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

Abstract

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.

Keywords: biomarker; breast cancer; immunotherapy; radiological images; radiomics; tumor microenvironment.

Figure 1.

Four types of approaches to evaluate tumor microenvironment (TME). They include pathological observation, peripheral blood detection, radiomics, and genome analysis. (Figure 1 is created with BioRender.com and has obtained confirmation of publication and licensing rights).

Figure 2.

Summary of TME-predicting radiomic building process. TME-predicting radiomic model can evaluate various components (including neutrophil, NK cell, lymphocyte etc) through analysis of correlation with radiological images features (shape features, histogram features, texture features and wavelet-transformed features etc). Abbreviations: TCIA, The Cancer Imaging Archive; TME, tumor microenvironment; NK, natural killer cells. (Pathological and radiological images in Figure 2 are cited from the open-access database TCIA. Parts of cell pictures used in Figure 2 from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/)).

Figure 3.

Concept of radiogenomics. There are two types of radiogenomics studies. One is to build the relationship between radiological images and genome. Another is the prediction of TME (for example) through combination of radiological images and genome. Abbreviations: TCIA, The Cancer Imaging Archive; TME, tumor microenvironment. (Radiological images in Figure 3 are cited from open-access database TCIA).