In breast cancer, neoadjuvant chemotherapy (NAC) provides a standard treatment option for patients who have locally advanced cancer and some large operable tumors. A patient will have better prognosis when he has achieved a pathological complete response (pCR) with the treatment of NAC. There has been a trend to directly predict pCR to NAC from histological images based on deep learning (DL). However, the DL-based predictive models numerically have better performances in internal validation than in external validation. In this paper, we aim to alleviate this situation with an intrinsic approach. We propose an experts' cognition-driven ensemble deep learning (ECDEDL) approach. Taking the cognition of both pathology and artificial intelligence experts into consideration to improve the generalization of the predictive model to the external validation, ECDEDL can intrinsically approximate the working paradigm of a human being which will refer to his various working experiences to make decisions. ECDEDL 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 was also validated with 340 WSIs collected from other three centers as the external dataset to perform the external validation. In external validation, ECDEDL 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). ECDEDL was quite effective for external validation of predicting pCR to NAC from histological images in breast cancer, numerically approximating the internal validation.

翻译：在乳腺癌治疗中，新辅助化疗（NAC）为局部晚期癌症患者及部分大型可手术肿瘤患者提供了标准治疗方案。若患者通过NAC治疗达到病理完全缓解（pCR），其预后将显著改善。目前存在基于深度学习（DL）直接从组织学图像预测NAC后pCR的趋势。然而，基于DL的预测模型在内部验证中的数值表现通常优于外部验证。本文旨在通过一种本质性方法缓解此问题。我们提出一种专家认知驱动的集成深度学习（ECDEDL）方法。该方法综合病理学与人工智能专家的认知，以提升预测模型在外部验证中的泛化能力，其本质在于模拟人类参考多元工作经验进行决策的工作范式。ECDEDL使用来自同一中心的695张全切片图像作为主要数据集进行模型开发与内部验证，并采用来自其他三个中心的340张全切片图像作为外部数据集进行外部验证。在外部验证中，ECDEDL将pCR预测的AUC值从61.52(59.80-63.26)提升至67.75(66.74-68.80)，并将pCR预测准确率从56.09(49.39-62.79)提升至71.01(69.44-72.58)。ECDEDL能有效实现乳腺癌组织学图像预测NAC后pCR的外部验证，其数值表现已接近内部验证水平。