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

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...

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Bibliographic Details
Published in:Technology in Cancer Research & Treatment 2023-01, Vol.22, p.15330338231218227-15330338231218227
Main Authors: Lin, Guang, Wang, Xiaojia, Ye, Hunan, Cao, Wenming
Format: Article
Language:English
Subjects:
Artificial Intelligence
Biomarkers
Biomarkers, Tumor
Breast cancer
Breast Neoplasms - diagnostic imaging
Female
Humans
Immunotherapy
Inflammation
Lymphocytes
Malignancy
Navigating Breast Health: From Screening to Survivorship
Prognosis
Radiomics
Tumor Microenvironment
Tumor-infiltrating lymphocytes
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Summary: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.
ISSN:1533-0346
1533-0338
DOI:10.1177/15330338231218227