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September 5, 2025BioengineeringOpen Access

Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization

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Authors

RFRoberta FuscoIstituto Nazionale Tumori IRCCS "Fondazione G. Pascale"VGVincenza GranataIstituto Nazionale Tumori IRCCS "Fondazione G. Pascale"TPTeresa PetrosinoIstituto Nazionale Tumori IRCCS "Fondazione G. Pascale"

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Implication

Observational analysis reveals machine learning predicts tumor profiles and grading in breast cancer, indicating strong potential for non-invasive diagnostics.

Key Points

Machine learning models based on radiomic features effectively distinguish between benign and malignant lesions.
Gradient Boosting Machine achieved the highest accuracy (0.911) in differentiating benign from malignant tumors.
The study utilized dynamic contrast-enhanced MRI and mammography features to evaluate various tumor characteristics.
These models demonstrate potential for improving early diagnosis and treatment planning in breast cancer management.

Cite This Study

Fusco et al. (2025) studied this question.

synapsesocial.com/papers/68bb5f586d6d5674bcd03999 https://doi.org/10.3390/bioengineering12090952

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Implication

Observational analysis reveals machine learning predicts tumor profiles and grading in breast cancer, indicating strong potential for non-invasive diagnostics.

Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization

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Cite This Study

Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization

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Cite This Study

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