Fully automated deep learning system for osteoporosis screening using chest computed tomography images.

Background: Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs). Methods: In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated. Results: In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively. Conclusions: The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

Iron Metabolic Biomarkers and the Mortality Risk in the General Population: A Nationwide Population-Based Cohort Study.

Context: Iron is an essential element in the human body and plays a critical role in many physiological and cellular processes. However, the association between iron status and the risk of all-cause or cause-specific mortality has not been well-investigated. And it is unclear whether the association between iron metabolic biomarkers and the risk of mortality differs between people with and without diabetes mellitus (DM). Objective: This work aimed to investigate associations between iron metabolic biomarkers and all-cause and cause-specific mortality risk in the general population, and heterogeneities in the associations among population with and without DM.. Methods: A total of 29 166 adults from the National Health and Nutrition Examination Survey (NHANES) III and NHANES 1999 to 2010 were included, with linkage to the National Death Index to December 31, 2019. Cox proportional-hazard models and Fine-Gray subdistribution hazard models were used to estimate associations between iron metabolic biomarkers and outcomes. Results: During a median follow-up of 18.83 years, 9378 deaths were observed, including 3420 cardiovascular disease (CVD) deaths and 1969 cancer deaths. A significant linear association between serum ferritin (SF) and all-cause mortality was observed among the overall population and those without DM. J-shaped associations between transferrin saturation (TSAT) and all-cause and CVD mortality were observed among all populations. In the overall population, compared to the first quartile (Q1) group, the adjusted hazard ratio (HR) (95% CI) for all-cause mortality was 1.07 (1.00-1.15), 1.05 (0.98-1.12), 1.13 (1.05-1.21) in Q2, Q3, and Q4 groups for SF, while the HR was 0.94 (0.88-0.99), 0.92 (0.86-0.97), and 0.93 (0.88-0.99) for TSAT. In individuals without DM, the adjusted HR of the Q4 of SF were 1.19 (1.03-1.37) for CVD mortality and 1.25 (1.05-1.48) for cancer mortality. In individuals with DM, the adjusted HRs of the Q4 of TSAT were 0.76 (0.62-0.93) for CVD mortality and 1.47 (1.07-2.03) for cancer mortality. Conclusion: Iron metabolism abnormalities increase mortality risk in the general population. The associations of iron status with mortality were significantly different between individuals with and without DM, which indicated tailored strategies for iron homeostasis are needed.

Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics.

Chemotherapy alters the prognostic biomarker histopathological growth pattern (HGP) phenotype in colorectal liver metastases (CRLMs) patients. We aimed to develop a CT-based radiomics model to predict the transformation of the HGP phenotype after chemotherapy. This study included 181 patients with 298 CRLMs who underwent preoperative contrast-enhanced CT followed by partial hepatectomy between January 2007 and July 2022 at two institutions. HGPs were categorized as pure desmoplastic HGP (pdHGP) or non-pdHGP. The samples were allocated to training, internal validation, and external validation cohorts comprising 153, 65, and 29 CRLMs, respectively. Radiomics analysis was performed on pre-enhanced, arterial phase, portal venous phase (PVP), and fused images. The model was used to predict prechemotherapy HGPs in 112 CRLMs, and HGP transformation was analysed by comparing these findings with postchemotherapy HGPs determined pathologically. The prevalence of pdHGP was 19.8% (23/116) and 45.8% (70/153) in chemonaïve and postchemotherapy patients, respectively (P < 0.001). The PVP radiomics signature showed good performance in distinguishing pdHGP from non-pdHGPs (AUCs of 0.906, 0.877, and 0.805 in the training, internal validation, and external validation cohorts, respectively). The prevalence of prechemotherapy pdHGP predicted by the radiomics model was 33.0% (37/112), and the prevalence of postchemotherapy pdHGP according to the pathological analysis was 47.3% (53/112; P = 0.029). The transformation of HGP was bidirectional, with 15.2% (17/112) of CRLMs transforming from prechemotherapy pdHGP to postchemotherapy non-pdHGP and 30.4% (34/112) transforming from prechemotherapy non-pdHGP to postchemotherapy pdHGP (P = 0.005). CT-based radiomics method can be used to effectively predict the HGP transformation in chemotherapy-treated CRLM patients, thereby providing a basis for treatment decisions.

Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging.

BACKGROUND: The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging. METHODS: The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022. Initially, a deep learning (DL) model was developed to segment PDAC. Subsequently, radiomics features were extracted from the automatically segmented region of interest (ROI), which encompassed both the tumor region and a 3 mm surrounding area, to construct a predictive model for determining T4 stage of PDAC. The assessment of the models' performance involved the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The study encompassed a cohort of 509 PDAC patients, with a median age of 62 years (interquartile range: 55-67). The proportion of patients in T4 stage within the model was 16.9%. The model achieved an AUC of 0.849 (95% CI: 0.753-0.940), a sensitivity of 0.875, and a specificity of 0.728 in predicting T4 stage of PDAC. The performance of the model was determined to be comparable to that of two experienced abdominal radiologists (AUCs: 0.849 vs. 0.834 and 0.857). CONCLUSION: The automated AI pipeline utilizing tumor and peritumor-related radiomics features demonstrated comparable performance to that of senior abdominal radiologists in predicting T4 stage of PDAC.

Prediction of macrotrabecular-massive hepatocellular carcinoma by using MR-based models and their prognostic implications.

PURPOSE: To evaluate the efficacy of MRI-based radiomics and clinical models in predicting MTM-HCC. Additionally, to investigate the ability of the radiomics model designed for MTM-HCC identification in predicting disease-free survival (DFS) in patients with HCC. METHODS: A total of 336 patients who underwent oncological resection for HCC between June 2007 and March 2021 were included. 127 patients in Cohort1 were used for MTM-HCC identification, and 209 patients in Cohort2 for prognostic analyses. Radiomics analysis was performed using volumes of interest of HCC delineated on pre-operative MRI images. Radiomics and clinical models were developed using Random Forest algorithm in Cohort1 and a radiomics probability (RP) of MTM-HCC was obtained from the radiomics model. Based on the RP, patients in Cohort2 were divided into a RAD-MTM-HCC (RAD-M) group and a RAD-non-MTM-HCC (RAD-nM) group. Univariate and multivariate Cox regression analyses were employed to identify the independent predictors for DFS of patients in Cohort2. Kaplan-Meier curves were used to compare the DFS between different groups pf patients based on the predictors. RESULTS: The radiomics model for identifying MTM-HCC showed AUCs of 0.916 (95% CI: 0.858-0.960) and 0.833 (95% CI: 0.675-0.935), and the clinical model showed AUCs of 0.760 (95% CI: 0.669-0.836) and 0.704 (95% CI: 0.532-0.843) in the respective training and validation sets. Furthermore, the radiomics biomarker RP, portal or hepatic vein tumor thrombus, irregular rim-like arterial phase hyperenhancement (IRE) and AFP were independent predictors of DFS in patients with HCC. The DFS of RAD-nM group was significantly higher than that of the RAD-M group (p < .001). CONCLUSION: MR-based clinical and radiomic models have the potential to accurately diagnose MTM-HCC. Moreover, the radiomics signature designed to identify MTM-HCC also can be used to predict prognosis in patients with HCC, realizing the diagnostic and prognostic aims at the same time.

A 4D Printed Adhesive, Thermo-Contractile, and Degradable Hydrogel for Diabetic Wound Healing.

Chronic wound healing remains a substantial clinical challenge. Current treatments are often either prohibitively expensive or insufficient in meeting the various requirements needed for effective diabetic wound healing. A 4D printing multifunctional hydrogel dressing is reported here, which aligns perfectly with wounds owning various complex shapes and depths, promoting both wound closure and tissue regeneration. The hydrogel is prepared via digital light process (DLP) 3D printing of the mixture containing N-isopropylacrylamide (NIPAm), curcumin-loaded Pluronic F127 micelles (Cur-PF127), and poly(ethylene glycol) diacrylate-dopamine (PEGDA575-Do), a degradable crosslinker. The use of PEGDA575-Do ensures tissue adhesion and degradability, and cur-PF127 serves as an antibacterial agent. Moreover, the thermo-responsive mainchains (i.e., polymerized NIPAm) enables the activation of wound contraction by body temperature. The features of the prepared hydrogel, including robust tissue adhesion, temperature-responsive contraction, effective hemostasis, spectral antibacterial, biocompatibility, biodegradability, and inflammation regulation, contribute to accelerating diabetic wound healing in Methicillin-resistant Staphylococcus aureus (MRSA)-infected full-thickness skin defect diabetic rat models and liver injury mouse models, highlighting the potential of this customizable, mechanobiological, and inflammation-regulatory dressing to expedite wound healing in various clinical settings.

