Quantitative CT lumbar spine BMD cutpoint value for classifying osteoporosis among older East Asian women should be lower than the value for Caucasians.

For Caucasian women, the QCT (quantitative CT) lumbar spine (LS) bone mineral density (BMD) cutpoint value for classifying osteoporosis is 80 mg/ml. At the age of approximate 78 years, US Caucasian women QCT LS BMD population mean is 80 mg/ml, while that of Chinese women and Japanese women is around 50 mg/ml. Correlation analyses show, for Chinese women and Japanese women, QCT LS BMD of 45 mg/ml corresponds to the dual-energy X-ray absorptiometry cutpoint value for classifying osteoporosis. For Chinese and Japanese women, if QCT LS BMD 80 mg/ml is used as the threshold to classify osteoporosis, then the specificity of classifying subjects with vertebral fragility fracture into the osteoporotic group is low, whereas threshold of 45 mg/ml approximately achieve a similar separation for women with and without vertebral fragility fracture as the reports for Caucasian women. Moreover, by using 80mg/ml as the cutpoint value, LS QCT leads to excessively high prevalence of osteoporosis for Chinese women, with the discordance between hip dual-energy X-ray absorptiometry and LS QCT measures far exceeding expectation. Considering the different bone properties and the much lower prevalence of fragility fractures in the East Asian women compared with Caucasians, we argue that the QCT cutpoint value for classifying osteoporosis among older East Asian women will be close to and no more than 50 mg/ml LS BMD. We suggest that it is also imperative the QCT osteoporosis classification criterion for East Asian male LS, and male and female hips be re-examined.

'There is always good fortune in misfortune to encourage us in coping with difficulties'-The lived experiences of family caregivers of people with dementia during the COVID-19 outbreak in China: A phenomenological study.

AIM: To describe the lived experiences of family caregivers of individuals with dementia during the coronavirus disease (COVID-19) outbreak in China. DESIGN: This study used a descriptive phenomenological research method. METHODS: Between May and September 2021, semi-structured interviews were conducted with 22 family caregivers of people with dementia. Colaizzi's method was used for manual analysis. RESULTS: Qualitative data revealed an overarching experience of finding 'There is always good fortune in misfortune to encourage us in coping with difficulties'. Three themes emerged: family reactions to the COVID-19 outbreak, feeling supported by multiple resources performing respective functions and resilient adaptation to new situations. CONCLUSION: During the COVID-19 outbreak, family caregivers of people living with dementia in China looked for positive aspects among difficulties and experienced corresponding reactions, social support resources and resilient adapted coping styles. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurses in China and other countries facing similar pandemic characteristics, cultures or economic development levels, can guide family caregivers to look at family hardships from a positive perspective, develop interventions to rapidly respond to families' reactions after a disaster and help them identify social support resources and form adapted coping styles. IMPACT: We identified the resilience and the positive experiences of Chinese family caregivers of individuals with dementia during the COVID-19 outbreak. The results can inform countries with similar cultures and economic levels, offering measures to support their adaptation to pandemics. REPORTING METHOD: This study followed the COREQ guidelines. PATIENT OR PUBLIC CONTRIBUTION: Family caregivers of people with dementia who met the inclusion criteria and who were interested in sharing their understanding of their experiences, participated in the study.

Intranasal Delivery of Temozolomide-Conjugated Gold Nanoparticles Functionalized with Anti-EphA3 for Glioblastoma Targeting.

