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Peer-reviewedCC BY 4.0PLOS One

MR-based radiomics of mesorectal fat for improved prediction of perirectal lymph node metastasis and extramural venous invasion in locally advanced rectal cancer

  • Yaniga Swaengdee1
  • Sararas Khongwirotphan2
  • Jaravee Lasode3
  • Phakakarn Kuecharoen4
  • Phathayphout Phetvilay5
  • Thitithep Limvorapitak6
  • Anapat Sanpavat7
  • Sira Sriswasdi8
  • Piyaporn Boonsirikamchai9
  • Yothin Rakvongthai10
10 affiliations
  1. 1Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
  2. 2Chulalongkorn Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
  3. 3Department of Radiological Technology and Medical Physics, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
  4. 4Division of Diagnostic Radiology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
  5. 5Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
  6. 6Colorectal Division, Department of Surgery, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
  7. 7Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
  8. 8Center for Artificial Intelligence in Medicine, Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
  9. 9Center of Excellence in Computational Molecular Biology, Chulalongkorn University, Bangkok, Thailand
  10. 10Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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Abstract

Objective: Accurately assessing residual disease after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) remains challenging. Residual extramural venous invasion (EMVI) and perirectal lymph node (PLN) metastasis indicate adverse outcomes, but treatment-related changes obscure their detection on post-treatment MRI. This study developed MRI-based radiomics models to predict residual EMVI and PLN metastasis using post-nCRT restaging MRI. Materials and methods: In this retrospective study, 219 patients with LARC who completed nCRT and underwent post-treatment MRI for restaging prior to surgery were included. Radiomic features were extracted from manually segmented regions of interest encompassing the primary tumor and mesorectal fat on high-resolution T2-weighted images using PyRadiomics. Logistic regression (LR), support vector machine (SVM), and random forest (RF) models were developed to predict pathological EMVI and PLN status. Model performance was assessed using repeated 5-fold cross-validation, with the area under the receiver operating characteristic curve (AUC) as the primary evaluation metric. Differences in model performance were compared using DeLong test. Results: For EMVI prediction, the combined tumor and mesorectal fat radiomics model achieved the highest AUC of 0.797 ± 0.073 using the LR model. For PLN prediction, the combined model also demonstrated superior performance, achieving an AUC of 0.824 ± 0.073. Models incorporating both tumor and mesorectal fat features consistently outperformed single-region models. Conclusion: MRI-based radiomics models using post-nCRT restaging images could predict residual EMVI and PLN metastasis in LARC. Incorporating mesorectal fat features improved model performance, suggesting that information from the surrounding mesorectal compartment may be useful for post-treatment risk assessment.

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Cite

BibTeXRIS
BibTeX
@article{swaengdee2026mrbased,
  title = {{MR-based radiomics of mesorectal fat for improved prediction of perirectal lymph node metastasis and extramural venous invasion in locally advanced rectal cancer}},
  author = {Yaniga Swaengdee and Sararas Khongwirotphan and Jaravee Lasode and Phakakarn Kuecharoen and Phathayphout Phetvilay and Thitithep Limvorapitak and Anapat Sanpavat and Sira Sriswasdi and Piyaporn Boonsirikamchai and Yothin Rakvongthai},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0357268},
  url = {https://doi.org/10.1371/journal.pone.0357268},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - MR-based radiomics of mesorectal fat for improved prediction of perirectal lymph node metastasis and extramural venous invasion in locally advanced rectal cancer
AU  - Yaniga Swaengdee
AU  - Sararas Khongwirotphan
AU  - Jaravee Lasode
AU  - Phakakarn Kuecharoen
AU  - Phathayphout Phetvilay
AU  - Thitithep Limvorapitak
AU  - Anapat Sanpavat
AU  - Sira Sriswasdi
AU  - Piyaporn Boonsirikamchai
AU  - Yothin Rakvongthai
PY  - 2026
DA  - 2026/09/02
JO  - PLOS One
DO  - 10.1371/journal.pone.0357268
UR  - https://doi.org/10.1371/journal.pone.0357268
AB  - Objective: Accurately assessing residual disease after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) remains challenging. Residual extramural venous invasion (EMVI) and perirectal lymph node (PLN) metastasis indicate adverse outcomes, but treatment-related changes obscure their detection on post-treatment MRI. This study developed MRI-based radiomics models to predict residual EMVI and PLN metastasis using post-nCRT restaging MRI. Materials and methods: In this retrospective study, 219 patients with LARC who completed nCRT and underwent post-treatment MRI for restaging prior to surgery were included. Radiomic features were extracted from manually segmented regions of interest encompassing the primary tumor and mesorectal fat on high-resolution T2-weighted images using PyRadiomics. Logistic regression (LR), support vector machine (SVM), and random forest (RF) models were developed to predict pathological EMVI and PLN status. Model performance was assessed using repeated 5-fold cross-validation, with the area under the receiver operating characteristic curve (AUC) as the primary evaluation metric. Differences in model performance were compared using DeLong test. Results: For EMVI prediction, the combined tumor and mesorectal fat radiomics model achieved the highest AUC of 0.797 ± 0.073 using the LR model. For PLN prediction, the combined model also demonstrated superior performance, achieving an AUC of 0.824 ± 0.073. Models incorporating both tumor and mesorectal fat features consistently outperformed single-region models. Conclusion: MRI-based radiomics models using post-nCRT restaging images could predict residual EMVI and PLN metastasis in LARC. Incorporating mesorectal fat features improved model performance, suggesting that information from the surrounding mesorectal compartment may be useful for post-treatment risk assessment.
SN  - 1932-6203
ER  -