CT-Based Radiomics Models for Differentiation of Benign and Malignant Thyroid Nodules: A Multicenter Development-and-Validation.

Background: CT is increasingly detecting thyroid nodules. Prior studies indicated a potential role of CT-based radiomics models in characterizing thyroid nodules, although lacked external validation. Objectives: To develop and validate a CT-based radiomics model for the differentiation of benign and malignant thyroid nodules. Methods: This retrospective study included 378 patients (mean age, 46.3±13.9 years; 86 men, 292 women) with 408 resected thyroid nodules (145 benign, 263 malignant) from two centers (center 1: 293 nodules, January 2018-December 2022; center 2: 115 nodules, January 2020-December 2022), who underwent preoperative multiphase neck CT (noncontrast, arterial, and venous phases). Nodules from center 1 were divided into training (n=206) and internal validation (n=87) sets; all nodules from center 2 formed an external validation set. Radiologists assessed nodules for morphologic CT features. Nodules were manually segmented on all phases, and radiomic features were extracted. Conventional (clinical and morphologic CT), noncontrast radiomics, arterial-phase radiomics, venous-phase radiomics, multiphase radiomics, and combined (clinical, morphologic, and multiphase radiomics) models were established using feature selection methods and evaluated by ROC curve analysis, calibration curves, and decision-curve analysis. Results: The combined model included patient age, three morphologic features (cystic change, edge interruption sign, abnormal cervical lymph nodes), and 28 radiomic features (from all three phases). In the external validation set, the combined model had AUC of 0.923 and, at an optimal threshold derived in the training set, sensitivity of 84.0%, specificity of 94.1%, and accuracy of 87.0%. In the external validation set, AUC was significantly higher for the combined model than for the conventional model (0.827), noncontrast radiomics model (0.847), arterial-phase radimoics model (0.826), venous-phase radiomics model (0.773), and multiphase radiomics model (0.824) (all p<.05). In the external validation set, the calibration curves indicated lowest (i.e., best) Brier score for the combined model; in decision-curve analysis, the combined model had the highest net benefit for most of the range of threshold probabilities. Conclusion: A combined model incorporating clinical, morphologic CT, and multiphasic radiomics CT features, exhibited robust performing in differentiating benign and malignant thyroid nodules. Clinical Impact: The combined radiomics model may help guide further management for thyroid nodules detected on CT.

Geriatric nutrition risk index: a more powerful index identifying muscle mass loss in patients with rheumatoid arthritis.

OBJECTIVE: To explore the association of geriatric nutrition risk index (GNRI), a traditional albumin-body weight calculation, with myopenia in patients with rheumatoid arthritis (RA) and compare its ability to identify myopenia with protein indicators. METHODS: This cross-sectional study was carried out based on a Chinese RA cohort. Clinical data and protein indicators (including albumin, globulin, albumin to globulin ratio, prealbumin, hemoglobin) were collected. GNRI was estimated by serum albumin and body weight. Myopenia was indicated as muscle mass loss measured by bioelectric impedance analysis. RESULTS: There were 789 RA patients included with mean age 52.6 ± 12.6 years and 77.6% female. There were 41.3%, 18.0%, 27.5%, 13.2% patients with no (GNRI > 98), low (GNRI 92 to ≤ 98), moderate (GNRI 82 to < 92), and major nutrition-related risk (GNRI < 82). There were 406 (51.5%) RA patients with myopenia, RA patients with major nutrition-related risk had the highest prevalence of myopenia (87.5% vs. 73.3% vs. 50.0% vs. 26.1%). Multivariate logistic analysis showed that compared with no risk, RA patients with low (OR = 3.23, 95% CI: 1.86-5.61), moderate (OR = 9.56, 95% CI: 5.70-16.01), and major nutrition-related risk (OR = 28.91, 95% CI: 13.54-61.71) were associated with higher prevalence of myopenia. Receiver operating characteristic curves showed that GNRI (AUC = 0.79) performed a better identifiable ability toward myopenia than serum albumin (AUC = 0.66) or others indicators (AUC range 0.59 to 0.65), respectively. CONCLUSION: GNRI, an objective and convenient albumin-weight index, may be preferable for identifying myopenia in RA patients. Key Points • We firstly elucidated the association of GNRI with muscle mass loss among RA patients, and compared its ability to identify muscle mass loss with serum albumin or other protein indicators. • Major nutrition-related risk identified by GNRI showed the highest risk of muscle mass loss, GNRI demonstrated a greater ability to identify myopenia in RA patients. which indicated GNRI was an objective and convenient albumin-weight index to identify myopenia in RA patients.

Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics.

