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Your search keyword '"Li, Kuo"' showing total 278 results
Start Over Author "Li, Kuo" Publisher elsevier b.v.
278 results on '"Li, Kuo"'

33. Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography.

Author

Ye, Guanchao, Wu, Guangyao, Li, Kuo, Zhang, Chi, Zhuang, Yuzhou, Liu, Hong, Song, Enmin, Qi, Yu, Li, Yiying, Yang, Fan, and Liao, Yongde

Abstract

To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk pathological pulmonary nodules. The study cohort consisted of 469 cases of lung adenocarcinoma patients, divided into a training cohort (n = 400) and an external validation cohort (n = 69). We obtained computed tomography (CT) semantic features and clinical characteristics, as well as extracted radiomics and deep transfer learning (DTL) features from the CT images. Selected features were used for constructing prediction models using the logistic regression (LR) algorithm. The performance of the models was evaluated through metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. The clinical model achieved an AUC of 0.774 (95% CI: 0.728–0.821) in the training cohort and 0.762 (95% confidence interval [CI]: 0.650–0.873) in the external validation cohort. The radiomics model demonstrated an AUC of 0.847 (95% CI: 0.810–0.884) in the training cohort and 0.800 (95% CI: 0.693–0.907) in the external validation cohort. The radiomics-DTL (Rad-DTL) model showed an AUC of 0.871 (95% CI: 0.838–0.905) in the training cohort and 0.806 (95% CI: 0.698–0.914) in the external validation cohort. The proposed combined model yielded AUC values of 0.872 and 0.814 in the training and external validation cohorts, respectively. The combined model demonstrated superiority over both the clinical model and the Rad-DTL model. There were no statistically significant differences observed in the comparison between the combined model incorporating clinical features and the Rad-DTL model. Decision curve analysis (DCA) indicated that the models provided a net benefit in predicting high-risk pathologic pulmonary nodules. Rad-DTL signature is a potential biomarker for predicting high-risk pathologic pulmonary nodules using preoperative CT, determining the appropriate surgical strategy, and guiding the extent of resection. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Mechanism study on carbon atom growth on different Ni facets in CO2 reforming reaction.

Author

Niu, Juntian, Li, Kuo, Zhang, Cunxin, Liu, Haiyu, Jin, Yan, and Ran, Jingyu

Subjects
*ATOMS, *DENSITY functional theory, *CARBON dioxide, *CARBON, *ATOMIC models
Abstract

In order to achieve the industrial application on dry reforming of methane (DRM), it is crucial to design and develop low-cost, highly active, and stable catalysts. In this study, based on density functional theory (DFT), adsorption models of carbon atoms on four different Ni-based catalyst surfaces are constructed to investigate the process of adsorption onto O* and growth mechanism of carbon atoms Cn(n = 1–6) on different surfaces, aiming to explore the differences in carbon-resistance performance of Ni-based catalyst surfaces. It is found that carbon atom growth on flat surface occurs mainly through ring-chain combination, while on step surface, chain growth is dominant. In general, carbon is more prone to form on step surface rather than flat surface. This study reveals the growth mechanism of carbon atoms on different Ni facets at the atomic level, providing guidance for the development of high-performance Ni-based reforming catalysts. [Display omitted] • Ni(111) exhibits better carbon-resistance compared to Ni(100) of flat surface. • Ni(211) exhibits better carbon-resistance compared to Ni(311) of step surface. • Carbon is more prone to form on step surface rather than flat surface. [ABSTRACT FROM AUTHOR]

Published
2024
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