Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model

ConclusionsDeepTEPP represents a pioneering MR-based deep learning model that enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thereby providing valuable guidance for anti-HER2 (de-)escalation strategies. DeepTEPP provides an important reference for choosing the appropriate individualized treatment in HER2  + breast cancer patients, warranting prospective validation.Clinical relevance statementWe built an MR-based deep learning model DeepTEPP, which enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thus guiding anti-HER2 (de-)escalation strategies in early HER2-positive breast cancer patients.Key Points•DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation.•DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival.•To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction.
Source: European Radiology - Category: Radiology Source Type: research