OBJECTIVES: To explore the performance of multiparametric MRI-based radiomics in discriminating different human epidermal growth factor receptor 2 (HER2) expressing statuses (i.e., HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer. METHODS: A total of 771 breast cancer patients from two institutions were retrospectively studied. Five-hundred-eighty-one patients from Institution I were divided into a training dataset (n1 = 407) and an independent validation dataset (n1 = 174); 190 patients from Institution II formed the external validation dataset. All patients were categorized into HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing groups based on pathologic examination. Multiparametric (including T2-weighted imaging with fat suppression [T2WI-FS], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC], and dynamic contrast-enhanced [DCE]) MRI-based radiomics features were extracted and then selected from the training dataset using the least absolute shrinkage and selection operator (LASSO) regression. Three predictive models to discriminate HER2-overexpressing vs. others, HER2-low expressing vs. others, and HER2-zero-expressing vs. others were developed based on the selected features. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: Eleven radiomics features from DWI, ADC, and DCE; one radiomics feature from DWI; and 17 radiomics features from DWI, ADC, and DCE were selected to build three predictive models, respectively. In training, independent validation, and external validation datasets, radiomics models achieved AUCs of 0.809, 0.737, and 0.725 in differentiating HER2-overexpressing from others; 0.779, 0.778, and 0.782 in differentiating HER2-low-expressing from others; and 0.889, 0.867, and 0.813 in differentiating HER2-zero-expressing from others, respectively. CONCLUSIONS: Multiparametric MRI-based radiomics model may preoperatively predict HER2 statuses in breast cancer patients. CLINICAL RELEVANCE STATEMENT: The MRI-based radiomics models could be used to noninvasively identify the new three-classification of HER2 expressing status in breast cancer, which is helpful to the decision-making for HER2-target therapies. KEY POINTS: • Detecting HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing status in breast cancer patients is crucial for determining candidates for anti-HER2 therapy. • Radiomics features from multiparametric MRI significantly differed among HER2-overexpressing, HER2-low expressing, and HER2-zero-expressing breast cancers. • Multiparametric MRI-based radiomics could preoperatively evaluate three different HER2-expressing statuses and help to determine potential candidates for anti-HER2 therapy in breast cancer patients.

Development and Validation of MRI Radiomics Models to Differentiate HER2-Zero, -Low, and -Positive Breast Cancer.

BACKGROUND. Breast cancer HER2 expression has been redefined using a three-tiered system, with HER2-zero cancers considered ineligible for HER2-targeted therapy, HER2-low cancers considered candidates for novel HER2-targeted drugs, and HER2-positive cancers treated with traditional HER2-targeted medications. OBJECTIVE. The purpose of this study was to assess MRI radiomics models for a three-tiered classification of HER2 expression of breast cancer. METHODS. This retrospective study included 592 patients with pathologically confirmed breast cancer (mean age, 47.0 ± 18.0 [SD] years) who underwent breast MRI at either of a health system's two hospitals from April 2016 through June 2022. Three-tiered HER2 status was pathologically determined. Radiologists assessed the conventional MRI features of tumors and manually segmented the tumors on multiparametric sequences (T2-weighted images, DWI, ADC maps, and T1-weighted delayed contrast-enhanced images) to extract radiomics features. Least absolute shrinkage and selection operator analysis was used to develop two radiomics signatures, to differentiate HER2-zero cancers from HER2-low or HER2-positive cancers (task 1) as well as to differentiate HER2-low cancers from HER2-positive cancers (task 2). Patients from hospital 1 were randomly assigned to a discovery set (task 1: n = 376; task 2: n = 335) or an internal validation set (task 1: n = 161; task 2: n = 143); patients from hospital 2 formed an external validation set (task 1: n = 55; task 2: n = 50). Multivariable logistic regression analysis was used to create nomograms combining radiomics signatures with clinicopathologic and conventional MRI features. RESULTS. AUC, sensitivity, and specificity in the discovery, internal validation, and external validation sets were as follows: for task 1, 0.89, 99.4%, and 69.0%; 0.86, 98.6%, and 76.5%; and 0.78, 100.0%, and 0.0%, respectively; for task 2, 0.77, 93.8%, and 32.3%; 0.75, 92.9%, and 6.8%; and 0.77, 97.0%, and 29.4%, respectively. For task 1, no nomogram was created because no clinicopathologic or conventional MRI feature was associated with HER2 status independent of the MRI radiomics signature. For task 2, a nomogram including an MRI radiomics signature and three pathologic features (histologic grade of III, high Ki-67 index, and positive progesterone receptor status) that were independently associated with HER2-low expression had an AUC of 0.87, 0.83, and 0.80 in the three sets. CONCLUSION. MRI radiomics features were used to differentiate HER2-zero from HER2-low cancers or HER2-positives cancers as well as to differentiate HER2-low cancers from HER2-positive cancers. CLINICAL IMPACT. MRI radiomics may help select patients for novel or traditional HER2-targeted therapies, particularly those patients with ambiguous results of immunohistochemical staining results or limited access to fluorescence in situ hybridization.

Prenatal Diagnosis of Placenta Accreta Spectrum Disorders: Deep Learning Radiomics of Pelvic MRI.

BACKGROUND: Diagnostic performance of placenta accreta spectrum (PAS) by prenatal MRI is unsatisfactory. Deep learning radiomics (DLR) has the potential to quantify the MRI features of PAS. PURPOSE: To explore whether DLR from MRI can be used to identify pregnancies with PAS. STUDY TYPE: Retrospective. POPULATION: 324 pregnant women (mean age, 33.3 years) suspected PAS (170 training and 72 validation from institution 1, 82 external validation from institution 2) with clinicopathologically proved PAS (206 PAS, 118 non-PAS). FIELD STRENGTH/SEQUENCE: 3-T, turbo spin-echo T2-weighted images. ASSESSMENT: The DLR features were extracted using the MedicalNet. An MRI-based DLR model incorporating DLR signature, clinical model (different clinical characteristics between PAS and non-PAS groups), and MRI morphologic model (radiologists' binary assessment for the PAS diagnosis) was developed. These models were constructed in the training dataset and then validated in the validation datasets. STATISTICAL TESTS: The Student t-test or Mann-Whitney U, χ2 or Fisher exact test, Kappa, dice similarity coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator logistic regression, multivariate logistic regression, receiver operating characteristic (ROC) curve, DeLong test, net reclassification improvement (NRI) and integrated discrimination improvement (IDI), calibration curve with Hosmer-Lemeshow test, decision curve analysis (DCA). P < 0.05 indicated a significant difference. RESULTS: The MRI-based DLR model had a higher area under the curve than the clinical model in three datasets (0.880 vs. 0.741, 0.861 vs. 0.772, 0.852 vs. 0.675, respectively) or MRI morphologic model in training and independent validation datasets (0.880 vs. 0.760, 0.861, vs. 0.781, respectively). The NRI and IDI were 0.123 and 0.104, respectively. The Hosmer-Lemeshow test had nonsignificant statistics (P = 0.296 to 0.590). The DCA offered a net benefit at any threshold probability. DATA CONCLUSION: An MRI-based DLR model may show better performance in diagnosing PAS than a clinical or MRI morphologic model. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Discrimination between human epidermal growth factor receptor 2 (HER2)-low-expressing and HER2-overexpressing breast cancers: a comparative study of four MRI diffusion models.

