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 3^rd post-contrast time point. Clinical and pathological features should be included to improve the accuracy of prediction.

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