Gan Jiefeng, Wang Hanchen, Yu Hui, He Zitong, Zhang Wenjuan, Ma Ke, Zhu Lianghui, Bai Yutong, Zhou Zongwei, Yullie Alan, Bai Xiang, Wang Mingwei, Yang Dehua, Chen Yanyan, Chen Guoan, Lasenby Joan, Cheng Chao, Wu Jia, Zhang Jianjun, Wang Xinggang, Chen Yaobing, Wang Guoping, Xia Tian
Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.
iScience. 2023 Jun 29;26(10):107243. doi: 10.1016/j.isci.2023.107243. eCollection 2023 Oct 20.
Image-based AI has thrived as a potentially revolutionary tool for predicting molecular biomarker statuses, which aids in categorizing patients for appropriate medical treatments. However, many methods using hematoxylin and eosin-stained (H&E) whole-slide images (WSIs) have been found to be inefficient because of the presence of numerous uninformative or irrelevant image patches. In this study, we introduced the region of biomarker relevance (ROB) concept to identify the morphological areas most closely associated with biomarkers for accurate status prediction. We actualized this concept within a framework called saliency ROB search (SRS) to enable efficient and effective predictions. By evaluating various lung adenocarcinoma (LUAD) biomarkers, we showcased the superior performance of SRS compared to current state-of-the-art AI approaches. These findings suggest that AI tools, built on the ROB concept, can achieve enhanced molecular biomarker prediction accuracy from pathological images.
基于图像的人工智能已蓬勃发展成为一种具有潜在革命性的工具，用于预测分子生物标志物状态，这有助于对患者进行分类以便进行适当的医学治疗。然而，由于存在大量无信息或不相关的图像块，许多使用苏木精和伊红染色（H&E）全切片图像（WSIs）的方法已被发现效率低下。在本研究中，我们引入了生物标志物相关区域（ROB）概念，以识别与生物标志物最密切相关的形态学区域，用于准确的状态预测。我们在一个称为显著性ROB搜索（SRS）的框架内实现了这一概念，以实现高效且有效的预测。通过评估各种肺腺癌（LUAD）生物标志物，我们展示了SRS与当前最先进的人工智能方法相比的卓越性能。这些发现表明，基于ROB概念构建的人工智能工具可以从病理图像中提高分子生物标志物预测的准确性。