OBJECTIVES: To determine the value of conventional DWI, continuous-time random walk (CTRW), fractional order calculus (FROC), and stretched exponential model (SEM) in discriminating human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC). METHODS: This prospective study included 158 women who underwent DWI, CTRW, FROC, and SEM and were pathologically categorized into the HER2-zero-expressing group (n = 10), HER2-low-expressing group (n = 86), and HER2-overexpressing group (n = 62). Nine diffusion parameters, namely ADC, αCTRW, ßCTRW, DCTRW, ßFROC, DFROC, µFROC, αSEM, and DDCSEM of the primary tumor, were derived from four diffusion models. These diffusion metrics and clinicopathologic features were compared between groups. Logistic regression was used to determine the optimal diffusion metrics and clinicopathologic variables for classifying the HER2-expressing statuses. Receiver operating characteristic (ROC) curves were used to evaluate their discriminative ability. RESULTS: The estrogen receptor (ER) status, progesterone receptor (PR) status, and tumor size differed between HER2-low-expressing and HER2-overexpressing groups (p < 0.001 to p = 0.009). The αCTRW, DCTRW, ßFROC, DFROC, µFROC, αSEM, and DDCSEM were significantly lower in HER2-low-expressing BCs than those in HER2-overexpressing BCs (p < 0.001 to p = 0.01). Further multivariable logistic regression analysis showed that the αCTRW was the single best discriminative metric, with an area under the curve (AUC) being higher than that of ADC (0.802 vs. 0.610, p < 0.05); the addition of ER status, PR status, and tumor size to the αCTRW improved the AUC to 0.877. CONCLUSIONS: The αCTRW could help discriminate the HER2-low-expressing and HER2-overexpressing BCs. CLINICAL RELEVANCE STATEMENT: Human epidermal growth factor receptor 2 (HER2)-low-expressing breast cancer (BC) might also benefit from the HER2-targeted therapy. Prediction of HER2-low-expressing BC or HER2-overexpressing BC is crucial for appropriate management. Advanced continuous-time random walk diffusion MRI offers a solution to this clinical issue. KEY POINTS: • Human epidermal receptor 2 (HER2)-low-expressing BC had lower αCTRW, DCTRW, ßFROC, DFROC, µFROC, αSEM, and DDCSEM values compared with HER2-overexpressing breast cancer. • The αCTRW was the single best diffusion metric (AUC = 0.802) for discrimination between the HER2-low-expressing and HER2-overexpressing breast cancers. • The addition of αCTRW to the clinicopathologic features (estrogen receptor status, progesterone receptor status, and tumor size) further improved the discriminative ability.

Prediction of histopathologic grades of myxofibrosarcoma with radiomics based on magnetic resonance imaging.

PURPOSE: To develop a radiomics-based model from preoperative magnetic resonance imaging (MRI) for predicting the histopathological grades of myxofibrosarcoma. METHODS: This retrospective study included 54 patients. The tumors were classified into high-grade and low-grade myxofibrosarcoma. The tumor size, signal intensity heterogeneity, margin, and surrounding tissue were evaluated on MRI. Using the least absolute shrinkage and selection operator (LASSO) algorithms, 1037 radiomics features were obtained from fat-suppressed T2-weighted images (T2WI), and a radiomics signature was established. Using multivariable logistic regression analysis, three models were built to predict the histopathologic grade of myxofibrosarcoma. A radiomics nomogram represents the integrative model. The three models' performance was evaluated using the receiver operating characteristics (ROC) and calibration curves. RESULTS: The high-grade myxofibrosarcoma had greater depth (P = 0.027), more frequent heterogeneous signal intensity at T2WI (P = 0.015), and tail sign (P = 0.014) than the low-grade tumor. The area under curve (AUC) of these conventional MRI features models was 0.648, 0.656, and 0.668, respectively. Seven radiomic features were selected by LASSO to construct the radiomics signature model, with an AUC of 0.791. The AUC of the integrative model based on radiomics signature and conventional MRI features was 0.875. The integrative model's calibration curve and insignificant Hosmer-Lemeshow test statistic (P = 0.606) revealed good calibration. CONCLUSION: An integrative model using radiomics signature and three conventional MRI features can preoperatively predict low- or high-grade myxofibrosarcoma.

Detecting lesion-specific ischemia in patients with coronary artery disease with computed tomography fractional flow reserve measured at different sites.

