Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction

无线电技术 医学 乳腺癌 肿瘤微环境 分类器(UML) 磁共振成像 总体生存率 机器学习 随机森林 肿瘤科 癌症 放射科 内科学 人工智能 计算机科学
作者
Xiaorui Han,Zhengze Gong,Yuan Guo,Wenjie Tang,Xinhua Wei
出处
期刊:European Journal of Radiology [Elsevier BV]
卷期号:175: 111441-111441 被引量:3
标识
DOI:10.1016/j.ejrad.2024.111441
摘要

Rationale and Objectives: Breast cancer progression and treatment response are significantly influenced by the tumor microenvironment (TME). Traditional methods for assessing TME are invasive, posing a challenge for patient care. This study introduces a non-invasive approach to TME classification by integrating radiomics and machine learning, aiming to predict the TME status using imaging data, thereby aiding in prognostic outcome prediction. Materials and Methods Utilizing multi-omics data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), this study employed CIBERSORT and MCP-counter algorithms analyze immune infiltration in breast cancer. A radiomics classifier was developed using a random forest algorithm, leveraging quantitative features extracted from intratumoral and peritumoral regions of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans. The classifer's ability to predict diverse TME states were and their prognostic implications were evaluated using Kaplan-Meier survival curves. Results Three distinct TME states were identified using RNA-Seq data, each displaying unique prognostic and biological characteristics. Notably, patients with increased immune cell infiltration showed significantly improved prognoses (P < 0.05). The classifier, comprising 24 radiomic features, demonstrated high predictive accuracy (AUC of training set = 0.960, 95 % CI: 0.922, 0.997; AUC of testing set = 0.853, 95 % CI: 0.687, 1.000) in differentiating these TME states. Predictions from the classifier also correlated significantly with overall patient survival (P < 0.05). Conclusion This study offers a detailed analysis of the complex TME states in breast cancer and presents a reliable, noninvasive radiomics classifier for TME assessment. The classifer's accurate prediction of TME status and its correlation with prognosis highlight its potential as a tool in personalized breast cancer treatment, paving the way for more individualized and less invasive therapeutic strategies.
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