Radiomics coupled with machine learning is based on the extraction of signatures from medical images that are invisible to the human eye to create models which would improve breast cancer diagnosis. Radiomics features extracted from dynamic contrast-enhanced MRI and diffusion-weighted imaging can be combined in multiparametric MRI. We hypothesize that radiomics features extracted from multiparametric MRI would allow for an improved model affording a more accurate breast cancer diagnosis. We developed a multiparametric model that achieved the best accuracy for breast cancer diagnosis compared to models based on dynamic contrast-enhanced MRI or diffusion-weighted imaging.
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