生境分析在恶性肿瘤影像组学中的研究进展

国际肿瘤学杂志››2024,Vol. 51››Issue (5): 292-297.doi:10.3760/cma.j.cn371439-20240108-00049

生境分析在恶性肿瘤影像组学中的研究进展

傅旖, 马辰莺, 张露, 周菊英()

苏州大学附属第一医院放疗科，苏州 215006

收稿日期:2024-01-08修回日期:2024-03-15出版日期:2024-05-08发布日期:2024-06-26
通讯作者:周菊英，Email：zhoujuyingsy@163.com
基金资助:
国家自然科学基金(82373204);国家自然科学基金(81602792);江苏省高等学校基础科学（自然科学）研究项目(23KJB310023);江苏省妇幼保健科研项目(F202210);“十四五”江苏省医学重点学科(ZDXK202235);苏州大学放射医学与辐射防护国家重点实验室资助项目(GZK1202101);苏州市科技计划(SLT201920);苏州市“科教兴卫”青年科技项目(KJXW2020008)

Research progress of habitat analysis in radiomics of malignant tumors

Fu Yi, Ma Chenying, Zhang Lu, Zhou Juying()

Department of Radiation Oncology， First Affiliated Hospital of Soochow University， Suzhou 215006， China

Received:2024-01-08Revised:2024-03-15Online:2024-05-08Published:2024-06-26
Contact:Zhou Juying， Email：zhoujuyingsy@163.com
Supported by:
National Natural Science Foundation of China(82373204);National Natural Science Foundation of China(81602792);Jiangsu Provincial Higher Education Institutions General Program for Basic Sciences （Natural Sciences）(23KJB310023);Research Project of Maternal and Child Health in Jiangsu Province(F202210);"14th Five-Year Plan" Jiangsu Provincial Medical Key Discipline(ZDXK202235);Project of State Key Laboratory of Radiation Medicine and Protection of Soochow University(GZK1202101);Suzhou Science and Technology Plan(SLT201920);Suzhou City "Science and Education Promote Health" Youth Science and Technology Project(KJXW2020008)

摘要/Abstract

摘要：

目前，针对传统影像组学的研究已逐渐成熟，但是它通常将肿瘤视为一个整体，高通量数据往往产生于整个肿瘤区域，无法表达明确的空间异质性。为挖掘肿瘤内部潜在的生物学信息，实现个体化精准诊疗，生境分析技术应运而生。该新技术提供了一种识别肿瘤微环境的新思路，其在传统影像组学的基础上，将具有相似表征的肿瘤细胞群进行聚类，并据此将肿瘤分割为多个亚区域，让肿瘤研究不再受限于观察者对影像特征描述的主观差异，最理想化地得到肿瘤空间异质性信息。

关键词:肿瘤,肿瘤微环境,影像组学,机器学习

Abstract:

Nowadays， the research on traditional radiomics has gradually matured. However， it usually regards the tumor as a whole， and high-throughput data are often generated in the entire tumor region， which cannot express clear spatial heterogeneity. In order to explore the potential biological information within tumors and realize individualized precise diagnosis and treatment， habitat analysis technology emerges at the historic moment， which provides a new way of thinking to identify tumor microenvironment. On the basis of traditional radiomics， the tumor cell population with similar characteristics is clustered， and the tumor is segmented into multiple sub-regions. Therefore， the study of tumor is no longer limited by the subjective differences of observers in the description of imaging features， and the information of tumor spatial heterogeneity is ideally obtained.

Key words:Neoplasms,Tumor microenvironment,Radiomics,Machine learning

傅旖, 马辰莺, 张露, 周菊英. 生境分析在恶性肿瘤影像组学中的研究进展[J]. 国际肿瘤学杂志, 2024, 51(5): 292-297.

Fu Yi, Ma Chenying, Zhang Lu, Zhou Juying. Research progress of habitat analysis in radiomics of malignant tumors[J]. Journal of International Oncology, 2024, 51(5): 292-297.

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