Deep learning radiomics: Redefining precision oncology through noninvasive insights into the tumor immune microenvironment

Abstract

Computed tomography-based deep learning radiomics provides a novel, noninvasive approach to predicting the tumor immune microenvironment in colorectal cancer, revolutionizing precision oncology. The retrospective study by Zhou et al analyzed preoperative computed tomography scans from 315 patients using convolutional neural networks, achieving robust predictive performance (area under the curve: 0.851-0.892) for critical tumor immune microenvironment features, such as tumor-stroma ratio and lymphocyte infiltration, without requiring invasive biopsies. This editorial explores how this technique advances personalized immunotherapy, chemotherapy, and targeted therapies; challenges conventional oncology practices; and paves the way for a future of precision medicine. By integrating advanced imaging with immune profiling, deep learning radiomics redefines colorectal cancer management, highlighting the need to re-evaluate the interplay of technology, biology, and ethics in gastrointestinal oncology.

Keywords: Colorectal cancer; Radiomics; Tumor immune microenvironment; Therapy; Immunotherapy

Core Tip: Computed tomography-based deep learning radiomics provides a noninvasive, scalable approach to predict the tumor immune microenvironment in colorectal cancer, achieving high accuracy (area under the curve: 0.851-0.892) in the study by Zhou et al. By overcoming biopsy limitations, the approach revolutionizes personalized immunotherapy, chemotherapy, and targeted therapies, integrating advanced imaging with immune profiling to redefine precision oncology and improve patient outcomes.