Keywords: Diagnosis/Prediction, Breast, Cancer
Motivation: Accurate prediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) allows replacing mastectomy with breast-conserving surgery for patients with locally advanced breast cancer.
Goal(s): To develop a method of post-NAC pCR prediction with automatic segmentation
Approach: A segmentation model was trained on pre-NAC MRI data and applied on post-NAC MRI data. A histogram signature was converted from post-NAC multi-phase images and classified into pCR and non-pCR by a convolutional neural network (CNN).
Results: The AUC of post-NAC histogram signature for pCR prediction is 0.869 (95%CI: 0.815-0.913) in the training group (n=199) and 0.815 (95%CI: 0.738-0.877) in the testing group (n=132).
Impact: Post-NAC MRI histogram signature based on pre-NAC segmentation model can be used to automatically predict pCR after NAC and assist individualized treatment for locally advanced breast cancer.
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