Huang Zhi, Shao Wei, Han Zhi, Alkashash Ahmad Mahmoud, De la Sancha Carlo, Parwani Anil V, Nitta Hiroaki, Hou Yanjun, Wang Tongxin, Salama Paul, Rizkalla Maher, Zhang Jie, Huang Kun, Li Zaibo
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Department of Electrical and Computer Engineering, Indiana University - Purdue University Indianapolis, Indianapolis, IN, 46202, USA.
NPJ Precis Oncol. 2023 Jan 27;7(1):14. doi: 10.1038/s41698-023-00352-5.
Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.
计算算法和工具的进步使得利用计算病理学预测癌症患者的预后成为可能。然而，从治疗前的组织病理学图像预测临床结果仍然是一项具有挑战性的任务，这受到对肿瘤免疫微环境了解不足的限制。在本研究中，描述了一种自动、准确、全面、可解释且可重复的全切片图像（WSI）特征提取流程，称为基于图像的病理配准与分割统计（IMPRESS）。我们使用了苏木精和伊红（H&E）染色以及多重免疫组化（PD-L1、CD8+和CD163+）图像，研究了基于人工智能（AI）的算法使用自动特征提取方法能否预测人表皮生长因子受体2阳性（HER2+）和三阴性乳腺癌（TNBC）患者的新辅助化疗（NAC）结果。特征来源于肿瘤免疫微环境和临床数据，并用于训练机器学习模型，以准确预测乳腺癌患者对NAC的反应（HER2+曲线下面积[AUC]=0.8975；TNBC AUC=0.7674）。结果表明，该方法优于由病理学家手动生成的特征所训练的结果。所开发的图像特征和算法通过独立队列进行了进一步的外部验证，得到了令人鼓舞的结果，尤其是对于HER2+亚型。
