Congress:
ECR25
Poster Number:
C-11257
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
M. Swoboda, J. Deeg, D. Egle, V. Ladenhauf, M. Galijašević, S. Haushammer, B. Amort, M. Pamminger, L. Gruber; Innsbruck/AT
Disclosures:
Michael Swoboda:
Nothing to disclose
Johannes Deeg:
Nothing to disclose
Daniel Egle:
Nothing to disclose
Valentin Ladenhauf:
Nothing to disclose
Malik Galijašević:
Nothing to disclose
Silke Haushammer:
Nothing to disclose
Birgit Amort:
Nothing to disclose
Mathias Pamminger:
Nothing to disclose
Leonhard Gruber:
Nothing to disclose
Keywords:
Artificial Intelligence, Breast, Oncology, Elastography, Ultrasound, Diagnostic procedure, Cancer
This retrospective study was approved by the local ethics review board and was conducted in accordance with the Declaration of Helsinki (as revised in 2013). After screening 623 patients, 421 cases with histologically verified fibroadenomas and MTMF between 2011 and 2021 were included. The ultrasound images of the patients included in the study were reviewed. Sonographic lesion features were compared to histopathological results which included specific diagnosis and histopathologic classification (B2 = benign; B3 = intermediate malignant potential or B5 = malignant). An algorithm-based quantitative ranking of predictors contributing most to the correct classification of malignant tumors was conducted.