Effects of low nitrogen on seedling growth, photosynthetic characteristics and antioxidant system of rice varieties with different nitrogen efficiencies.

Nitrogen plays a significant role in influencing various physiological processes in plants, thereby impacting their ability to withstand abiotic stresses. This study used hydroponics to compare the effects of three nitrogen supply levels (1N, 1/2N and 1/4N) on the antioxidant capacity of rice varieties JJ88 (nitrogen efficient) and XN999 (nitrogen inefficient) with different nitrogen use efficiencies. The results show that compared with the XN999 variety, the JJ88 variety has stronger adaptability to low-nitrogen conditions, which is mainly reflected in the relatively small decrease in dry weight and net photosynthetic rate (Pn); In the early stage of low-nitrogen treatment (0-7 d), the [Formula: see text] production rate, hydrogen peroxide (H2O2) and malondialdehyde (MDA) content of JJ88 variety increased relatively slightly, but the superoxide dismutase (SOD), peroxide The activity of enzyme (POD) and catalase (CAT) increased significantly; After low-nitrogen treatment, the ASA-GSH cycle enzyme activity of JJ88 variety was relatively high, and the dehydroascorbate reductase (DHAR) activity after 14 days of low-nitrogen treatment was higher than that of 1N treatment; The content of reduced ascorbic acid (ASA) in non-enzymatic antioxidants was lower than that of 1N treatment after 14 days of low nitrogen treatment; The contents of oxidized dehydroascorbic acid (DHA) and carotenoids (Car) were higher than those of 1N treatment after 21d and 14d of low nitrogen treatment respectively; The contents of reduced glutathione (GSH), oxidized glutathione (GSSG) and proline (Pro) showed a larger upward trend during the entire low-nitrogen treatment period. In summary, the JJ88 rice variety has a strong ability to regulate oxidative stress and osmotic damage under low nitrogen conditions. It can slow down plant damage by regulating antioxidant enzyme activity and antioxidant content. This provides a basis for achieving nitrogen reduction and efficiency improvement in rice and the breeding of nitrogen-efficient varieties.

Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT.

PURPOSE: To investigate the clinical value of radiomic analysis on [18F]FDG and [18F]FLT PET on the differentiation of [18F]FDG-avid benign and malignant pulmonary nodules (PNs). METHODS: Data of 113 patients with inconclusive PNs based on preoperative [18F]FDG PET/CT who underwent additional [18F]FLT PET/CT scans within a week were retrospectively analyzed in the present study. Three methods of analysis including visual analysis, radiomic analysis based on [18F]FDG PET/CT images alone, and radiomic analysis based on dual-tracer PET/CT images were evaluated for differential diagnostic value of benign and malignant PNs. RESULTS: A total of 678 radiomic features were extracted from volumes of interest (VOIs) of 123 PNs. Fourteen valuable features were thereafter selected. Based on a visual analysis of [18F]FDG PET/CT images, the diagnostic accuracy, sensitivity, and specificity were 61.6%, 90%, and 28.8%, respectively. For the test set, the area under the curve (AUC), sensitivity, and specificity of the radiomic models based on [18F]FDG PET/CT plus [18F]FLT signature were equal or better than radiomics based on [18F]FDG PET/CT only (0.838 vs 0.810, 0.778 vs 0.778, 0.750 vs 0.688, respectively). CONCLUSION: Radiomic analysis based on dual-tracer PET/CT images is clinically promising and feasible for the differentiation between benign and malignant PNs. CLINICAL RELEVANCE STATEMENT: Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT. KEY POINTS: • Radiomics brings new insights into the differentiation of benign and malignant pulmonary nodules beyond the naked eyes. • Dual-tracer imaging shows the biological behaviors of cancerous cells from different aspects. • Radiomics helps us get to the histological view in a non-invasive approach.

A Machine Learning-Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using T2-Weighted and Diffusion-Weighted MRI.

