MRI‐Derived Lymph Nodes Morphological and Topological Structure (LNs‐MTS) Model for Evaluating Immune Status and Prognosis in Rectal Cancer
Tumor-draining lymph nodes (LNs) immune status critically influences cancer progression and treatment response, yet reliable non-invasive assessment remains clinically unavailable. To address this critical gap, an MRI-derived LNs morphological and topological structure (LNs-MTS) model is developed to evaluate the immune status of LNs in rectal cancer (RC). Integrating multicenter imaging, transcriptomic, and immunohistochemical data from 1,156 stage I-II RC patients, enhanced immune activation in non-metastatic LNs sized ≥0.5 cm and located ≥5 cm from the primary tumor is discovered. Then two quantitative MRI-derived imaging features across 7,030 radiologically annotated LNs: total LNs volume (tLNV) and total LNs drainage distance (tLND) is developed, forming the basis of the LNs-MTS model risk subtypes: high-risk (HRS), moderate-risk (MRS), and low-risk (LRS). Patients in the LRS (characterized by large tLNV and distant tLND) show robust immune cell infiltration in the tumor microenvironment and excellent 5-year survival, whereas HRS (characterized by small tLNV and near tLND) show stromal dominance and poorer prognosis. Clinically, LNs-MTS offers a more precise and personalized risk assessment than current guideline-based risk stratification, potentially sparing stage II-LRS patients from unnecessary adjuvant treatment while identifying stage I-HRS individuals for more aggressive treatment, providing valuable insights for personalized treatment strategies in RC management.
Authors
- Yanfen Cui (ORCID: https://orcid.org/0000-0001-6631-5687)
- Xishan Wang (ORCID: https://orcid.org/0000-0002-1675-5083)
- Jun Xiang (ORCID: https://orcid.org/0000-0002-8026-3294)
- Jinchen Li (ORCID: https://orcid.org/0009-0002-3893-8128)
- Xu Guan (ORCID: https://orcid.org/0000-0003-0997-498X)
- Jianhua Ma (ORCID: https://orcid.org/0000-0001-9383-2559)
- Shuai Jiao (ORCID: https://orcid.org/0000-0002-9100-3172)
- Liwen Zhang (ORCID: https://orcid.org/0000-0001-9816-1344)
- Hanqing Hu (ORCID: https://orcid.org/0000-0003-3680-1582)
- Yunxiao Liu (ORCID: https://orcid.org/0000-0003-4086-5044)
- Guiyu Wang (ORCID: https://orcid.org/0000-0001-7166-6377)
- Xiaotang Yang
- Haiyi Liu (ORCID: https://orcid.org/0000-0003-4289-8480)
- Jinna Li (ORCID: https://orcid.org/0000-0002-6834-0760)
- Weiyuan Zhang
- Jie Tian
Institutions
- Harbin Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Shandong Institute of Automation (CN)
- Shanxi Provincial Cancer Hospital (CN)
- Second Affiliated Hospital of Harbin Medical University (CN)
- Cancer Hospital of Chinese Academy of Medical Sciences (CN)
- Beihang University (CN)
Publication Details
- Journal
- Advanced Science
- Published
- 2025-08-31
- DOI
- https://doi.org/10.1002/advs.202506523
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Science and Technology Major Project