Bibliographic Details
| Title: |
Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence—the Novel Biomarkers in Breast Cancer Immune Microenvironment |
| Authors: |
Guang Lin MSc, Xiaojia Wang PhD, Hunan Ye MSc, Wenming Cao MD |
| Source: |
Technology in Cancer Research & Treatment, Vol 22 (2023) |
| Publisher Information: |
SAGE Publishing, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| Subject Terms: |
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1533-0338
15330338 |
| Relation: |
https://doaj.org/toc/1533-0338 |
| DOI: |
10.1177/15330338231218227 |
| Access URL: |
https://doaj.org/article/853c0784d46343fc875b3a93cd83c712 |
| Accession Number: |
edsdoj.853c0784d46343fc875b3a93cd83c712 |
| Database: |
Directory of Open Access Journals |