BACKGROUND: Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guidelines, but its long examination time and use of contrast material remains concerning. PURPOSE: To develop an unenhanced radiomics model with using non-gadolinium based sequences for detecting breast cancer based on T2-weighted (T2W) and diffusion-weighted (DW) MRI. STUDY TYPE: Retrospective analysis followed by retrospective and prospective cohorts study. POPULATION: 1760 patients: Of these, 1293 for model construction (n = 775 for training and 518 for validation). The remaining patients for model testing in internal retrospective (n = 167), internal prospective (n = 188), and external retrospective (n = 112) cohorts. FIELD STRENGTH/SEQUENCE: 3.0T MR scanners from two institution. T2WI, DWI, and first contrast-enhanced T1-weighted sequence. ASSESSMENT: AUCs in distinguishing breast cancer were compared between combined model with gadolinium agent sequence and unenhanced model. Subsequently, the AUCs in testing cohorts of unenhanced model was compared with two radiologists' diagnosis for this research. Finally, patient subgroup analysis in testing cohorts was performed based on clinical subgroups and different types of malignancies. STATISTICAL TESTS: Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, weighted kappa test, and DeLong's test. RESULTS: The unenhanced radiomics model performed best under Gaussian process (GP) classifiers (AUC: training, 0.893; validation, 0.848) compared to support vector machine (SVM) and logistic, showing favorable prediction in testing cohorts (AUCs, 0.818-0.840). The AUCs for the unenhanced radiomics model were not statistically different in five cohorts from those of the combined radiomics model (P, 0.317-0.816), as well as the two radiologists (P, 0.181-0.918). The unenhanced radiomics model was least successful in identifying ductal carcinoma in situ, whereas did not show statistical significance in other subgroups. DATA CONCLUSION: An unenhanced radiomics model based on T2WI and DWI has comparable diagnostic accuracy to the combined model using the gadolinium agent. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma.

OBJECTIVES: This study aimed to develop and validate radiomics models on the basis of computed tomography (CT) and clinical features for the prediction of pulmonary metastases (MT) in patients with Ewing sarcoma (ES) within 2 years after diagnosis. MATERIALS AND METHODS: A total of 143 patients with a histopathological diagnosis of ES were enrolled in this study (114 in the training cohort and 29 in the validation cohort). The regions of interest (ROIs) were handcrafted along the boundary of each tumor on the CT and CT-enhanced (CTE) images, and radiomic features were extracted. Six different models were built, including three radiomics models (CT, CTE and ComB models) and three clinical-radiomics models (CT_clinical, CTE_clinical and ComB_clinical models). The area under the receiver operating characteristic curve (AUC), and accuracy were calculated to evaluate the different models, and DeLong test was used to compare the AUCs of the models. RESULTS: Among the clinical risk factors, the therapeutic method had significant differences between the MT and non-MT groups (P＜0.01). The six models performed well in predicting pulmonary metastases in patients with ES, and the ComB model (AUC: 0.866/0.852 in training/validation cohort) achieved the highest AUC among the six models. However, no statistically significant difference was observed between the AUC of the models. CONCLUSIONS: In patients with ES, clinical-radiomics model created using radiomics signature and clinical features provided favorable ability and accuracy for pulmonary metastases prediction.

Postoperative Relapse Prediction in Patients With Ewing Sarcoma Using Computed Tomography-Based Radiomics Models Covering Tumor Per Se and Peritumoral Signatures.

OBJECTIVE: We aimed to develop and validate a computed tomography (CT)-based radiomics model for early relapse prediction in patients with Ewing sarcoma (ES). METHODS: We recruited 104 patients in this study. Tumor areas and areas with a tumor expansion of 3 mm were used as regions of interest for radiomics analysis. Six different models were constructed: Pre-CT, CT enhancement (CTE), Pre-CT +3 mm , CTE +3 mm , Pre-CT and CTE combined (ComB), and Pre-CT +3 mm and CTE +3 mm combined (ComB +3 mm ). All 3 classifiers used a grid search with 5-fold cross-validation to identify their optimal parameters, followed by repeat 5-fold cross-validation to evaluate the model performance based on these parameters. The average performance of the 5-fold cross-validation and the best one-fold performance of each model were evaluated. The AUC (area under the receiver operating characteristic curve) and accuracy were calculated to evaluate the models. RESULTS: The 6 radiomics models performed well in predicting relapse in patients with ES using the 3 classifiers; the ComB and ComB +3 mm models performed better than the other models (AUC -best : 0.820-0.922/0.823-0.833 and 0.799-0.873/0.759-0.880 in the training and validation cohorts, respectively). Although the Pre-CT +3 mm , CTE +3 mm, and ComB +3 mm models covering tumor per se and peritumoral CT features preoperatively forecasted ES relapse, the model was not significantly improved. CONCLUSIONS: The radiomics model performed well for early recurrence prediction in patients with ES, and the ComB and ComB +3 mm models may be superior to the other models.

