Comprehensive study on the difference in radiomic feature values between for-processing and for-presentation mammographic images and their discriminative power regarding BI-RADS density classification

Aim: To assess the difference in radiomic feature values between pairs of mammographic images used for processing(FOR PROC) and for presentation(FOR PRES) as well as the ability to determine the BIRADS density classification from these radiomic features with different classification models. Methods: A dataset of FOR PROC and FOR PRES image pairs annotated with labels for the BI-RADS classification done by a radiologist is used in this study. The differences in radiomic feature values between the image types are evaluated with the intraclass correlation coefficient(ICC). Additionally, the discriminative power of radiomic feature values regarding the BI-RADS score is evaluated with Logistic Regression, Random Forest and a 5-layer deep Neural Network. The results of these models are evaluated with a 5-fold crossvalidation. Results: The reliability of radiomic feature is generally low between pairs of FOR PROC and FOR PRES images for all radiomic feature groups. Furthermore, the simple models used to determine the ability to assign the BI-RADS density classification based on the radiomic feature values reached insufficient accuracy to be considered adequate. Conclusion: The study revealed low reliability between both image types. Furthermore radiomic features alone seem to be insufficient to determine the BI-RADS classification using simple models.