Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors
Jiejie Yao,
Wei Zhou,
Xiaohong Jia
et al.
Abstract:Purpose
Noninvasive, accurate and novel approaches to predict patients who will achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) could assist precise treatment strategies. The aim of this study was to explore machine learning (ML)-based peritumoral ultrasound radiomics signature (PURS), compared with intratumoral radiomics (IURS) and clinicopathologic factors, for early prediction of pCR.
Methods
We analyzed 358 locally advanced breast cancer patients (250 in the training set a… Show more
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