An MRI-based radiomics nomogram for differentiating spinal metastases from multiple myeloma.

BACKGROUND: Spinal metastasis and multiple myeloma share many overlapping conventional radiographic imaging characteristics, thus, their differentiation may be challenging. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the differentiation of spinal metastasis and multiple myeloma. MATERIALS AND METHODS: A total of 312 patients (training set: n = 146, validation set: n = 65, our center; external test set: n = 101, two other centers) with spinal metastasis (n = 196) and multiple myeloma (n = 116) were retrospectively enrolled. Demographics and MRI findings were assessed to build a clinical factor model. Radiomics features were extracted from MRI images. A radiomics model was constructed by the least absolute shrinkage and selection operator method. A radiomics nomogram combining the radiomics signature and independent clinical factors was constructed. And, one experienced radiologist reviewed the MRI images for all case. The diagnostic performance of the different models was evaluated by receiver operating characteristic curves. RESULTS: A clinical factors model was built based on heterogeneous appearance and shape. Twenty-one features were used to build the radiomics signature. The area under the curve (AUC) values of the radiomics nomogram (0.853 and 0.762, respectively) were significantly higher than that of the clinical factor model (0.692 and 0.540, respectively) in both validation (p = 0.048) and external test (p < 0.001) sets. The AUC values of the radiomics nomogram model were higher than that of radiologist in training, validation and external test sets (all p < 0.05). Moreover, no significant difference in AUC values of radiomics nomogram model was found between the validation set and external test set (p = 0.212). CONCLUSION: The radiomics nomogram can differentiate spinal metastasis and multiple myeloma with a moderate to good performance, and may be as a valuable method to assist in the clinical diagnosis and preoperative decision-making.

Combined effect of simulated microgravity and low-dose ionizing radiation on structure and antibiotic resistance of a synthetic community model of bacteria isolated from spacecraft assembly room.

Understanding the structural and antibiotic resistance changes of microbial communities in space environments is critical for identifying potential pathogens that may pose health risks to astronauts and for preventing and controlling microbial contamination. The research to date on microbes under simulated space factors has primarily been carried out on single bacterial species under the individual effects of microgravity or low-dose radiation. However, microgravity (MG) and low-dose ionizing radiation (LDIR) coexist in the actual spacecraft environment, and microorganisms coexist as communities in the spacecraft environment. Thus, the microbial response to the real changes present during space habitation has not been adequately explored. To address this knowledge gap, we compared the dynamics of community composition and antibiotic resistance of synthetic bacterial communities under simulated microgravit, low-dose ionizing radiation, and the conditions combined, as it occurs in spacecraft. To ensure representative bacteria were selected, we co-cultured of 12 bacterial strains isolated from spacecraft cleanrooms. We found that the weakened competition between communities increased the possibility of species coexistence, community diversity, and homogeneity. The number of Bacilli increased significantly, while different species under the combined conditions showed various changes in abundance compared to those under the individual conditions. The resistance of the synthetic community to penicillins increased significantly under low doses of ionizing radiation but did not change significantly under simulated microgravity or the combined conditions. The results of functional predictions revealed that antibiotic biosynthesis and resistance increased dramatically in the community under space environmental stress, which confirmed the results of the drug sensitivity assays. Our results show that combined space environmental factors exert different effects on the microbial community structure and antibiotic resistance, which provides new insights into our understanding of the mechanisms of evolution of microorganisms in spacecraft, and is relevant to effective microbial pollution prevention and control strategies.

Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma.

