolution DCE-MRI (L_DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions. METHODS: This single-center study was approved by the IRB. From April 2015 to June 2017, patients with breast lesions were prospectively included and randomly assigned to undergo either H_DCE-MRI, including 27 phases, or L_DCE-MRI, including 7 phases. Patients with BI-RADS 4 lesions were diagnosed by the senior radiologist in this study. Using a two-compartment extended Tofts model and a three-dimensional volume of interest, several pharmacokinetic parameters reflecting hemodynamics, including Ktrans, Kep, Ve, and Vp, were obtained from the intralesional, perilesional and background parenchymal enhancement areas, which were labeled the Lesion, Peri and BPE areas, respectively. Models were developed based on hemodynamic parameters, and the performance of these models in discriminating between benign and malignant lesions was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 140 patients were included in the study and underwent H_DCE-MRI (n = 62) or L_DCE-MRI (n = 78) scans; 56 of these 140 patients had BI-RADS 4 lesions. Some pharmacokinetic parameters from H_DCE-MRI (Lesion_Ktrans, Kep, and Vp; Peri_Ktrans, Kep, and Vp) and from L_DCE-MRI (Lesion_Kep, Peri_Vp, BPE_Ktrans and BPE_Vp) were significantly different between benign and malignant breast lesions (P < 0.01). ROC analysis showed that Lesion_Ktrans (AUC = 0.866), Lesion_Kep (AUC = 0.929), Lesion_Vp (AUC = 0.872), Peri_Ktrans (AUC = 0.733), Peri_Kep (AUC = 0.810), and Peri_Vp (AUC = 0.857) in the H_DCE-MRI group had good discrimination performance. Parameters from the BPE area showed no differentiating ability in the H_DCE-MRI group. Lesion_Kep (AUC = 0.767), Peri_Vp (AUC = 0.726), and BPE_Ktrans and BPE_Vp (AUC = 0.687 and 0.707) could differentiate between benign and malignant breast lesions in the L_DCE-MRI group. The models were compared with the senior radiologist's assessment for the identification of BI-RADS 4 breast lesions. The AUC, sensitivity and specificity of Lesion_Kep (0.963, 100.0%, and 88.9%, respectively) in the H_DCE-MRI group were significantly higher than those of the same parameter in the L_DCE-MRI group (0.663, 69.6% and 75.0%, respectively) for the assessment of BI-RADS 4 breast lesions. The DeLong test was conducted, and there was a significant difference only between Lesion_Kep in the H_DCE-MRI group and the senior radiologist (P = 0.04). CONCLUSIONS: Pharmacokinetic parameters (Ktrans, Kep and Vp) from the intralesional and perilesional regions on high-temporal-resolution DCE-MRI, especially the intralesional Kep parameter, can improve the assessment of benign and malignant BI-RADS 4 breast lesions to avoid unnecessary biopsy.

A study on the correlation of the asymmetric regulation between the periaqueductal gray and the bilateral trigeminal nucleus caudalis in migraine male rats.

BACKGROUND: The study was designed to explore the correlation of the asymmetric regulation between periaqueductal gray (PAG) and bilateral trigeminal nucleus caudalis (TNC) in migraine rats through studying the changes of metabolites in pain regulatory pathway of acute migraine attack. METHODS: Thirty male Sprague-Dawley (SD) rats were randomly divided into three groups: blank, control, model groups. Then, blank group was intraperitoneally injected with ultrapure water, while control group injected with saline and model group injected with Glyceryl Trinitrate (GTN). Two hours later, PAG and bilateral TNC were removed respectively, and metabolite concentrations of PAG, Left-TNC, Right-TNC were obtained. Lastly, the differences of metabolite among three brain tissues were compared. RESULTS: The relative concentrations of rNAA, rGlu, rGln, rTau, rMI in PAG or bilateral TNC had interaction effects between groups and sites. The concentration of rLac of three brain tissues increased in migraine rats, however, the rLac of LTNC and RTNC increased more than that of PAG. Besides, the concentrations of rNAA and rGln increased in RTNC, while rGABA decreased in RTNC. CONCLUSIONS: There is correlation between PAG, LTNC and RTNC in regulation of pain during acute migraine attack, and the regulation of LTNC and RTNC on pain is asymmetric.

Development and validation of the potential biomarkers based on m6A-related lncRNAs for the predictions of overall survival in the lung adenocarcinoma and differential analysis with cuproptosis.