OBJECTIVES: Whether a stenosis can cause hemodynamic lesion-specific ischemia is critical for the treatment decision in patients with coronary artery disease (CAD). Based on coronary computed tomography angiography (CCTA), CT fractional flow reserve (FFRCT) can be used to assess lesion-specific ischemia. The selection of an appropriate site along the coronary artery tree is vital for measuring FFRCT. However the optimal site to measure FFRCT for a target stenosis remains to be adequately determined. The purpose of this study was to determine the optimal site to measure FFRCT for a target lesion in detecting lesion-specific ischemia in CAD patients by evaluating the performance of FFRCT measured at different sites distal to the target lesion in detecting lesion-specific ischemia with FFR measured with invasive coronary angiography (ICA) as reference standard. METHODS: In this single-center retrospective cohort study, a total of 401 patients suspected of having CAD underwent invasive ICA and FFR between March 2017 and December 2021 were identified. 52 patients having both CCTA and invasive FFR within 90 days were enrolled. Patients with vessels 30%-90% diameter stenosis as determined by ICA were referred to invasive FFR evaluation, which was performed 2-3 cm distal to the stenosis under the condition of hyperemia. For each vessel with 30%-90% diameter stenosis, if only one stenosis was present, this stenosis was selected as the target lesion; if serial stenoses were present, the stenosis most distal to the vessel end was chosen as the target lesion. FFRCT was measured at four sites: 1 cm, 2 cm, and 3 cm distal to the lower border of the target lesion (FFRCT-1 cm, FFRCT-2 cm, FFRCT-3 cm), and the lowest FFRCT at the distal vessel tip (FFRCT-lowest). The normality of quantitative data was assessed using the Shapiro-Wilk test. Pearson's correlation analysis and Bland-Altman plots were used for assessing the correlation and difference between invasive FFR and FFRCT. Correlation coefficients derived from Chi-suqare test were used to assess the correlation between invasive FFR and the cominbaiton of FFRCT measred at four sites. The performances of significant obstruction stenosis (diameter stenosis ≥ 50%) at CCTA and FFRCT measured at the four sites and their combinations in diagnosing lesion-specific ischemia were evaluated by receiver-operating characteristic (ROC) curves using invasive FFR as the reference standard. The areas under ROC curves (AUCs) of CCTA and FFRCT were compared by the DeLong test. RESULTS: A total of 72 coronary arteries in 52 patients were included for analysis. Twenty-five vessels (34.7%) had lesion-specific ischemia detected by invasive FFR and 47 vesseles (65.3%) had no lesion-spefifice ischemia. Good correlation was found between invasive FFR and FFRCT-2 cm and FFRCT-3 cm (r = 0.80, 95% CI, 0.70 to 0.87, p < 0.001; r = 0.82, 95% CI, 0.72 to 0.88, p < 0.001). Moderate correlation was found between invasive FFR and FFRCT-1 cm and FFRCT-lowest (r = 0.77, 95% CI, 0.65 to 0.85, p < 0.001; r = 0.78, 95% CI, 0.67 to 0.86, p < 0.001). FFRCT-1 cm + FFRCT-2 cm, FFRCT-2 cm + FFRCT-3 cm, FFRCT-3 cm + FFRCT-lowest, FFRCT-1 cm + FFRCT-2 cm + FFRCT-3 cm, and FFRCT-2 cm + FFRCT-3 cm + FFRCT-lowest were correatled with invasive FFR (r = 0.722; 0.722; 0.701; 0.722; and 0.722, respectively; p < 0.001 for all). Bland-Altman plots revealed a mild difference between invasive FFR and the four FFRCT (invasive FFR vs. FFRCT-1 cm, mean difference -0.0158, 95% limits of agreement: -0.1475 to 0.1159; invasive FFR vs. FFRCT-2 cm, mean difference 0.0001, 95% limits of agreement: -0.1222 to 0.1220; invasive FFR vs. FFRCT-3 cm, mean difference 0.0117, 95% limits of agreement: -0.1085 to 0.1318; and invasive FFR vs. FFRCT-lowest, mean difference 0.0343, 95% limits of agreement: -0.1033 to 0.1720). AUCs of CCTA, FFRCT-1 cm, FFRCT-2 cm, FFRCT-3 cm, and FFRCT-lowest in detecting lesion-specific ischemia were 0.578, 0.768, 0.857, 0.856 and 0.770, respectively. All FFRCT had a higher AUC than CCTA (all p < 0.05), FFRCT-2 cm achieved the highest AUC at 0.857. The AUCs of FFRCT-2 cm and FFRCT-3 cm were comparable (p > 0.05). The AUCs were similar between FFRCT-1 cm + FFRCT-2 cm, FFRCT-3 cm + FFRCT-lowest and FFRCT-2 cm alone (AUC = 0.857, 0.857, 0.857, respectively; p > 0.05 for all). The AUCs of FFRCT-2 cm + FFRCT-3 cm, FFRCT-1 cm + FFRCT-2 cm + FFRCT-3 cm, FFRCT-and 2 cm + FFRCT-3 cm + FFRCT-lowest (0.871, 0.871, 0.872, respectively) were slightly higher than that of FFRCT-2 cm alone (0.857), but without significnacne differences (p > 0.05 for all). CONCLUSIONS: FFRCT measured at 2 cm distal to the lower border of the target lesion is the optimal measurement site for identifying lesion-specific ischemia in patients with CAD.

Fabrication and characterizations of Zn-doped SnO2-Ti4O7 anode for electrochemical degradation of hexafluoropropylene oxide dimer acid and its homologues.

