From imaging to multi-omics: decoding the tumor microenvironment in colorectal cancer

Colorectal cancer (CRC) remains one of the most common and lethal malignancies worldwide, characterized by substantial heterogeneity in response to immunotherapy. Accumulating evidence indicates that the tumor microenvironment (TME) plays a pivotal role in determining both treatment response and clinical outcomes. In recent years, radiomics has emerged as a promising non-invasive approach that extracts quantitative features from routine medical images, enabling whole-tumor characterization of the TME and intratumoral heterogeneity. Concurrently, the advent of pathomics allows high-throughput, quantitative profiling of tumor architecture and immune cell spatial distribution, offering complementary insights into the microscopic heterogeneity of the TME. With advances in genomics and transcriptomics, radiomic and pathomic features can now be integrated with molecular data to unravel CRC heterogeneity and TME properties across multiple scales—from macroscopic phenotypes to underlying molecular mechanisms. This narrative review organizes CRC radiomics evidence around direct TME components, vascular-invasion-related phenotypes, and TME-associated tumor-intrinsic features. It further examines cross-scale integration with genomics, transcriptomics, pathomics, and emerging single-cell, spatial-omics, and multimodal-fusion approaches. Collectively, imaging-driven multi-omics integration provides a scalable cross-scale framework for the non-invasive and quantitative assessment of CRC TME-related features, with potential value for precision risk stratification, although its clinical utility and role in therapeutic decision-making remain to be established through prospective validation.

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Publication Details

Journal
Journal of Translational Medicine
Published
2026-09-19
DOI
https://doi.org/10.1186/s12967-026-08958-6
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

From imaging to multi-omics: decoding the tumor microenvironment in colorectal cancer

Liaoyuan Wang, Bin Zhang, Wenlong Zhang, Yue Gu et al.
Journal of Translational Medicine
Radiomics and Machine Learning in Medical Imaging
article

From imaging to multi-omics: decoding the tumor microenvironment in colorectal cancer

Liaoyuan Wang, Bin Zhang, Wenlong Zhang, Yue Gu, Hui Shen, Bo Lai, Xue Han, Liya Gong, Shuixing Zhang, Jinyi Zheng, Jinping Yuan, Yuxin Xiong, Xuewei Wu, Xin Liu
article en

Abstract

Colorectal cancer (CRC) remains one of the most common and lethal malignancies worldwide, characterized by substantial heterogeneity in response to immunotherapy. Accumulating evidence indicates that the tumor microenvironment (TME) plays a pivotal role in determining both treatment response and clinical outcomes. In recent years, radiomics has emerged as a promising non-invasive approach that extracts quantitative features from routine medical images, enabling whole-tumor characterization of the TME and intratumoral heterogeneity. Concurrently, the advent of pathomics allows high-throughput, quantitative profiling of tumor architecture and immune cell spatial distribution, offering complementary insights into the microscopic heterogeneity of the TME. With advances in genomics and transcriptomics, radiomic and pathomic features can now be integrated with molecular data to unravel CRC heterogeneity and TME properties across multiple scales—from macroscopic phenotypes to underlying molecular mechanisms. This narrative review organizes CRC radiomics evidence around direct TME components, vascular-invasion-related phenotypes, and TME-associated tumor-intrinsic features. It further examines cross-scale integration with genomics, transcriptomics, pathomics, and emerging single-cell, spatial-omics, and multimodal-fusion approaches. Collectively, imaging-driven multi-omics integration provides a scalable cross-scale framework for the non-invasive and quantitative assessment of CRC TME-related features, with potential value for precision risk stratification, although its clinical utility and role in therapeutic decision-making remain to be established through prospective validation.

Journal of Translational Medicine
First Affiliated Hospital of Jinan University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 11%
Radiomics and Machine Learning in Medical Imaging
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