Glioblastoma multiforme (GBM) is a highly lethal and aggressive tumor of the brain that carries a poor prognosis. Temozolomide (TMZ) has been widely used as a first-line treatment for GBM. However, poor brain targeting, side effects, and drug resistance limit its application for the treatment of GBM. We designed a Temozolomide-conjugated gold nanoparticle functionalized with an antibody against the ephrin type-A receptor 3 (anti-EphA3-TMZ@GNPs) for targeted GBM therapy via intranasal administration. The system can bypass the blood-brain barrier and target active glioma cells to improve the glioma targeting of TMZ and enhance the treatment efficacy, while reducing the peripheral toxicity and drug resistance. The prepared anti-EphA3-TMZ@GNPs were 46.12 ± 2.0 nm and suitable for intranasal administration, which demonstrated high safety to the nasal mucosa in a toxicity assay. In vitro studies showed that anti-EphA3-TMZ@GNPs exhibited significantly enhanced cellular uptake and toxicity, and a higher cell apoptosis ratio has been seen compared with that of TMZ (54.9 and 14.1%, respectively) toward glioma cells (C6). The results from experiments on TMZ-resistant glioma cells (T98G) demonstrated that the IC50 of anti-EphA3-TMZ@GNPs (64.06 ± 0.16 µM) was 18.5-fold lower than that of TMZ. In addition, Western blot analysis also revealed that anti-EphA3-TMZ@GNPs effectively down-modulated expression of O6-methylguanine-DNA methyltransferase and increased chemosensitivity of T98G to TMZ. The antiglioma efficacy in vivo was investigated in orthotopic glioma-bearing rats, and the results demonstrated that the anti-EphA3-TMZ@GNPs prolonged the median survival time to 42 days and increased tumor-cell apoptosis dramatically compared with TMZ. In conclusion, anti-EphA3-TMZ@GNPs could serve as an intranasal drug delivery system for efficacious treatment of GBM.

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet.

Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model.

As a critical part of a hydraulic transmission system, a hydraulic axial piston pump plays an indispensable role in many significant industrial fields. Owing to the practical undesirable working environment and hidden faults, it is challenging to precisely and effectively detect and diagnose the varying fault in the engineering. Deep learning-based technology presents special strengths in processing mechanical big data. It can simultaneously complete the feature extraction and classification, and achieve the automatic information learning. The popular convolutional neural network (CNN) is exploited for its potent ability of image processing. In this paper, a novel combined intelligent method is developed for fault diagnosis towards a hydraulic axial piston pump. First, the conversion of signals to images is conducted via continuous wavelet transform; the effective feature is preliminarily extracted from the transformed time-frequency images. Second, a novel deep CNN model is constructed to achieve the fault classification. To disclose the potential learning in the disparate layers of the CNN model, the visualization of reduced features is performed by employing t-distributed stochastic neighbor embedding. The effectiveness and stability of the proposed model are validated through the experiments. With the proposed method, different fault types can be precisely identified and high classification accuracy is achieved in a hydraulic axial piston pump.

An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump.

A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump.

PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif.

Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapidly and accurately identifying plant protein SCLs without a machine learning algorithm. PlantLoc provides predicted SCLs results, confidence estimates and which is the substantiality motif and where it is located on the sequence. PlantLoc achieved the highest accuracy (overall accuracy of 80.8%) of identification of plant protein SCLs as benchmarked by using a new test dataset compared other plant SCL prediction webservers. The ability of PlantLoc to predict multiple sites was also significantly higher than for any other webserver. The predicted substantiality motifs of queries also have great potential for analysis of relationships with protein functional regions. The PlantLoc server is available at http://cal.tongji.edu.cn/PlantLoc/.

Retrieving backbone string neighbors provides insights into structural modeling of membrane proteins.

Identification of protein structural neighbors to a query is fundamental in structure and function prediction. Here we present BS-align, a systematic method to retrieve backbone string neighbors from primary sequences as templates for protein modeling. The backbone conformation of a protein is represented by the backbone string, as defined in Ramachandran space. The backbone string of a query can be accurately predicted by two innovative technologies: a knowledge-driven sequence alignment and encoding of a backbone string element profile. Then, the predicted backbone string is employed to align against a backbone string database and retrieve a set of backbone string neighbors. The backbone string neighbors were shown to be close to native structures of query proteins. BS-align was successfully employed to predict models of 10 membrane proteins with lengths ranging between 229 and 595 residues, and whose high-resolution structural determinations were difficult to elucidate both by experiment and prediction. The obtained TM-scores and root mean square deviations of the models confirmed that the models based on the backbone string neighbors retrieved by the BS-align were very close to the native membrane structures although the query and the neighbor shared a very low sequence identity. The backbone string system represents a new road for the prediction of protein structure from sequence, and suggests that the similarity of the backbone string would be more informative than describing a protein as belonging to a fold.