Hexafluoropropylene oxide dimer acid (HFPO-DA) and its homologues, as perfluorinated ether alkyl substances with strong antioxidant properties, have rarely been reported by electrooxidation processes to achieve good results. Herein, we report the use of an oxygen defect stacking strategy to construct Zn-doped SnO2-Ti4O7 for the first time and enhance the electrochemical activity of Ti4O7. Compared with the original Ti4O7, the Zn-doped SnO2-Ti4O7 showed a 64.4% reduction in interfacial charge transfer resistance, a 17.5% increase in the cumulative rate of •OH generation, and an enhanced oxygen vacancy concentration. The Zn-doped SnO2-Ti4O7 anode exhibited high catalytic efficiency of 96.4% for HFPO-DA within 3.5 h at 40 mA/cm2. Hexafluoropropylene oxide trimer and tetramer acid exhibit more difficult degradation due to the protective effect of the -CF3 branched chain and the addition of the ether oxygen atom leading to a significant increase in the C-F bond dissociation energy. The degradation rates of 10 cyclic degradation experiments and the leaching concentrations of Zn and Sn after 22 electrolysis experiments demonstrated the good stability of the electrodes. In addition, the aqueous toxicity of HFPO-DA and its degradation products was evaluated. This study analyzed the electrooxidation process of HFPO-DA and its homologues for the first time, and provided some new insights.

Ginsenosides Rc, as a novel SIRT6 activator, protects mice against high fat diet induced NAFLD.

Background: Hepatic lipid disorder impaired mitochondrial homeostasis and intracellular redox balance, triggering development of non-alcohol fatty liver disease (NAFLD), while effective therapeutic approach remains inadequate. Ginsenosides Rc has been reported to maintain glucose balance in adipose tissue, while its role in regulating lipid metabolism remain vacant. Thus, we investigated the function and mechanism of ginsenosides Rc in defending high fat diet (HFD)-induced NAFLD. Methods: Mice primary hepatocytes (MPHs) challenged with oleic acid & palmitic acid were used to test the effects of ginsenosides Rc on intracellular lipid metabolism. RNAseq and molecular docking study were performed to explore potential targets of ginsenosides Rc in defending lipid deposition. Wild type and liver specific sirtuin 6 (SIRT6, 50721) deficient mice on HFD for 12 weeks were subjected to different dose of ginsenosides Rc to determine the function and detailed mechanism in vivo. Results: We identified ginsenosides Rc as a novel SIRT6 activator via increasing its expression and deacetylase activity. Ginsenosides Rc defends OA&PA-induced lipid deposition in MPHs and protects mice against HFD-induced metabolic disorder in dosage dependent manner. Ginsenosides Rc (20mg/kg) injection improved glucose intolerance, insulin resistance, oxidative stress and inflammation response in HFD mice. Ginsenosides Rc treatment accelerates peroxisome proliferator activated receptor alpha (PPAR-α, 19013)-mediated fatty acid oxidation in vivo and in vitro. Hepatic specific SIRT6 deletion abolished ginsenoside Rc-derived protective effects against HFD-induced NAFLD. Conclusion: Ginsenosides Rc protects mice against HFD-induced hepatosteatosis by improving PPAR-α-mediated fatty acid oxidation and antioxidant capacity in a SIRT6 dependent manner, and providing a promising strategy for NAFLD.

The value of quantitative plaque analysis based on coronary computed tomography angiography in predicting the percutaneous coronary intervention outcome of chronic total occlusion lesions.

Background: Due to the uncertainty of the success of percutaneous coronary intervention (PCI) and the complexity of selecting suitable treatment cases, the interventional outcome of coronary chronic total occlusion (CTO) remains challenging. The purpose of this study was to evaluate the role of quantitative plaque analysis based on coronary computed tomography angiography (CCTA) in predicting the CTO-PCI outcome. Methods: We retrospectively included 78 patients with CTO (80 lesions) confirmed by invasive coronary angiography from July 2016 to December 2018. All patients underwent PCI treatment according to standard practice. A total of 47 lesions in 47 patients were successfully treated with PCI. PCI failed in the remaining 33 lesions in 31 patients. The following conventional CCTA morphologic parameters were evaluated and compared between the PCI-success and PCI-failure groups: stump morphology; occlusion length, tortuous course; CTO lesion calcium; bridging collateral vessel; retrograde collateral vessel; the appearance of the occluded distal segment; and quantitative CTO plaque characteristics, including total plaque volume, calcified plaque (CP) volume, noncalcified plaque (NCP) volume, low-density noncalcified plaque (LDNCP) volume, and plaque length. Univariate and multivariate logistic regression analyses were performed to determine independent parameters predictive of CTO-PCI outcomes. The predictive performances were assessed using receiver operating characteristic curve analysis. Results: The blunt stump was the only independent CCTA morphologic parameter to predict the outcome of CTO-PCI [odds ratio (OR): 10.807; P<0.001]. NCP volume (OR: 1.018; P<0.001), CP volume (OR: 1.026; P=0.049), and plaque length (OR: 1.058; P=0.037) were independent quantitative CTO plaque characteristics predictive of CTO-PCI outcomes. The plaque-based model combining NCP volume with CP volume and plaque length had a higher area under the curve (AUC =0.96) than did the morphology-based model that included blunt stump (AUC 0.68) in predicting the outcomes of CTO-PCI (P<0.001). Conclusions: The CCTA-based plaque characteristics, including NCP volume, CP volume, and plaque length, outperformed morphologic parameters in predicting the CTO-PCI outcomes.

Short Peptide Nanofiber Biomaterials Ameliorate Local Hemostatic Capacity of Surgical Materials and Intraoperative Hemostatic Applications in Clinics.

