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计算病理学在乳腺癌诊断、免疫微环境识别和免疫治疗评估中的应用进展:叙述性综述。

Advances in the application of computational pathology in diagnosis, immunomicroenvironment recognition, and immunotherapy evaluation of breast cancer: a narrative review.

机构信息

Department of Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, Guangxi, People's Republic of China.

Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China.

出版信息

J Cancer Res Clin Oncol. 2023 Oct;149(13):12535-12542. doi: 10.1007/s00432-023-05002-8. Epub 2023 Jun 30.

Abstract

BACKGROUND

Breast cancer (BC) is a prevalent and highly lethal malignancy affecting women worldwide. Immunotherapy has emerged as a promising therapeutic strategy for BC, offering potential improvements in patient survival. Neoadjuvant therapy (NAT) has also gained significant clinical traction. With the advancement of computer technology, Artificial Intelligence (AI) has been increasingly applied in pathology research, expanding and redefining the scope of the field. This narrative review aims to provide a comprehensive overview of the current literature on the application of computational pathology in BC, specifically focusing on diagnosis, immune microenvironment recognition, and the evaluation of immunotherapy and NAT response.

METHODS

A thorough examination of relevant literature was conducted, focusing on studies investigating the role of computational pathology in BC diagnosis, immune microenvironment recognition, and immunotherapy and NAT assessment.

RESULTS

The application of computational pathology has shown significant potential in BC management. AI-based techniques enable improved diagnosis and classification of BC subtypes, enhance the identification and characterization of the immune microenvironment, and facilitate the evaluation of immunotherapy and NAT response. However, challenges related to data quality, standardization, and algorithm development still need to be addressed.

CONCLUSION

The integration of computational pathology and AI has transformative implications for BC patient care. By leveraging AI-based technologies, clinicians can make more informed decisions in diagnosis, treatment planning, and therapeutic response assessment. Future research should focus on refining AI algorithms, addressing technical challenges, and conducting large-scale clinical validation studies to facilitate the translation of computational pathology into routine clinical practice for BC patients.

摘要

背景

乳腺癌(BC)是一种常见且高度致命的恶性肿瘤,影响着全球范围内的女性。免疫疗法已成为 BC 的一种有前途的治疗策略,有望提高患者的生存率。新辅助治疗(NAT)也获得了显著的临床关注。随着计算机技术的进步,人工智能(AI)在病理学研究中得到了越来越多的应用,拓展并重新定义了该领域的范围。本综述旨在全面概述当前计算病理学在 BC 中的应用文献,重点关注诊断、免疫微环境识别以及免疫疗法和 NAT 反应评估。

方法

对相关文献进行了全面审查,重点研究了计算病理学在 BC 诊断、免疫微环境识别以及免疫疗法和 NAT 评估中的作用。

结果

计算病理学在 BC 管理中的应用具有显著的潜力。基于 AI 的技术能够改善 BC 亚型的诊断和分类,增强对免疫微环境的识别和特征描述,并有助于评估免疫疗法和 NAT 反应。然而,数据质量、标准化和算法开发等相关挑战仍需解决。

结论

计算病理学与 AI 的结合对 BC 患者的护理具有变革性意义。通过利用基于 AI 的技术,临床医生可以在诊断、治疗计划和治疗反应评估方面做出更明智的决策。未来的研究应侧重于改进 AI 算法、解决技术挑战,并进行大规模的临床验证研究,以促进计算病理学在 BC 患者中的常规临床应用。

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