DSP: a protein shape string and its profile prediction server.

Many studies have demonstrated that shape string is an extremely important structure representation, since it is more complete than the classical secondary structure. The shape string provides detailed information also in the regions denoted random coil. But few services are provided for systematic analysis of protein shape string. To fill this gap, we have developed an accurate shape string predictor based on two innovative technologies: a knowledge-driven sequence alignment and a sequence shape string profile method. The performance on blind test data demonstrates that the proposed method can be used for accurate prediction of protein shape string. The DSP server provides both predicted shape string and sequence shape string profile for each query sequence. Using this information, the users can compare protein structure or display protein evolution in shape string space. The DSP server is available at both http://cheminfo.tongji.edu.cn/dsp/ and its main mirror http://chemcenter.tongji.edu.cn/dsp/.

Radiomic Features at Contrast-Enhanced CT Predict Virus-Driven Liver Fibrosis: A Multi-Institutional Study.

INTRODUCTION: Liver fibrosis is a major cause of morbidity and mortality among in patients with chronic hepatitis. Radiomics, particularly of the spleen, may improve diagnostic accuracy and treatment strategies. External validations are necessary to ensure reliability and generalizability. METHODS: In this retrospective study, we developed 3 radiomics models using contrast-enhanced computed tomography scans from 167 patients with liver fibrosis (training group) between January 2020 and December 2021. Radiomic features were extracted from arterial venous, portal venous, and equilibrium phase images. Recursive feature selection random forest and the least absolute shrinkage and selection operator logistic regression were used for feature selection and dimensionality reduction. Performance was assessed by area under the curve, C-index, calibration plots, and decision curve analysis. External validation was performed on 114 patients from 2 institutions. RESULTS: Twenty-five radiomic features were significantly associated with fibrosis stage, with 80% of the top 10 features originating from portal venous phase spleen images. The radiomics models showed good performance in the validation cohort (C-indices 0.723-0.808) and excellent calibration. Decision curve analysis indicated clinical benefits, with machine learning-based radiomics models (Random Forest score and support vector machine based radiomics score) providing more significant advantages. DISCUSSION: Radiomic features offer significant benefits over existing serum indices for staging virus-driven liver fibrosis, underscoring the value of radiomics in enhancing diagnostic accuracy. Specifically, radiomics analysis of the spleen presents additional noninvasive options for assessing fibrosis, highlighting its potential in improving patient management and outcomes.

Differences in pulmonary nodular consolidation and pulmonary cavity among drug-sensitive, rifampicin-resistant and multi-drug resistant tuberculosis patients: the Guangzhou computerized tomography study.