Short designer self-assembling peptide (dSAP) biomaterials are a new addition to the hemostat group. It may provide a diverse and robust toolbox for surgeons to integrate wound microenvironment with much safer and stronger hemostatic capacity than conventional materials and hemostatic agents. Especially in noncompressible torso hemorrhage (NCTH), diffuse mucosal surface bleeding, and internal medical bleeding (IMB), with respect to the optimal hemostatic formulation, dSAP biomaterials are the ingenious nanofiber alternatives to make bioactive neural scaffold, nasal packing, large mucosal surface coverage in gastrointestinal surgery (esophagus, gastric lesion, duodenum, and lower digestive tract), epicardiac cell-delivery carrier, transparent matrix barrier, and so on. Herein, in multiple surgical specialties, dSAP-biomaterial-based nano-hemostats achieve safe, effective, and immediate hemostasis, facile wound healing, and potentially reduce the risks in delayed bleeding, rebleeding, post-operative bleeding, or related complications. The biosafety in vivo, bleeding indications, tissue-sealing quality, surgical feasibility, and local usability are addressed comprehensively and sequentially and pursued to develop useful surgical techniques with better hemostatic performance. Here, the state of the art and all-round advancements of nano-hemostatic approaches in surgery are provided. Relevant critical insights will inspire exciting investigations on peptide nanotechnology, next-generation biomaterials, and better promising prospects in clinics.

The Value of Myocardial Fibrosis Parameters Derived from Cardiac Magnetic Resonance Imaging in Risk Stratification for Patients with Hypertrophic Cardiomyopathy.

RATIONALE AND OBJECTIVES: The aim of the study was to determine whether myocardial fibrosis parameters of cardiac magnetic resonance imaging (MRI) has added value in the risk stratification of hypertrophic cardiomyopathy (HCM) patients. MATERIALS AND METHODS: In this retrospective study, 108 patients with HCM (mean age ± standard deviation, 55.5 ± 13.4 years) were included from January 2019 to April 2022, and were followed up for 2 years to record sudden cardiac death (SCD) adverse events. All HCM patients underwent cardiac MRI and were divided into a training cohort (n = 81; mean age, 56.1 ± 13.0 years) and a validation cohort (n = 27; mean age, 57.8 ± 13.9 years). According to the presence of SCD risk factors defined by the 2020 AHA/ACC guidelines, HCM patients were classified into low-risk and high-risk groups. Cardiac MRI features, including late gadolinium enhancement (LGE), T1 mapping, and extracellular volume fraction (ECV), were assessed and compared between the two groups. Logistic regression analysis was used to select the optimal predictors of SCD from cardiac MRI features and HCM Risk-SCD score to construct prediction models. Receiver operating curve (ROC) analysis was used to assess the predictive performance of the constructed prediction model. Cox regression analysis was also used to determine the optimal predictors of SCD adverse events. RESULTS: Multivariate logistic analysis showed that the global ECV was the single myocardial fibrosis parameter predictive of the risk of SCD (p < 0.001). The areas under the ROC curves (AUC) of global ECV were higher than those of LGE, global native T1, global postcontrast T1, and HCM Risk-SCD (AUC = 0.85 vs. 0.74, 0.77, 0.63, 0.78). An integrative risk stratification model combining global ECV (odds ratio, 1.36 [95% CI: 1.16-1.60]; p < 0.001) and HCM Risk-SCD score (odds ratio, 1.63 [95% CI: 1.08-2.47]; p < 0.001) achieved an AUC of 0.89 (95% CI: 0.81-0.96) in the training cohort, which was significantly higher than that of HCM Risk-SCD score alone (p = 0.03). The AUC of the integrative model was 0.93 (95% CI: 0.84-1.00) in the validation cohort. Multivariate Cox regression analysis also showed that the global ECV was an independent predictor of SCD adverse events (hazard ratio, 1.27 [95% CI: 1.10-1.47]). CONCLUSION: The ECV derived from cardiac MRI is comparable to the HCM Risk-SCD scale in predicting the SCD risk stratification in patients with HCM.

N6-methylandenosine-related immune genes correlate with prognosis and immune landscapes in gastric cancer.

Objectives: This study aimed to probe into the significance of N6-methyladenosine (m6A)-related immune genes (m6AIGs) in predicting prognoses and immune landscapes of patients with gastric cancer (GC). Methods: The clinical data and transcriptomic matrix of GC patients were acquired from The Cancer Genome Atlas database. The clinically meaningful m6AIGs were acquired by univariate Cox regression analysis. GC patients were stratified into different clusters via consensus clustering analysis and different risk subgroups via m6AIGs prognostic signature. The clinicopathological features and tumor microenvironment (TME) in the different clusters and different risk subgroups were explored. The predictive performance was evaluated using the KM method, ROC curves, and univariate and multivariate regression analyses. Moreover, we fabricated a nomogram based on risk scores and clinical risk characteristics. Biological functional analysis was performed based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The connectivity map was used to screen out potential small molecule drugs for GC patients. Results: A total of 14 prognostic m6AIGs and two clusters based on 14 prognostic m6AIGs were identified. A prognostic signature based on 4 m6AIGs and a nomogram based on independent prognostic factors was constructed and validated. Different clusters and different risk subgroups were significantly correlated with TME scores, the distribution of immune cells, and the expression of immune checkpoint genes. Some malignant and immune biological processes and pathways were correlated with the patients with poor prognosis. Ten small molecular drugs with potential therapeutic effect were screened out. Conclusions: This study revealed the prognostic role and significant values of m6AIGs in GC, which enhanced the understanding of m6AIGs and paved the way for developing predictive biomarkers and therapeutic targets for GC.

