Classification of Parotid Tumors with Robust Radiomic Features from DCE- and DW-MRI DOI Creative Commons
Francesca Angelone,

Silvia Tortora,

Francesca Patella

et al.

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(4), P. 122 - 122

Published: April 17, 2025

This study aims to evaluate the role of MRI-based radiomic analysis and machine learning using both DWI with multiple B-values dynamic contrast-enhanced T1-weighted sequences differentiate benign (B) malignant (M) parotid tumors. Patients underwent DCE- DW-MRI. An expert radiologist performed manual selection 3D ROIs. Classification vs. tumors was based on features extracted from DCE-based DW-based parametric maps. Care taken in robustness evaluation no-bias features. Several classifiers were employed. Sensitivity specificity ranged 0.6 0.8. The combination LASSO + neural networks achieved highest performance (0.76 sensitivity 0.75 specificity). Our identified a few robust respect ROI that can effectively be adopted classifying

Language: Английский

Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue DOI Creative Commons

Hongju Yan,

Chaochao Dai, Xiaojing Xu

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 6, 2025

To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for classification glandular tissue components (GTC) in dense tissue. A total 1,848 healthy women with mammograms classified as were enrolled this prospective study. Residual Network (ResNet) 101 model and ResNet fully Convolutional Networks (ResNet + FCN) segmentation trained. The better effective was selected to appraise performance 3 radiologists non-breast radiologists. evaluation metrics included sensitivity, specificity, positive predictive value (PPV). ResNet101 demonstrated superior compared FCN model. It significantly enhanced sensitivity all by 0.060, 0.021, 0.170, 0.009, 0.052, 0.047, respectively. For P1 P4 glandular, PPVs increased 0.154, 0.178, 0.027, 0.109 Ai-assisted. Notably, experienced a particularly substantial rise PPV (p < 0.01). This study trained deep learning is reliable accurate system assisting different differentiate images.

Language: Английский

Citations

0
Beyond dental radiographs, a radiomics-based study for the classification of caries extension and depth DOI Creative Commons
Niccolò Giuseppe Armogida, Francesca Angelone, Parisa Soltani

et al.

Journal of Dental Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Classification of Parotid Tumors with Robust Radiomic Features from DCE- and DW-MRI DOI Creative Commons
Francesca Angelone,

Silvia Tortora,

Francesca Patella

et al.

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(4), P. 122 - 122

Published: April 17, 2025

This study aims to evaluate the role of MRI-based radiomic analysis and machine learning using both DWI with multiple B-values dynamic contrast-enhanced T1-weighted sequences differentiate benign (B) malignant (M) parotid tumors. Patients underwent DCE- DW-MRI. An expert radiologist performed manual selection 3D ROIs. Classification vs. tumors was based on features extracted from DCE-based DW-based parametric maps. Care taken in robustness evaluation no-bias features. Several classifiers were employed. Sensitivity specificity ranged 0.6 0.8. The combination LASSO + neural networks achieved highest performance (0.76 sensitivity 0.75 specificity). Our identified a few robust respect ROI that can effectively be adopted classifying

Language: Английский

Citations

0