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Abstract #0573

Predicting response to neoadjuvant chemotherapy in breast cancer: machine learning-based analysis of radiomics features from baseline DCE-MRI

Gabrielle Baxter1, Andrew J Patterson2, Leonardo Rundo1, Ramona Woitek1, Reem Bedair2, Julia Carmona-Bozo1, Roido Manavaki1, Mary A McLean3, Scott A Reid4, Martin J Graves2, and Fiona J Gilbert1
1Department of Radiology, University of Cambridge, Cambridge, United Kingdom, 2Department of Radiology, Addenbrooke's Hospital, Cambridge, United Kingdom, 3Cancer Research UK, Cambridge, United Kingdom, 4GE Healthcare, Amersham, United Kingdom

This study investigated the prediction of pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer using radiomics features derived from pre-treatment DCE-MRI. 121 women with biopsy-confirmed breast cancers (44 pCR and 77 non-pCR) were imaged before treatment. 384 radiomics features were extracted from 5 post-contrast images. A logistic regression model trained on 21 of these features was able to predict pCR with an AUC of 0.78. The highest AUC (0.85) was achieved by using 7 features from only the 3rd post-contrast time point. Clinical and pathological features should be included to improve the accuracy of prediction.

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