Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence
Motivation: The treatment methods and prognosis of cellular uterine leiomyomas (CULs) and uterine sarcomas (USs) are different. The ADC values has certain differential diagnostic value, but there is some overlap between them. Texture analysis (TA) may have some potential and complementary role in differential diagnosis.
Goal(s): To explore the capability of TA based on MRI and ADC values in the differential diagnosis of USs from CULs.
Approach: Combining the ADC values and texture parameters to set up diagnostic model and evaluate the diagnosis value and clinical usefulness of the model.
Results: Texture analysis combined with DWI could be helpful to distinguish USs and CULs.
Impact: Texture analysis combined with DWI give a better method to identify uterine sarcomas and cellular uterine leiomyomas, providing a more reliable basis for the choice of clinical treatment.
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