Department of Pathology, Keio University School of Medicine, Tokyo, Japan.
Department of Diagnostic Pathology, National Hospital Organization Saitama Hospital, Saitama, Japan.
Jpn J Clin Oncol. 2024 Sep 4;54(9):1009-1023. doi: 10.1093/jjco/hyae066.
The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify lung adenocarcinoma images into seven histological subtypes and adopted the model for whole-slide images to investigate the relationship between the distribution of histological subtypes and clinicopathological factors.
Using histological subtype images, which are typical for pathologists, we trained and validated an artificial intelligence model. Then, the model was applied to whole-slide images of resected lung adenocarcinoma specimens from 147 cases.
The model achieved an accuracy of 99.7% in training sets and 90.4% in validation sets consisting of typical tiles of histological subtyping for pathologists. When the model was applied to whole-slide images, the predominant subtype according to the artificial intelligence model classification matched that determined by pathologists in 75.5% of cases. The predominant subtype and tumor grade (using the WHO fourth and fifth classifications) determined by the artificial intelligence model resulted in similar recurrence-free survival curves to those determined by pathologists. Furthermore, we stratified the recurrence-free survival curves for patients with different proportions of high-grade components (solid, micropapillary and cribriform) according to the physical distribution of the high-grade component. The results suggested that tumors with centrally located high-grade components had a higher malignant potential (P < 0.001 for 5-20% high-grade component).
The new artificial intelligence model for histological subtyping of lung adenocarcinoma achieved high accuracy, and subtype quantification and subtype distribution analyses could be achieved. Artificial intelligence model therefore has potential for clinical application for both quantification and spatial analysis.
肺腺癌的组织学亚型是一个主要的预后因素。我们开发了一种新的人工智能模型,将肺腺癌图像分为七种组织学亚型,并采用该模型对全切片图像进行分析,以研究组织学亚型的分布与临床病理因素之间的关系。
使用组织学亚型图像,这些图像是病理学家典型的,我们对人工智能模型进行了训练和验证。然后,将该模型应用于 147 例肺腺癌切除标本的全切片图像。
该模型在训练集和验证集中的准确率分别达到了 99.7%和 90.4%,验证集由病理学家典型的组织学分型图像组成。当该模型应用于全切片图像时,根据人工智能模型分类的主要亚型与病理学家确定的主要亚型在 75.5%的病例中相匹配。人工智能模型确定的主要亚型和肿瘤分级(使用 WHO 第四和第五分类)与病理学家确定的无复发生存曲线相似。此外,我们根据高级别成分(实体、微乳头状和筛状)的物理分布对不同比例高级别成分患者的无复发生存曲线进行分层。结果表明,中央有高级别成分的肿瘤恶性潜能更高(高级别成分占 5-20%时,P<0.001)。
新的肺腺癌组织学亚型人工智能模型具有较高的准确性,并且可以进行亚型定量和亚型分布分析。因此,人工智能模型具有用于定量和空间分析的临床应用潜力。