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Abstract #0267

 Artificial Intelligence for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy from MRI and Prognostic Clinical Features

Hongyi Duanmu1, Pauline Huang1, Srinidhi Brahmavar1, Fusheng Wang1, and Tim Q Duong1
1Stony Brook University, Stony Brook, NY, United States

Pathological complete response (pCR) is a measurement of the effectiveness of neoadjuvant chemotherapy (NAC). While there are several studies about predicting the pCR, no one system can fully automate this prediction process. We proposed a 3D convolutional neural network (CNN) system, integrating information on breast MRI images and prognostic clinical features, to predict pCR pre-NAC. This system achieved inspiring results in the ISPY1 Clinical Trial dataset, with 77% accuracy. This approach shows the potential in breast cancer diagnose and assessment. Furthermore, the mechanism of integrating images and features information can be used and generalized to other tasks.

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