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In breast cancer imaging, there has been a trend to directly predictpathological complete response (pCR) to neoadjuvant chemotherapy (NAC) fromhistological images based on deep learning (DL). However, it has been acommonly known problem that the constructed DL-based models numerically havebetter performances in internal validation than in external validation. Theprimary reason for this situation lies in that the distribution of the externaldata for validation is different from the distribution of the training data forthe construction of the predictive model. In this paper, we aim to alleviatethis situation with a more intrinsic approach. We propose an experts'cognition-driven ensemble deep learning (ECDEDL) approach for externalvalidation of predicting pCR to NAC from histological images in breast cancer.The proposed ECDEDL, which takes the cognition of both pathology and artificialintelligence experts into consideration to improve the generalization of thepredictive model to the external validation, more intrinsically approximatesthe working paradigm of a human being which will refer to his various workingexperiences to make decisions. The proposed ECDEDL approach was validated with695 WSIs collected from the same center as the primary dataset to develop thepredictive model and perform the internal validation, and 340 WSIs collectedfrom other three centers as the external dataset to perform the externalvalidation. In external validation, the proposed ECDEDL approach improves theAUCs of pCR prediction from 61.52(59.80-63.26) to 67.75(66.74-68.80) and theAccuracies of pCR prediction from 56.09(49.39-62.79) to 71.01(69.44-72.58). Theproposed ECDEDL was quite effective for external validation, numerically moreapproximating the internal validation.

Experts' cognition-driven ensemble deep learning for external validation  of predicting pathological complete response to neoadjuvant chemotherapy from  histological images in breast cancer