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.

本文提出了一个基于人工智能与专家认知的集成深度学习方法 (ECDEDL) 来改善乳腺癌组织病理学图像中新辅助化疗 (pCR) 治疗的预测模型内、外部验证表现一致性问题，其效果显著提高了预测模型的 AUC 和准确率。

基于专家认知的集成深度学习模型，用于乳腺癌组织切片病理完全缓解预测的外部验证

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

Early prediction of pathological complete response (pCR) following
neoadjuvant chemotherapy (NAC) for breast cancer plays a critical role in
surgical planning and optimizing treatment strategies. Recently, machine and
deep-learning based methods were suggested for early pCR prediction from
multi-parametric MRI (mp-MRI) data including dynamic contrast-enhanced MRI and
diffusion-weighted MRI (DWI) with moderate success. We introduce PD-DWI, a
physiologically decomposed DWI machine-learning model to predict pCR from DWI
and clinical data. Our model first decomposes the raw DWI data into the various
physiological cues that are influencing the DWI signal and then uses the
decomposed data, in addition to clinical variables, as the input features of a
radiomics-based XGBoost model. We demonstrated the added-value of our PD-DWI
model over conventional machine-learning approaches for pCR prediction from
mp-MRI data using the publicly available Breast Multi-parametric MRI for
prediction of NAC Response (BMMR2) challenge. Our model substantially improves
the area under the curve (AUC), compared to the current best result on the
leaderboard (0.8849 vs. 0.8397) for the challenge test set. PD-DWI has the
potential to improve prediction of pCR following NAC for breast cancer, reduce
overall mp-MRI acquisition times and eliminate the need for contrast-agent
injection.

使用生理分解 DWI 的机器学习模型 PD-DWI，结合临床数据预测乳腺癌患者的病理完全缓解反应，具有比传统机器学习方法更高的预测精度，可用于减少 mp-MRI 采集时间和消除对比剂注射的需求。

PD-DWI: 基于生理分解扩散加权 MRI 机器学习模型预测侵袭性乳腺癌新辅助化疗的响应

PD-DWI: Predicting response to neoadjuvant chemotherapy in invasive breast cancer with Physiologically-Decomposed Diffusion-Weighted MRI machine-learning model