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TY  - THES
AU  - Schneider, Hannah Sophia
TI  - Radiomics in der Mamma MRT: Klassifikation kontrastmittelaufnehmender Läsionen nach ihrer Dignität mittels zweier klassischer Machine Learning Algorithmen
PB  - Rheinisch-Westfälische Technische Hochschule Aachen
VL  - Dissertation
CY  - Aachen
M1  - RWTH-2020-07774
SP  - 1 Online-Ressource (IV, 66 Seiten) : Illustrationen, Diagramme
PY  - 2020
N1  - Veröffentlicht auf dem Publikationsserver der RWTH Aachen University
N1  - Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2020
AB  - In this doctoral thesis, the application of radiomic algorithms in Mamma MRI for classification of contrast-enhancing lesions is depicted. 1294 lesions out of 447 breast MRI examinations were manually segmented and 562 image features were extracted in total. These included shape, statistical and texture features from routinely acquired MRI sequences. Either histopathological analysis or a 2-year follow-up were used as target values, and for comparison with a radiological expert, lesions were grouped into BI-RADS® categories according to their prospective diagnostic reports. Correlation of the extracted features and the corresponding target values was performed by training and validation of two conventional radiomic algorithms - L1-Regularization and Principal Component Analysis. Though classification results were clinically acceptable (sensitivity 72
LB  - PUB:(DE-HGF)11
DO  - DOI:10.18154/RWTH-2020-07774
UR  - https://publications.rwth-aachen.de/record/794608
ER  -