Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks DOI Creative Commons
Yuhui Yuan, Yutong Zhang, Yong‐Xin Li

et al.

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: March 27, 2025

Background Deep learning has shown considerable promise in the differential diagnosis of lung lesions. However, majority previous studies have focused primarily on X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), with relatively few investigations exploring predictive value ultrasound imaging. Objective This study aims to develop a deep model based differentiate between benign malignant peripheral tumors. Methods A retrospective analysis was conducted cohort 371 patients who underwent ultrasound-guided percutaneous tumor procedures across two centers. The dataset divided into training set ( n = 296) test 75) an 8:2 ratio for further evaluation. Five distinct models were developed using ResNet152, ResNet101, ResNet50, ResNet34, ResNet18 algorithms. Receiver Operating Characteristic (ROC) curves generated, Area Under Curve (AUC) calculated assess diagnostic performance each model. DeLong’s employed compare differences groups. Results Among five models, one algorithm demonstrated highest performance. It exhibited statistically significant advantages accuracy p < 0.05) compared ResNet34 Specifically, showed superior discriminatory power. Quantitative evaluation through Net Reclassification Improvement (NRI) revealed that NRI values model, when 0.180, 0.240, 0.186, 0.221, respectively. All corresponding -values less than 0.05 comparison), confirming significantly outperformed other four reclassification ability. Moreover, its outcomes led marked improvements risk stratification classification accuracy. Conclusion ResNet18-based distinguishing tumors, providing effective non-invasive tool early detection cancer.

Language: Английский

The senolytic ABT-263 improves cognitive functions in middle-aged male, but not female, atherosclerotic LDLr−/−;hApoB100+/+ mice DOI Creative Commons
Mélanie Lambert,

Géraldine Miquel,

Louis Villeneuve

et al.

GeroScience, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 21, 2025

Language: Английский

Citations

1
An Early Progression Biomarker in Glioblastoma: Microcirculatory Heterogeneity on Ultrasound Localization Microscopy DOI
Xing Hu, Gaobo Zhang, Xiandi Zhang

et al.

Ultrasound in Medicine & Biology, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks DOI Creative Commons
Yuhui Yuan, Yutong Zhang, Yong‐Xin Li

et al.

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: March 27, 2025

Background Deep learning has shown considerable promise in the differential diagnosis of lung lesions. However, majority previous studies have focused primarily on X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), with relatively few investigations exploring predictive value ultrasound imaging. Objective This study aims to develop a deep model based differentiate between benign malignant peripheral tumors. Methods A retrospective analysis was conducted cohort 371 patients who underwent ultrasound-guided percutaneous tumor procedures across two centers. The dataset divided into training set ( n = 296) test 75) an 8:2 ratio for further evaluation. Five distinct models were developed using ResNet152, ResNet101, ResNet50, ResNet34, ResNet18 algorithms. Receiver Operating Characteristic (ROC) curves generated, Area Under Curve (AUC) calculated assess diagnostic performance each model. DeLong’s employed compare differences groups. Results Among five models, one algorithm demonstrated highest performance. It exhibited statistically significant advantages accuracy p < 0.05) compared ResNet34 Specifically, showed superior discriminatory power. Quantitative evaluation through Net Reclassification Improvement (NRI) revealed that NRI values model, when 0.180, 0.240, 0.186, 0.221, respectively. All corresponding -values less than 0.05 comparison), confirming significantly outperformed other four reclassification ability. Moreover, its outcomes led marked improvements risk stratification classification accuracy. Conclusion ResNet18-based distinguishing tumors, providing effective non-invasive tool early detection cancer.

Language: Английский

Citations

0