Muscle mass loss is associated with physical dysfunction in patients with early rheumatoid arthritis.

Background: Muscle mass loss is common in long-standing rheumatoid arthritis (RA). The aim was to explore the prevalence and effects of RA disease characteristics in patients with early RA. Methods: This cross-sectional study was carried out based on a Chinese RA cohort and control subjects. The body composition (BC) was assessed using bioelectric impedance analysis. Myopenia was defined by an appendicular skeletal muscle mass index of ≤ 7.0 kg/m2 in men and ≤ 5.7 kg/m2 in women. Physical dysfunction was defined as a health assessment questionnaire disability index > 1. Propensity score matching was performed to balance age and gender differences among patients with early RA (disease duration ≤ 12 months) and established RA, and controls (with 1:3:3 matching). Results: In total, 2017 controls and 1,008 patients with RA were recruited for this study. Among the patients with RA, there were 190 (18.8%) patients with early RA, with a median disease duration of 7 (4, 11) months. The matched patients with early RA (n = 160) showed a higher prevalence of myopenia than the matched controls (41.3 vs. 15.8%, P < 0.0167), but no difference was found in the matched patients with established RA (41.3 vs. 50.4%, P > 0.0167). Compared with the patients with established RA, the patients with early RA exhibited higher disease activity scores [disease activity score in 28 joints with four variables including C-reactive protein (DAS28-CRP): median 4.76 vs. 3.93, P < 0.001] and a higher prevalence of physical dysfunction (26.3 vs. 19.4%, P = 0.035). In the patients with early RA, patients with myopenia showed a higher prevalence of physical dysfunction than those without myopenia (41.3 vs. 15.5%, P < 0.001), among which walking and common daily activities were the most involved subdimensions. Multivariate logistic regression analysis showed that DAS28-CRP was positively associated with myopenia [adjusted odds ratio (AOR) 1.558, 95% CI (1.138-2.132)], and myopenia [AOR 2.983, 95% CI (1.192-7.465)] was independently associated with physical dysfunction in the patients with early RA. Conclusion: Our data indicate the importance of early detection of muscle involvement in the early stage of RA and imply the significance of early aggressive control of disease activity for the prevention of myopenia and physical dysfunction in patients with early RA. Our study provides a new perspective on RA management.

Myokine myostatin is a novel predictor of one-year radiographic progression in patients with rheumatoid arthritis: A prospective cohort study.

Background: Associations between rheumatoid arthritis (RA) and reduced skeletal muscle have been studied, and we firstly reported myopenia independently predict one-year radiographic progression in RA. Myokine myostatin can negatively regulate skeletal muscle mass and promote osteoclast differentiation. However, there is no report about their relationships in RA patients. We firstly explored the relationship of serum myostatin and disease characteristics, as well as aggravated joint destruction during one-year follow-up. Methods: Consecutive RA patients were recruited from a real-world prospective cohort and completed at least one-year follow-up. Baseline serum level of myostatin was measured by enzyme-linked immunosorbent assay. Clinical data in RA patients as well as muscle index in both RA patients and healthy controls were collected. One-year radiographic progression as primary outcome was defined by a change in the total Sharp/van der Heijde modified score ≥0.5 units. Results: Totally 344 RA patients (age 47.9 ± 12.5 years, 84.0% female) and 118 healthy control subjects (age 42.8 ± 11.3 years, 74.6% female) were recruited. Compared with healthy controls, RA patients showed a higher level of serum myostatin at baseline (3.241 ± 1.679 ng/ml vs. 1.717 ± 0.872 ng/ml, P<0.001), although lower appendicular skeletal muscle mass index (ASMI, 6.0 ± 0.9 kg/m2 vs. 6.5 ± 1.0 kg/m2, P<0.001). In RA patients, those with high myostatin level showed a higher rate of radiographic progression than low myostatin group (45.3% vs. 18.6%, P<0.001). Furtherly, RA patients were stratified into four subgroups according to serum myostatin and myopenia. Compared with other three subgroups, RA patients with high myostatin overlapping myopenia had the highest rate of radiographic progression (67.2% vs. 10.3%-31.4%, P<0.001), as well as the lowest proportion of remission and the highest rate of physical dysfunction during one-year follow-up. After adjustment for confounding factors, high serum myostatin (AOR=3.451, 95%CI: 2.016-5.905) and myopenia (AOR=2.387, 95%CI: 1.416-4.022) at baseline were risk factors for one-year radiographic progression, especially for those with high myostatin overlapping myopenia (AOR=10.425, 95%CI: 3.959-27.450) as the highest-risk individuals among four subgroups. Significant synergistic interaction effect was observed between high myostatin and myopenia on one-year radiographic progression (AP=66.3%, 95%CI: 43.2%-89.3%). Conclusion: Myostatin is a novel predictor of aggravated joint destruction in RA patients which has synergistic interaction with myopenia for predicting value.

Radiomics based on magnetic resonance imaging for preoperative prediction of lymph node metastasis in head and neck cancer: Machine learning study.

BACKGROUND: In this study, we use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics approach for evaluation of cervical lymph node (CLN) status. METHODS: After collecting all patients' MRI images, we used CLN radiomic features, the apparent diffusion coefficients (ADC) from diffusion-weighted imaging (DWI), and lymph node short diameter of the CLN to build MRI model to predict the status of the CLN. RESULTS: One hundred and twenty cases met inclusion criteria. The MRI model including the radiomic features, ADC, and lymph node size of the CLN achieved better performance for CLN status prediction with the area under the receiver operating characteristic (ROC) curve (AUC) of 0.83. CONCLUSIONS: The multiomic signature of MRI radiomics, ADC, and lymph node size of CLNs has high predictive value for the status of CLNs. This model has provided scientific value to the surgeon regarding cervical lymph nodes before surgery.

