Motivation: Accurate histological grading is crucial for assessing tumor behavior and prognosis in invasive breast carcinoma. Traditional grading is susceptible to sample variability. Goal(s): This study aims to evaluate the diagnostic value of td-dMRI parameters for pathological grading of breast cancer. Approach: 55 breast cancer patients underwent td-dMRI, and data were analyzed using an IMPULSED scheme to derive microstructural parameters. Statistical tests and ROC curves were used to evaluate microstructural parameters for diagnostic accuracy. Results: Dex demonstrated the best predictive efficacy (AUC 0.774), improving to 0.812 when combined with other parameters, highlighting td-dMRI technique's potential for non-invasive tumor grading. Impact: This study highlights time-dependent diffusion MRI's potential for accurate, non-invasive breast cancer grading, enhancing clinical decision-making and treatment planning, and paving the way for further research into microstructural changes and applications in other cancer types.
Che et al. (Tue,) studied this question.
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