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.

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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

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article

MRI‐Derived Lymph Nodes Morphological and Topological Structure (LNs‐MTS) Model for Evaluating Immune Status and Prognosis in Rectal Cancer

Yanfen Cui, Xishan Wang, Jun Xiang, Jinchen Li et al.
Advanced Science
Radiomics and Machine Learning in Medical Imaging
article

MRI‐Derived Lymph Nodes Morphological and Topological Structure (LNs‐MTS) Model for Evaluating Immune Status and Prognosis in Rectal Cancer

Yanfen Cui, Xishan Wang, Jun Xiang, Jinchen Li, Xu Guan, Jianhua Ma, Shuai Jiao, Liwen Zhang, Hanqing Hu, Yunxiao Liu, Guiyu Wang, Xiaotang Yang, Haiyi Liu, Jinna Li, Weiyuan Zhang, Jie Tian
article en

Abstract

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.

Advanced ScienceVol. 12(39)
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)
National Natural Science Foundation of China, National Science and Technology Major Project
Openalex Percentile: Top 68%
Radiomics and Machine Learning in Medical Imaging
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