OBJECTIVES: Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the important factors affecting the long-term survival of OS. To develop and validate machine learning radiomics model based on radiographic and clinical features that could predict LM in OS within 3 years. METHODS: 486 patients (LM = 200, non-LM = 286) with histologically proven OS were retrospectively analyzed and divided into a training set (n = 389) and a validation set (n = 97). Radiographic features and risk factors (sex, age, tumor location, etc.) associated with LM of patients were evaluated. We built eight clinical-radiomics models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], Decision Tree [DT], Gradient Boosting Decision Tree [GBDT], AdaBoost, and extreme gradient boosting [XGBoost]) and compared their performance. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. RESULTS: The radscore, ALP, and tumor size had significant differences between the LM and non-LM groups (tradscore = -5.829, χ2ALP = 97.137, tsize = -3.437, P < 0.01). Multivariable LR analyses showed that ALP was an important indicator for predicting LM of OS (odds ratio [OR] = 7.272, P < 0.001). Among the eight models, the SVM-based clinical-radiomics model had the best performance in the validation set (AUC = 0.807, ACC = 0.784). CONCLUSION: The clinical-radiomics model had good performance in predicting LM in OS, which would be helpful in clinical decision-making.

Notoginsenoside R1 inhibits hepatitis B virus replication by modulating SIRT1 activity.

The hepatitis B virus (HBV) infection remains highly prevalent globally. The present study aimed to explore the possible therapeutic effect of notoginsenoside R1, which has attracted considerable attention due to its diverse pharmacological effects, on HBV infection. The HBV-containing hepatocellular carcinoma cell lines, HepG2 and MHCC97H, were used in this study. We first treated the two cell lines with different concentrations of notoginsenoside R1 and subsequently measured the relative levels of HBV DNA, HBV surface antigen, HBV core antigen, and sirtuin 1 (SIRT1) using reverse transcription-quantitative polymerase chain reaction and western blotting. Finally, an HBV hemodynamic replication model was created to test the effect of notoginsenoside R1 on HBV replication. Notoginsenoside R1 inhibited the replication of HBV. This inhibitory effect was mediated through the downregulation of SIRT1 activity. Additionally, the inhibition of SIRT1 activity by silencing its expression or treatment with the SIRT1 inhibitor, selisistat, suppressed HBV replication. Furthermore, our animal experiments demonstrated that notoginsenoside R1 was effective at suppressing HBV replication in vivo. Thus, notoginsenoside R1 suppresses HBV replication by downregulating SIRT1 activity in vitro and in vivo. Keywords: notoginsenoside R1; hepatitis B virus; SIRT1.

Antitumor mechanism of kangliu pill on gliomas in mice through PI3K-Akt signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Gliomas are common malignant intracranial tumors that have worse prognosis and pose a serious threat to human health. The Kangliu pill (KLP) is an innovative herbal compound from Xuanwu Hospital of Capital Medical University that has been clinically used for the treatment of gliomas for more than 40 years, and is one of the few drugs for primary treatment of this disorder. But the fundamental molecular mechanisms and pathways of KLP are not clear. AIM OF THE STUDY: To investigate the therapeutic mechanism of KLP in the treatment of gliomas. MATERIALS AND METHODS: An in situ xenograft model of red fluorescent protein-labeled human glioma cell line (U87-RFP) in BALB/c-nu mouse was established, and the therapeutic effect of KLP on gliomas was assessed by tumor weights and fluorescence areas. A quantitative proteomics approach using tandem mass tags combined with liquid chromatography-tandem mass spectrometry was performed to explore differentially expressed proteins (DEPs) in glioma tissues, and bioinformatics analyses including Gene Ontology analysis, pathway analysis, and network analysis were performed to analyze the proteins involved in the network therapeutic mechanisms responsible for key metabolic pathways. Cytological experiments corroborated the above analysis results. RESULTS: Network pharmacology approach screened 246 bioactive compounds contained in KLP, targeting 724 proteins and 173 potential targets of KLP for glioma treatment. The important targets obtained after visualizing the PPI network were AKT1, INS, GAPDH, SRC, TP53, etc. The KEGG enrichment results showed that 9 proteins were related to cancer, including Pathways in cancer, PI3K/AKT signaling pathway, etc. KLP had antitumor activity in gliomas, which reduced tumor weights and fluorescence areas. A number of DEPs possibly associated with gliomas were identified through quantitative proteomic techniques. Among these DEPs, 50 (25 upregulated and 25 downregulated) were identified that might be associated with KLP action. Bioinformatics showed that these 50 DEPs were mainly focused on focal adhesion, extracellular matrix (ECM)-receptor interactions, and the PI3K-Akt signaling pathway. Cytological experiments revealed that KLP significantly inhibited the proliferation and promoted apoptosis of U87-MG human glioma cells, and its mechanism was through the inhibition of PI3K/AKT signaling pathway. CONCLUSION: Therapeutic effect of KLP was regulation of multiple pathways in the treatment of gliomas. In specific, it interacts through the PI3K-Akt signaling pathway. This work may contribute proteomic insights for further research on the medical treatment of glioma using KLP.

