Abstract
The use of neoadjuvant therapy (NAT) for operable breast cancer (BC) has progressively increased over time and it is today recommended by major guidelines. The achievement of a pathological complete response (pCR) is associated with an improved outcome. As a consequence new strategies are required to early identify patients who will not respond. In this persepctive, metabolomics may represent an innovative technology to identify host related factors correlated with outcome. In this research we evaluate the use metabolomics analysis coupled to artificial intelligence to predict treatment outcome for BC patients undergoing NAT.
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