Translating Molecular Subtypes into Cost-Effective Radiogenomic Biomarkers for Prognosis of Colorectal Cancer

Jan 14, 2026·
Bao-Wen GAI
Bao-Wen GAI
1st Author
Xin DUAN
Xin DUAN
Cheng-Hang LI
Cheng-Hang LI
Chu-Ling HU
Chu-Ling HU
Min-Yi LV
Min-Yi LV
Jia-Xin LEI
Jia-Xin LEI
Run-Xian WANG
Run-Xian WANG
Feng GAO
Feng GAO
Co-Corresponding Author
Du CAI
Du CAI
Corresponding Author
· 0 min read
Abstract
This study develops a radiogenomic framework to translate colorectal cancer molecular subtype information into clinically feasible prognostic biomarkers. Across 2948 patients from eight cohorts, the authors established both a subtype-associated gene signature and a non-invasive radiogenomic signature, each showing robust risk stratification performance. The work provides a cost-effective path for prognosis assessment and potential treatment guidance in routine clinical settings.
Type
Publication
Diagnostics
publication
Bao-Wen GAI
Authors
PhD Student
I am a PhD student working on AI methods for colorectal cancer diagnosis and prognosis.
Xin DUAN
Authors
Postdoc
I focus on medical image analysis and artificial intelligence for cancer research, including molecular subtyping and predictive modeling.
Cheng-Hang LI
Authors
Research Assistant
I am a research assistant focusing on deep learning, multimodal feature fusion, and medical AI system development.
Chu-Ling HU
Authors
PhD Student
I am a PhD student focusing on AI-driven colorectal cancer research and clinically useful model development.
Min-Yi LV
Authors
PhD Student
I am a PhD student focusing on colorectal cancer research, biostatistics, and evidence-driven clinical modeling.
Jia-Xin LEI
Authors
Research Assitant
I am a research assistant interested in deep learning and medical image analysis for clinical applications.
Run-Xian WANG
Authors
Research Student
I am a PhD student focusing on AI-based colorectal cancer research and predictive clinical modeling.
Feng GAO
Authors
Professor
My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.
Du CAI
Authors
Postdoc
I focus on leveraging explainable AI and large foundation models to advance medical imaging and digital pathology in colorectal cancer research.