Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Division of Endocrinology and Metabolic Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Front Immunol. 2023 Jul 13;14:1216901. doi: 10.3389/fimmu.2023.1216901. eCollection 2023.
Immunotherapy showed remarkable efficacy in several cancer types. However, the majority of patients do not benefit from immunotherapy. Evaluating tumor heterogeneity and immune status before treatment is key to identifying patients that are more likely to respond to immunotherapy. Demographic characteristics (such as sex, age, and race), immune status, and specific biomarkers all contribute to response to immunotherapy. A comprehensive immunodiagnostic model integrating all these three dimensions by artificial intelligence would provide valuable information for predicting treatment response. Here, we coined the term "immunodiagnosis" to describe the blueprint of the immunodiagnostic model. We illustrated the features that should be included in immunodiagnostic model and the strategy of constructing the immunodiagnostic model. Lastly, we discussed the incorporation of this immunodiagnosis model in clinical practice in hopes of improving the prognosis of tumor immunotherapy.
免疫疗法在多种癌症类型中显示出显著疗效。然而，大多数患者并未从中获益。在治疗前评估肿瘤异质性和免疫状态是识别更有可能对免疫疗法产生反应的患者的关键。人口统计学特征（如性别、年龄和种族）、免疫状态和特定生物标志物都与免疫疗法的反应有关。人工智能整合所有这三个维度的综合免疫诊断模型将为预测治疗反应提供有价值的信息。在这里，我们创造了“免疫诊断”一词来描述免疫诊断模型的蓝图。我们说明了免疫诊断模型应包含的特征和构建免疫诊断模型的策略。最后，我们讨论了将这种免疫诊断模型纳入临床实践，希望改善肿瘤免疫治疗的预后。