Background: Pulmonary nodular consolidation (PN) and pulmonary cavity (PC) may represent the two most promising imaging signs in differentiating multidrug-resistant (MDR)-pulmonary tuberculosis (PTB) from drug-sensitive (DS)-PTB. However, there have been concerns that literature described radiological feature differences between DS-PTB and MDR-PTB were confounded by that MDR-PTB cases tend to have a longer history. This study seeks to further clarify this point. Methods: All cases were from the Guangzhou Chest Hospital, Guangzhou, China. We retrieved data of consecutive new MDR cases [n=46, inclusive of rifampicin-resistant (RR) cases] treated during the period of July 2020 and December 2021, and according to the electronic case archiving system records, the main PTB-related symptoms/signs history was ≤3 months till the first computed tomography (CT) scan in Guangzhou Chest Hospital was taken. To pair the MDR-PTB cases with assumed equal disease history length, we additionally retrieved data of 46 cases of DS-PTB patients. Twenty-two of the DS patients and 30 of the MDR patients were from rural communities. The first CT in Guangzhou Chest Hospital was analysed in this study. When the CT was taken, most cases had anti-TB drug treatment for less than 2 weeks, and none had been treated for more than 3 weeks. Results: Apparent CT signs associated with chronicity were noted in 10 cases in the DS group (10/46) and 9 cases in the MDR group (10/46). Thus, the overall disease history would have been longer than the assumed <3 months. Still, the history length difference between DS patients and MDR patients in the current study might not be substantial. The lung volume involvement was 11.3%±8.3% for DS cases and 8.4%±6.6% for MDR cases (P=0.022). There was no statistical difference between DS cases and MDR cases both in PN prevalence and in PC prevalence. For positive cases, MDR cases had more PN number (mean of positive cases: 2.63 vs. 2.28, P=0.38) and PC number (mean of positive cases: 2.14 vs. 1.38, P=0.001) than DS cases. Receiver operating characteristic curve analysis shows, PN ≥4 and PC ≥3 had a specificity of 86% (sensitivity 25%) and 93% (sensitivity 36%), respectively, in suggesting the patient being a MDR cases. Conclusions: A combination of PN and PC features allows statistical separation of DS and MDR cases.

An evaluation of the anti-tumor efficacy of oleanolic acid-loaded PEGylated liposomes.

The effective delivery of oleanolic acid (OA) to the target site has several benefits in therapy for different pathologies. However, the delivery of OA is challenging due to its poor aqueous solubility. The study aims to evaluate the tumor inhibition effect of the PEGylated OA nanoliposome on the U14 cervical carcinoma cell line. In our previous study, OA was successfully encapsulated into PEGylated liposome with the modified ethanol injection method. Oral administration of PEGylated OA liposome was demonstrated to be more efficient in inhibiting xenograft tumors. The results of organ index indicated that PEG liposome exhibited higher anti-tumor activity and lower cytotoxicity. It was also found that OA and OA liposomes induced tumor cell apoptosis detected by flow cytometry. Furthermore, effects of OA on the morphology of tumor and other tissues were observed by hematoxylin and eosin staining. The histopathology sections did not show pathological changes in kidney or liver in tested mice. In contrast, there was a significant difference in tumor tissues between treatment groups and the negative control group. These observations imply that PEGylated liposomes seem to have advantages for cancer therapy in terms of effective delivery of OA.

Data-Knowledge-Driven Modeling and Operational Adjustment for the Pharmaceutical Tablet Manufacturing Process via Wet Granulation.

In the context of Pharma 4.0, pharmaceutical quality control (PQC) is beset by issues such as uncertainties from ever-changing critical material attributes and strong coupling between variables in the multi-unit pharmaceutical tablet manufacturing process (PTMP), and how to timely adjust the operational variables to deal with such challenges has become a key problem in PQC. In this study, we propose a novel data-knowledge-driven modeling and operational adjustment framework for PTMP by integrating Bayesian network (BN) and case-based reasoning (CBR). At the modeling level, first, a distributed concept is introduced, i.e., the BN model for each subunit of PTMP is established in accordance with the operation process sequence, and the transition variables are given by the BN model established first and retrieved as the new query for the next unit. Once the BN models of all subunits are built, they are integrated into a global BN model. At the operational adjustment level, by taking the expected critical quality attributes (CQAs) and related prior information as evidence, the operational adjustment is achieved through global BN reasoning. Finally, the case study in a sprayed fluidized-bed granulation-based PTMP demonstrates the feasibility and effectiveness in improving the terminal CQAs of the proposed method, which is also compared with other methods to showcase its efficacy and merits.

Clinical-radiomic analysis for non-invasive prediction of liver steatosis on non-contrast CT: A pilot study.

