Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France; Laboratoire d'Informatique Paris Descartes (LIPADE), Université de Paris, Paris, France.
Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France.
J Hepatol. 2022 Jul;77(1):116-127. doi: 10.1016/j.jhep.2022.01.018. Epub 2022 Feb 7.
BACKGROUND & AIMS: Patients with hepatocellular carcinoma (HCC) displaying overexpression of immune gene signatures are likely to be more sensitive to immunotherapy, however, the use of such signatures in clinical settings remains challenging. We thus aimed, using artificial intelligence (AI) on whole-slide digital histological images, to develop models able to predict the activation of 6 immune gene signatures.
AI models were trained and validated in 2 different series of patients with HCC treated by surgical resection. Gene expression was investigated using RNA sequencing or NanoString technology. Three deep learning approaches were investigated: patch-based, classic MIL and CLAM. Pathological reviewing of the most predictive tissue areas was performed for all gene signatures.
The CLAM model showed the best overall performance in the discovery series. Its best-fold areas under the receiver operating characteristic curves (AUCs) for the prediction of tumors with upregulation of the immune gene signatures ranged from 0.78 to 0.91. The different models generalized well in the validation dataset with AUCs ranging from 0.81 to 0.92. Pathological analysis of highly predictive tissue areas showed enrichment in lymphocytes, plasma cells, and neutrophils.
We have developed and validated AI-based pathology models able to predict the activation of several immune and inflammatory gene signatures. Our approach also provides insights into the morphological features that impact the model predictions. This proof-of-concept study shows that AI-based pathology could represent a novel type of biomarker that will ease the translation of our biological knowledge of HCC into clinical practice.
Immune and inflammatory gene signatures may be associated with increased sensitivity to immunotherapy in patients with advanced hepatocellular carcinoma. In the present study, the use of artificial intelligence-based pathology enabled us to predict the activation of these signatures directly from histology.
表达免疫基因特征的肝细胞癌（HCC）患者可能对免疫治疗更敏感，然而，在临床环境中使用此类特征仍然具有挑战性。因此，我们旨在通过全切片数字组织学图像的人工智能（AI），开发能够预测 6 种免疫基因特征激活的模型。
使用 RNA 测序或 NanoString 技术，在接受手术切除治疗的 HCC 患者的 2 个不同系列中对 AI 模型进行训练和验证。使用 3 种深度学习方法进行研究：基于斑块、经典 MIL 和 CLAM。对所有基因特征的最具预测性组织区域进行病理复查。
CLAM 模型在发现系列中表现出最佳的总体性能。其用于预测免疫基因特征上调肿瘤的最佳折叠接收者操作特征曲线（AUC）范围为 0.78 至 0.91。不同模型在验证数据集的表现也很好，AUC 范围为 0.81 至 0.92。对高预测性组织区域的病理分析显示淋巴细胞、浆细胞和中性粒细胞富集。
我们已经开发并验证了基于 AI 的病理学模型，能够预测几种免疫和炎症基因特征的激活。我们的方法还提供了有关影响模型预测的形态特征的见解。这项概念验证研究表明，基于 AI 的病理学可能代表一种新型生物标志物，将有助于将我们对 HCC 的生物学认识转化为临床实践。
免疫和炎症基因特征可能与晚期肝细胞癌患者对免疫治疗的敏感性增加有关。在本研究中，人工智能病理学的应用使我们能够直接从组织学预测这些特征的激活。
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