Journal of International Oncology ›› 2024, Vol. 51 ›› Issue (5): 292-297.doi: 10.3760/cma.j.cn371439-20240108-00049

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Research progress of habitat analysis in radiomics of malignant tumors

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

  1. Department of Radiation Oncology, First Affiliated Hospital of Soochow University, Suzhou 215006, China
  • Received:2024-01-08 Revised:2024-03-15 Online:2024-05-08 Published: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)

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