BACKGROUND: The treatment and prognosis of lung adenocarcinoma (LUAD) remains a challenge. The study aimed to conduct a systematic analysis of the predictive capacity of N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) in the prognosis of LUAD. METHODS: 594 samples were totally selected from a dataset from The Cancer Genome Atlas. The identification of prognostic m6A-related lncRNAs were performed by Pearson correlation analysis and Cox regression analysis. Systematic analyses, including cluster analysis, survival analysis, and immuno-correlated analysis, were conducted. A prognosis model was built from the optimized subset of m6A-related lncRNAs. The assessment of model was performed by survival analysis, and receiver operating characteristic (ROC) curve. Finally, the risk score of patients with LUAD calculated by the prognosis model was implemented by the analysis of Cox regression. Differential analysis was for further evaluation of the cuproptosis-related genes in two risk sets. RESULTS: These patients were grouped into two clusters according to the expression levels of 22 prognostic m6A-related lncRNAs. The patients with LUAD in cluster 2 was significantly worse in the overall survival (OS) (P = 0.006). Three scores calculated by the ESTIMATE methods in cluster 2 were significantly lower. After the least absolute shrinkage and selection operator algorithm, 10 prognostic m6A-related lncRNAs were totally selected to construct the final model to obtain the risk score. Then the area under the ROC curve of the prognosis model for 1, 3, and 5-year OS was 0.767, 0.709, and 0.736 in the training set, and 0.707, 0.691, and 0.675 in the test set. The OS of the low-risk cohort was significantly higher than that of the high-risk cohort in both the training set (P < 0.001) and test set (P < 0.001). After the analysis of Cox regression, the risk score [Hazard ratio (HR) = 5.792; P < 0.001] and stage (HR = 1.576; P < 0.001) were both considered as independent indicators of prognosis for LUAD. The expression levels of five cuproptosis-related genes were significantly different in two risk sets. CONCLUSIONS: The study constructed a predictive model for the OS of patients with LUAD and these OS-related m6A-lncRNAs might have potential roles in LUAD progression.

Radiomic Analysis of Pharmacokinetic Heterogeneity Within Tumor Based on the Unsupervised Decomposition of Dynamic Contrast-Enhanced MRI for Predicting Histological Characteristics of Breast Cancer.

BACKGROUND: Breast tumor heterogeneity is associated with histological characteristics. However, pharmacokinetic (PK) heterogeneity within tumor might merit further exploration. PURPOSE: To enhance the predictive power of molecular subtypes, Ki-67, and tumor grade by analyzing PK heterogeneity within tumor based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). STUDY TYPE: Retrospective. POPULATION: Two hundred and eight biopsy-proven breast cancer patients, randomly divided into a training cohort (N = 144) and a testing cohort (N = 64). FIELD STRENGTH/SEQUENCE: T1 -weighted DCE-MRI at 3.0 T. ASSESSMENT: A convex analysis of mixtures-compartmental modeling decomposition method was used to estimate the PK parameter (i.e., the volume transfer constant Ktrans ) in tumor subregions with distinct physiological kinetic patterns, including fast-flow kinetics, slow-flow kinetics, and plasma input. Radiomic features based on the PK parameter were calculated from each tumor subregion. STATISTICAL TESTS: The training cohort was used to build random forest classifiers based on the optimal features determined by the 5-fold cross-validation method. The performance was assessed on the testing cohort using the area under the receiver operating characteristic curve (AUC). The AUCs derived from the tumor subregion-based PK parameter were compared with those of the original images of the entire tumor using the DeLong test. A P-value of <0.05 was considered statistically significant. RESULTS: The tumor subregion-based PK parameter, which yielded the highest AUCs of 0.8782, 0.7568, 0.7019, 0.7963, 0.8080, and 0.7375 for luminal A, luminal B, basal-like, human epidermal growth factor receptor 2, Ki-67, and tumor grade, respectively, obtained better diagnostic performance than the original images in the entire tumor (highest AUCs = 0.8612, 0.6191, 0.5593, 0.7704, 0.7494, and 0.6261, respectively). In particular, statistically significant improvement in the diagnostic performance was obtained for luminal B. DATA CONCLUSION: Radiomic analysis of PK heterogeneity within tumor can enhance the predictive performance of radiomic models compared with that of the entire tumor. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.

The hypothalamus may mediate migraine and ictal photophobia: evidence from Granger causality analysis.

BACKGROUND: The hypothalamus plays a central role in the pathophysiology of migraine and is considered to be the "migraine generator." It participates in initiating a migraine attack through its connectivity to regions of the brain involved in processing and modulating pain. However, the underlying mechanisms of hypothalamic effective functional connectivity that bring about migraines remain unclear. This study investigated the hypothalamus-based directional effective connectivity in migraine without aura patients and assessed associations among the clinical characteristics. METHODS: Seven patients with migraine without aura during the attack (MWoA-DA) (four with photophobia (MWoA-DAWP) and three without photophobia (MWoA-DAWoP)), twenty-seven patients with migraine without aura during the interictal period (MWoA-DI), and twenty-nine healthy controls (HC) were included in this study. Granger causality analysis (GCA) was used to investigate the directional effective connectivity between the hypothalamus and other brain regions. RESULTS: MWoA-DA patients exhibited decreased outflow from the bilateral hypothalamus to the visual cortex compared with the MWoA-DI patients and HCs. The MWoA-DAWP group primarily contributed to this result. The MWoA-DA patients showed decreased outflow from the bilateral hypothalamus to the right inferior parietal gyrus compared with the HCs. The visual analogue scale (VAS) was negatively correlated with abnormal effective functional connectivity from the right hypothalamus to the right inferior parietal gyrus in the MWoA-DA group. CONCLUSIONS: These data provide evidence that the hypothalamus might serve as a central component of a multisystem network implicated in migraine and ictal photophobia, which includes hypothalamus and the visual and trigeminovascular systems.