Two nomograms based on radiomics models using triphasic CT for differentiation of adrenal lipid-poor benign lesions and metastases in a cancer population: an exploratory study.

OBJECTIVES: To investigate the effectiveness of CT-based radiomics nomograms in differentiating adrenal lipid-poor benign lesions and metastases in a cancer population. METHODS: This retrospective study enrolled 178 patients with cancer history from three medical centres categorised as those with adrenal lipid-poor benign lesions or metastases. Patients were divided into training, validation, and external testing cohorts. Radiomics features were extracted from triphasic CT images (unenhanced, arterial, and venous) to establish three single-phase models and one triphasic radiomics model using logistic regression. Unenhanced and triphasic nomograms were established by incorporating significant clinico-radiological factors and radscores. The models were evaluated by the receiver operating characteristic curve, Delong's test, calibration curve, and decision curve. RESULTS: Lesion side, diameter, and enhancement ratio resulted as independent factors and were selected into nomograms. The areas under the curves (AUCs) of unenhanced and triphasic radiomics models in validation (0.878, 0.914, p = 0.381) and external testing cohorts (0.900, 0.893, p = 0.882) were similar and higher than arterial and venous models (validation: 0.842, 0.765; testing: 0.814, 0.806). Unenhanced and triphasic nomograms yielded similar AUCs in validation (0.903, 0.906, p = 0.955) and testing cohorts (0.928, 0.946, p = 0.528). The calibration curves showed good agreement and decision curves indicated satisfactory clinical benefits. CONCLUSION: Unenhanced and triphasic CT-based radiomics nomograms resulted as a useful tool to differentiate adrenal lipid-poor benign lesions from metastases in a cancer population. They exhibited similar predictive efficacies, indicating that enhanced examinations could be avoided in special populations. KEY POINTS: • All four radiomics models and two nomograms using triphasic CT images exhibited favourable performances in three cohorts to characterise the cancer population's adrenal benign lesions and metastases. • Unenhanced and triphasic radiomics models had similar predictive performances, outperforming arterial and venous models. • Unenhanced and triphasic nomograms also exhibited similar efficacies and good clinical benefits, indicating that contrast-enhanced examinations could be avoided when identifying adrenal benign lesions and metastases.

Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma.

BACKGROUND: The preoperative prediction of lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) is essential in prognosis and treatment strategy formulation. PURPOSE: To compare the performance of computed tomography (CT) and magnetic resonance imaging (MRI) radiomics models for the preoperative prediction of LNM in PDAC. MATERIAL AND METHODS: In total, 160 consecutive patients with PDAC were retrospectively included, who were divided into the training and validation sets (ratio of 8:2). Two radiologists evaluated LNM basing on morphological abnormalities. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, and multiphase contrast enhanced MRI and multiphase CT, respectively. Overall, 1184 radiomics features were extracted from each volume of interest drawn. Only features with an intraclass correlation coefficient ≥0.75 were included. Three sequential feature selection steps-variance threshold, variance thresholding and least absolute shrinkage selection operator-were repeated 20 times with fivefold cross-validation in the training set. Two radiomics models based on multiphase CT and multiparametric MRI were built with the five most frequent features. Model performance was evaluated using the area under the curve (AUC) values. RESULTS: Multiparametric MRI radiomics model achieved improved AUCs (0.791 and 0.786 in the training and validation sets, respectively) than that of the CT radiomics model (0.672 and 0.655 in the training and validation sets, respectively) and of the radiologists' assessment (0.600-0.613 and 0.560-0.587 in the training and validation sets, respectively). CONCLUSION: Multiparametric MRI radiomics model may serve as a potential tool for preoperatively evaluating LNM in PDAC and had superior predictive performance to multiphase CT-based model and radiologists' assessment.