A clinical practice guideline for the screening and assessment of enthesitis in patients with spondyloarthritis.

Objective: The aim of this review is to provide guidance on the selection of approaches to the screening and assessment of enthesitis in patients with spondyloarthritis (SpA). Methods: Twenty-four questions regarding the approaches to the screening and assessment of enthesitis and the implementation details were devised, followed by a systemic literature review. The Grading of Recommendations Assessment, Development, and Evaluation methodology was employed in the development of this guideline, with modifications to evaluate non-interventional approaches under comprehensive consideration of costs, accessibility, and evidence strength. A consensus from the voting panel was required for the inclusion of the final recommendations and the strength of each recommendation. Results: Seventeen recommendations (including five strong recommendations) were included in this guideline. The voting panel expressed unequivocal support for the necessity of screening and assessment of enthesitis in patients with SpA. It was agreed unanimously that symptom evaluation and physical examination should serve as the initial steps to the recognition of enthesitis, whereas Maastricht Ankylosing Spondylitis Enthesitis Score is a reliable tool in both clinical trials and daily medical practice. Ultrasound examination is another reliable tool, with power Doppler ultrasound as an informative addition. Notwithstanding its high resolution, MRI is limited by the costs and relatively low accessibility, whereas radiographs had low sensitivity and therefore should be rendered obsolete in the assessment of enthesitis. PET/CT was strongly opposed in the detection of enthesitis. Conclusion: This guideline provides clinicians with information regarding the screening and assessment of enthesitis in patients with SpA. However, this guideline does not intend on dictating choices, and the ultimate decisions should be made in light of the actual circumstances of the facilities.

ADC and kinetic parameter of primary tumor: Surrogate imaging markers for fertility-sparing vaginal radical trachelectomy in patients with stage IB cervical cancer.

PURPOSE: To investigate the role of ADC and kinetic parameters derived from DCE-MRI in selecting eligible candidates for fertility-sparing vaginal radical trachelectomy (VRT). METHOD: Female patients with FIGO stage IB cervical cancers between March 2019 and January 2022 were retrospectively included. All patients underwent hysterectomy and bilateral lymphadenectomy. According to the surgical pathology, the study population was divided into VRT-eligible group and VRT-ineligible group. ADC, semi-quantitative and quantitative kinetic parameters of the primary tumor were compared between the two groups. Logistic regression analysis was used to determine the independent predictors for VRT eligibility and ROC curve was used to evaluate the predictive performance. RESULTS: 19 patients were deemed eligible for VRT and 50 were ineligible. Compared with VRT-eligible group, time to peak and ADC were significantly lower in VRT-ineligible group (P = 0.004 and 0.001, respectively) while volume fraction of plasma (Vp) was higher in VRT ineligible group (P = 0.001). ADC and Vp were independent predictors for VRT eligibility. Combining Vp and ADC yielded the highest area under the ROC curve of 0.853 compared with that of 0.766 for Vp and 0.764 for ADC, though marginal differences were found (P = 0.109 and 0.078, respectively). CONCLUSIONS: ADC and the kinetic DCE-MRI parameter Vp can be used as surrogate markers to select eligible candidates for fertility-sparing VRT.

Preoperative Prediction of the Aggressiveness of Oral Tongue Squamous Cell Carcinoma with Quantitative Parameters from Dual-Energy Computed Tomography.

Objectives: To determine whether quantitative parameters derived from dual-energy computed tomography (DECT) were predictive of the aggressiveness of oral tongue squamous cell carcinoma (OTSCC) including the pathologic stages, histologic differentiation, lymph node status, and perineural invasion (PNI). Methods: Between August 2019 and March 2021, 93 patients (mean age, 54.6 ± 13.8 years; 66 men) with pathologically diagnosed OTSCC were enrolled in this prospective study. Preoperative DECT was performed and quantitative parameters (e.g., slope of the spectral Hounsfield unit curve [λHu], normalized iodine concentration [nIC], normalized effective atomic number [nZeff], and normalized electron density [nRho]) were measured on arterial phase (AP) and venous phase (VP) DECT imaging. Quantitative parameters from DECT were compared between patients with different pathologic stages, histologic differentiation, lymph node statuses, and perineural invasion statuses. Logistic regression analysis was utilized to assess independent parameters and the diagnostic performance was analyzed by the receiver operating characteristic curves (ROC). Results: λHu and nIC in AP and λHu, nZeff, and nIC in VP were significantly lower in stage III-IV lesions than in stage I-II lesions (p < 0.001 to 0.024). λHu in VP was an independent predictor of tumor stage with an odds ratio (OR) of 0.29, and area under the curve (AUC) of 0.80. λHu and nIC were higher in well-differentiated lesions than in poorly differentiated lesions (p < 0.001 to 0.021). The nIC in VP was an independent predictor of histologic differentiation with OR of 0.31, and AUC of 0.78. λHu and nIC in VP were lower in OTSCCs with lymph node metastasis than those without metastasis (p < 0.001 to 0.005). λHu in VP was the independent predictor of lymph node status with OR of 0.42, and AUC of 0.74. No significant difference was found between OTSCCs without PNI and those with PNI in terms of the quantitative DECT parameters. Conclusion: DECT can be a complementary means for the preoperative prediction of the aggressiveness of OTSCC.