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基于人工智能的放射组学模型预测肿瘤微环境-乳腺癌免疫微环境的新型生物标志物。

Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence-the Novel Biomarkers in Breast Cancer Immune Microenvironment.

机构信息

Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.

出版信息

Technol Cancer Res Treat. 2023 Jan-Dec;22:15330338231218227. doi: 10.1177/15330338231218227.

Abstract

Breast cancer is the most common malignancy in women, and some subtypes are associated with a poor prognosis with a lack of efficacious therapy. Moreover, immunotherapy and the use of other novel antibody‒drug conjugates have been rapidly incorporated into the standard management of advanced breast cancer. To extract more benefit from these therapies, clarifying and monitoring the tumor microenvironment (TME) status is critical, but this is difficult to accomplish based on conventional approaches. Radiomics is a method wherein radiological image features are comprehensively collected and assessed to build connections with disease diagnosis, prognosis, therapy efficacy, the TME, etc In recent years, studies focused on predicting the TME using radiomics have increasingly emerged, most of which demonstrate meaningful results and show better capability than conventional methods in some aspects. Beyond predicting tumor-infiltrating lymphocytes, immunophenotypes, cytokines, infiltrating inflammatory factors, and other stromal components, radiomic models have the potential to provide a completely new approach to deciphering the TME and facilitating tumor management by physicians.

摘要

乳腺癌是女性最常见的恶性肿瘤,某些亚型缺乏有效治疗方法,预后较差。此外,免疫疗法和其他新型抗体药物偶联物的使用已迅速纳入晚期乳腺癌的标准治疗。为了从这些治疗中获得更多益处,明确和监测肿瘤微环境(TME)状态至关重要,但这很难通过常规方法实现。放射组学是一种方法,其中全面收集和评估放射影像学特征,以建立与疾病诊断、预后、治疗效果、TME 等的联系。近年来,使用放射组学预测 TME 的研究越来越多,其中大多数研究结果有意义,并且在某些方面比传统方法具有更好的能力。放射组学模型除了预测肿瘤浸润淋巴细胞、免疫表型、细胞因子、浸润性炎症因子和其他基质成分外,还有可能提供一种全新的方法来破译 TME,并帮助医生进行肿瘤管理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8a/10734346/d4915a9fa2e3/10.1177_15330338231218227-fig1.jpg

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