The central insight driving the review is that the tumor microenvironment is not a passive backdrop but an active determinant of both treatment response and clinical outcomes. In colorectal cancer, the composition of immune cells infiltrating a tumor, the ratio of tumor cells to stroma, the presence of tertiary lymphoid structures and the burden of tumor-infiltrating lymphocytes all carry prognostic and predictive weight. Yet these features are traditionally measured on tissue samples obtained through biopsy or surgery, which capture only a fragment of the tumor and cannot be repeated freely over time. Radiomics offers an alternative. By extracting hundreds of quantitative features from routine medical images, such as computed tomography and magnetic resonance imaging, radiomics aims to characterize the whole tumor, including its internal heterogeneity, without touching the patient.

Technically, radiomics pipelines convert standard acquisitions such as T2-weighted imaging, contrast-enhanced T1-weighted imaging and diffusion-weighted imaging into high-dimensional feature sets. First-pass texture statistics capture local gray-level variation, while higher-order features extracted through filtered images and deep learning networks probe patterns that the human eye cannot resolve. Apparent diffusion coefficient maps derived from diffusion-weighted imaging, for example, reflect tissue cellularity and can serve as indirect surrogates of tumor density and stromal content. The review organizes the colorectal cancer radiomics literature into three thematic clusters: features that correlate directly with microenvironment components, features associated with vascular-invasion-related phenotypes, and features tied to tumor-intrinsic properties that are themselves shaped by the microenvironment. Each cluster, the authors argue, contributes a different piece of the puzzle linking what radiologists see on a screen to what pathologists see under a microscope and what molecular biologists sequence in the lab.

The vascular-invasion theme is particularly consequential clinically. Extramural venous invasion and microvascular invasion are established markers of poor prognosis in colorectal cancer, signaling that tumor cells have entered the circulatory system and raised the risk of metastasis. Radiomic models trained on CT and MRI can flag these phenotypes before surgery, potentially informing decisions about neoadjuvant therapy and surgical planning. Meanwhile, radiomic signatures predicting microsatellite instability and deficient mismatch repair status offer a non-invasive proxy for the single most important biomarker in modern colorectal cancer immunotherapy, since patients with dMMR or MSI-high tumors are the ones most likely to benefit from immune checkpoint inhibitors. In locally advanced rectal cancer, radiomic models have also been used to predict pathological complete response after chemoradiotherapy, a finding that could eventually help identify patients for organ-preserving strategies.

Pathomics extends the same quantitative logic to the microscopic scale. Whole-slide imaging digitizes histopathology slides, and computational methods then profile tumor architecture and the spatial distribution of immune cells at high throughput. Where a pathologist might estimate tumor-infiltrating lymphocyte density visually, pathomic pipelines can quantify it precisely, map the spatial relationships between tumor nests and stromal compartments, and compute the tumor-stroma ratio automatically. Deep learning models trained on whole-slide images can even predict molecular alterations, such as microsatellite instability, directly from hematoxylin and eosin-stained tissue. Because pathomics operates on resected or biopsied tissue, it provides the microscopic ground truth that radiomics lacks, and the two approaches are natural partners: radiomics sees the whole tumor in vivo, while pathomics resolves the cellular detail of the sampled regions.

The most ambitious portion of the review describes cross-scale integration, in which radiomic and pathomic features are fused with genomics and transcriptomics to trace a continuous chain from macroscopic phenotype to molecular mechanism. Radiomic features that predict microsatellite instability, for instance, can be connected to the immune-inflamed transcriptional programs that accompany deficient mismatch repair, including upregulated checkpoint molecules and enriched cytotoxic T-cell signatures. Consensus molecular subtypes of colorectal cancer, which stratify tumors by their gene-expression patterns, also leave imaging fingerprints, and studies reviewed by the authors show that radiomic models can distinguish between these molecular classes with useful accuracy. At the single-cell and spatial-omics frontier, technologies such as single-cell RNA sequencing and spatially resolved transcriptomics reveal the precise cellular neighborhoods within the microenvironment, including interactions between tumor-associated macrophages, myeloid-derived suppressor cells, cancer-associated fibroblasts and lymphocytes, offering mechanistic explanations for the imaging features that models detect.

Multimodal fusion is the methodological glue holding this framework together. Rather than treating imaging, pathology and molecular data as parallel silos, fusion approaches combine them within a single predictive model, allowing each modality to compensate for the blind spots of the others. Deep learning architectures can ingest radiomic features from CT or MRI, pathomic features from whole-slide images, and genomic or transcriptomic profiles from the same patient, learning joint representations that outperform any single data type. The review highlights early studies demonstrating that such combined models improve prediction of prognosis, immunotherapy response and treatment-related outcomes in colorectal cancer compared with unimodal baselines, suggesting that the cross-scale framework is more than the sum of its parts.

The authors are explicit, however, about the caveats. Much of the evidence reviewed is retrospective, derived from single-center cohorts with limited sample sizes, and few radiomic models have been validated prospectively or across diverse populations. Standardization of image acquisition, feature definitions and model reporting remains inconsistent across the field, raising concerns about reproducibility. The relationship between imaging features and microenvironment biology is often correlational rather than mechanistically established, and the review repeatedly emphasizes that clinical utility, and any role in therapeutic decision-making, remain to be established through prospective validation. These are not trivial hurdles; they are the same obstacles that have slowed the translation of radiomics in other cancer types.

Even so, the trajectory described in the review is striking. What began as an effort to squeeze extra information out of images that radiologists already acquire routinely has matured into a multi-scale program that connects the radiology suite to the pathology lab and the sequencing core. If prospective studies bear out the promise of imaging-driven multi-omics integration, clinicians could one day profile a patient’s tumor microenvironment repeatedly, cheaply and non-invasively, tracking its evolution under therapy and selecting patients for immunotherapy with far greater precision than today’s single-timepoint biomarkers allow. For a disease that kills hundreds of thousands of people each year and frustrates clinicians with its heterogeneity, that would represent a genuinely transformative shift, one that this review maps out with unusual technical clarity and admirable restraint about what has, and has not, yet been proven.

Subject of Research: Decoding the tumor microenvironment in colorectal cancer through radiomics and multi-omics integration

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

Article References: Gong, L., Wu, X., Zhang, W., Lai, B., Yuan, J., Gu, Y., Shen, H., Liu, X., Xiong, Y., Zheng, J., Wang, L., Han, X., Zhang, B., & Zhang, S. (2026). From imaging to multi-omics: decoding the tumor microenvironment in colorectal cancer. Journal of Translational Medicine. https://doi.org/10.1186/s12967-026-08958-6

Image Credits: AI Generated

DOI: 10.1186/s12967-026-08958-6

Keywords: colorectal cancer, tumor microenvironment, radiomics, pathomics, multi-omics, immunotherapy, microsatellite instability, deep learning, spatial omics, tumor heterogeneity, precision oncology, biomarkers