A machine learning approach for personalized breast radiation dosimetry in CT: Integrating radiomics and deep neural networks

While CT imaging provides indispensable volumetric information, it also involves exposure to ionizing radiation, which carries inherent risks. These risks are particularly significant for breast tissue, which is susceptible to radiation damage due to its high cellular turnover rate and sensitivity to the mutagenic effects of ionizing radiation [1,2]. Therefore, accurate estimation of radiation dose to the breasts during diagnostic CT imaging is essential for minimizing potential risks associated…

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