Purpose: Our aim is to build and validate a clinical-radiomic model for non-invasive liver steatosis prediction based on non-contrast computed tomography (CT). Methods: We retrospectively reviewed 342 patients with suspected NAFLD diagnoses between January 2019 and July 2020 who underwent non-contrast CT and liver biopsy. Radiomics features from hepatic and splenic regions-of-interests (ROIs) were extracted based on abdominal non-contrast CT imaging. The radiomics signature was constructed based on reproducible features by adopting the least absolute shrinkage and selection operator (LASSO) regression. Then, multivariate logistic regression analysis was applied to develop a combined clinical-radiomic nomogram integrating radiomics signature with several independent clinical predictors in a training cohort of 124 patients between January 2019 and December 2019. The performance of models was determined by the area under the receiver operating characteristic curves and calibration curves. We conducted an internal validation during 103 consecutive patients between January 2020 and July 2020. Results: The radiomics signature was composed of four steatosis-related features and positively correlated with pathologic liver steatosis grade (p < 0.01). In both subgroups (Group One, none vs. steatosis; Group Two, none/mild vs. moderate/severe steatosis), the clinical-radiomic model performed best within the validation cohort with an AUC of 0.734 and 0.930, respectively. The calibration curve confirmed the concordance of excellent models. Conclusion: We developed a robust clinical-radiomic model for accurate liver steatosis stage prediction in a non-invasive way, which may improve the clinical decision-making ability.

Bifunctional Ni@NiO catalyst supported on loofah sponge-derived carbon for electrocatalytic air oxidation of biorefractory pollutant in a coupling system.

Oxygen (O2) in the air is a green oxidant, and utilization of air for pollutant removal is highly desired. Herein, we report the preparation and utilization of a novel biomass-based three-dimensional (3D) Ni@NiO/carbon composite for the electro-activation of O2 under room condition. The carbon-coated Ni@NiO nanoparticles are fabricated on a hierarchical 3D porous loofah sponge-derived carbon (LSC) support as the bifunctional catalyst for the activation of O2 via both the electro-oxidation and electro-reduction reactions. An electrocatalytic air oxidation coupling system is constructed with the Ni@NiO/LSC shell-core electrodes for pollutant degradation. A variety of organic pollutants, including pharmaceutics and personal care products (PPCPs), dyes, phenolic compounds, and real waters are mineralized by more than 60% with significantly enhanced biodegradability. Notably, the coupling system obtains high mineralization efficiency of 70.2 ± 1.9% on landfill leachate with significant biodegradability enhancement. The specific energy consumptions of the coupling system are only 6.8 ± 0.7 to 60.2 ± 3.6 kWh kg-TOC-1 in mineralizing different pollutants. The hollow structure of the LSC fibers endows the loaded Ni@NiO with superior intrinsic catalytic activity, which is associated with low reaction resistance and facile electron transfer. The Ni@NiO on LSC presents an electrocatalytic wet air oxidation (ECWAO) catalytic activity higher by 35.8% and cathodic air oxidation (CAO) catalytic activity higher by 22.7% as compared to that loaded on commercial graphite felt.

Predicting gram-positive bacterial protein subcellular localization based on localization motifs.

The subcellular localization of proteins is closely related to their functions. In this work, we propose a novel approach based on localization motifs to improve the accuracy of predicting subcellular localization of Gram-positive bacterial proteins. Our approach performed well on a five-fold cross validation with an overall success rate of 89.5%. Besides, the overall success rate of an independent testing dataset was 97.7%. Moreover, our approach was tested using a new experimentally-determined set of Gram-positive bacteria proteins and achieved an overall success rate of 96.3%.

Intelligent fault diagnosis of hydraulic piston pump based on deep learning and Bayesian optimization.

