Keywords: Diagnosis/Prediction, Cancer
Motivation: Accurate prediction of pathological complete response (pCR) after neoadjuvant chemotherapy enables individualized treatment options to avoid unnecessary breast excision and improve patients’ life quality.
Goal(s): To improve the prediction accuracy by simultaneously extracting temporal and spatial features of MRI signal during contrast enhancement.
Approach: A histogram signature is designed by concatenating histograms at different enhancing phases into a 2D picture and classified by convolutional neural network into pCR or non-pCR.
Results: The AUC, sensitivity, specificity of the histogram signature for pCR prediction is 0.833 in the test group (n=132). The model combining histogram signature with ER and HER2 increases AUC to 0.842.
Impact: Histogram signatures from multi-phase MRI can be used as a new marker to measure tumor heterogeneity, estimate drug uptake, evaluate treatment response and predict prognosis for breast cancer or other cancers.
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