Experts' cognition-driven ensemble deep learning for external validation
  of predicting pathological complete response to neoadjuvant chemotherapy from
  histological images in breast cancer
Yongquan Yang Fengling Li Yani Wei Yuanyuan Zhao Jing Fu Xiuli Xiao Hong Bu
In breast cancer imaging, there has been a trend to directly predict
pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) from
histological images based on deep learning (DL). However, it has been a
commonly known problem that the constructed DL-based models numerically have
better performances in internal validation than in external validation. The
primary reason for this situation lies in that the distribution of the external
data for validation is different from the distribution of the training data for
the construction of the predictive model. In this paper, we aim to alleviate
this situation with a more intrinsic approach. We propose an experts'
cognition-driven ensemble deep learning (ECDEDL) approach for external
validation of predicting pCR to NAC from histological images in breast cancer.
The proposed ECDEDL, which takes the cognition of both pathology and artificial
intelligence experts into consideration to improve the generalization of the
predictive model to the external validation, more intrinsically approximates
the working paradigm of a human being which will refer to his various working
experiences to make decisions. The proposed ECDEDL approach was validated with
695 WSIs collected from the same center as the primary dataset to develop the
predictive model and perform the internal validation, and 340 WSIs collected
from other three centers as the external dataset to perform the external
validation. In external validation, the proposed ECDEDL approach improves the
AUCs of pCR prediction from 61.52(59.80-63.26) to 67.75(66.74-68.80) and the
Accuracies of pCR prediction from 56.09(49.39-62.79) to 71.01(69.44-72.58). The
proposed ECDEDL was quite effective for external validation, numerically more
approximating the internal validation.