Hydraulic axial piston pump is broadly-used in aerospace, ocean engineering and construction machinery since it is the vital component of fluid power systems. In the light of the undiscoverability of its fault and the potential serious losses, it is valuable and challenging to complete the fault identification of a hydraulic pump accurately and effectively. Owing to the limitations of shallow machine learning methods in the intelligent fault diagnosis, more attention has been paid to deep learning methods. Hyperparameter plays an important role in a deep learning model. Although some manual tuning methods may represent good results in some cases, it is hard to reproduce due to the differences of datasets and other factors. Hence, Bayesian optimization (BO) algorithm is adopted to automatically select the hyperparameters. Firstly, the time-frequency images of vibration signals by continuous wavelet transform are taken as input data. Secondly, by setting some hyperparameters, a preliminary convolutional neural network (CNN) model is established. Thirdly, by identifying the range of each hyperparameter, BO based on Gaussian process is employed to construct an adaptive CNN model named CNN-BO. The performance of CNN-BO is verified by comparing with traditional LeNet 5 and improved LeNet 5 with manual optimization. The results indicate that CNN-BO can accomplish the intelligent fault diagnosis of a hydraulic pump accurately.

Sustained delivery of gemcitabine via in situ injectable mussel-inspired hydrogels for the local therapy of pancreatic cancer.

The issue of pervasively enhanced drug resistance of pancreatic cancer is fundamental to a better understanding of gemcitabine-based chemotherapy. Currently available treatment plans involving injectable therapeutics are mainly engineered to improve the performance and broaden their applications in the domain of biomedicine. Fixed-dose-rate infusion of free gemcitabine (Gem) has drawn appropriate attention for its potent anti-tumor efficacy against various solid tumors, whereas it remains a considerable challenge to extend its application and achieve better treatment. Here, we have prepared and demonstrated a long-acting delivery system using gemcitabine and injectable in situ hydrogel for the localized treatment of pancreatic cancer. The hydrogel was prepared using polysaccharide derivatives, oxidized-carboxymethylcellulose (OCMC) and carboxymethylchitosan (CMCS) at optimal ratios by a dopamine-functionalized method for the controlled release of Gem. In vitro drug release behaviors for up to a week indicated sustained drug release of the Gem delivery system. Moreover, desirable apoptosis promotion and apparent cellular proliferation inhibition associated with the drug depot have been found in vitro against BxPC-3 pancreatic cancer cells, bringing minimized side effects to systemic normal tissues. The current findings manifested that the release out of the localized delivery platform in a sustained pattern afforded a durable gemcitabine-based chemotherapy effect and inhibited tumor metastasis more persistently after intratumoral injection of the Gem@Gel system, thereby advancing the development of novel drug-loaded materials with properties not accessed previously.

Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker.

BACKGROUND: To establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images. MATERIALS AND METHODS: This retrospective study developed two radiomics-based models (R-score: radiomics signature; R-fibrosis: integrate radiomic and serum variables) in a training cohort of 332 patients (median age, 59 years; interquartile range, 51-67 years; 256 men) with biopsy-proven liver fibrosis who underwent contrast-enhanced CT between January 2017 and December 2020. Radiomic features were extracted from non-contrast, arterial and portal phase CT images and selected using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3-F4 from stage F0-F2. Optimal cutoffs to diagnose significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4) and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis. An internal validation was conducted in 111 randomly assigned patients (median age, 58 years; interquartile range, 49-66 years; 89 men). RESULTS: In the validation cohort, R-score and R-fibrosis (Obuchowski index, 0.843 and 0.846, respectively) significantly outperformed aspartate transaminase-to-platelet ratio (APRI) (Obuchowski index, 0.651; p < .001) and fibrosis-4 index (FIB-4) (Obuchowski index, 0.676; p < .001) for staging liver fibrosis. Using the cutoffs, R-fibrosis and R-score had a sensitivity range of 70-87%, specificity range of 71-97%, and accuracy range of 82-86% in diagnosing significant fibrosis, advanced fibrosis and cirrhosis. CONCLUSION: Radiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis.