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Radiologist-Informed Radiomics: Improving the Accuracy of Preoperative Assessment for Lymph Node Metastasis in Rectal

Chunl-Ong Fu¹, Long Zhou¹, Ze-Bin Yang¹

¹Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang 322100, China (C.O.F., L.Z., Z.B.Y., K.F.S., J.J.X., Z.Z.P., C.J.M., J.P.X., W.H.Z., F.H.Z.).

Academic Radiology

|July 13, 2026

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A new multimodal radiomics model improves preoperative lymph node metastasis (LNM) prediction in rectal cancer (RC) by integrating radiologist insights. This approach aids in personalized treatment strategies, though further validation is needed.

Area of Science:

Radiology
Oncology
Medical Imaging Analysis

Background:

Accurate preoperative lymph node metastasis (LNM) prediction is crucial for rectal cancer (RC) treatment planning.
Current methods may not fully capture the complexity of LNM in RC.
Radiomics offers a quantitative approach to analyze medical images for diagnostic insights.

Purpose of the Study:

To develop and validate a multimodal radiomics model for enhanced preoperative LNM prediction in RC.
To integrate radiologist-informed feature augmentation using expert-selected suspicious lymph nodes (LNs) based on ESGAR criteria.
To improve the accuracy of LNM assessment compared to traditional methods.

Main Methods:

Retrospective analysis of 563 RC patients' high-resolution T2-weighted imaging (HRT2WI) and diffusion-weighted imaging (DWI).

Keywords:

Imaging histology Lymph node metastasis Predictive modeling Prior knowledge Rectal cancer

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Extraction of radiomic features from the primary tumor, entire mesorectal nodal region, and radiologist-identified suspicious LNs.

Development of integrated predictive models combining clinical factors with multimodal radiomic features.

Main Results:

The integrated model, using DWI features from the nodal region and suspicious LNs plus clinical factors, showed optimal performance.
Achieved an area under the curve (AUC) of 0.87 (internal validation) and 0.83 (external validation).
Demonstrated the potential of multimodal radiomics in predicting LNM in RC.

Conclusions:

The multimodal radiomics model integrating radiologist knowledge shows promise for improving preoperative LNM assessment in RC.
This model may support personalized treatment strategies.
Further multi-center prospective studies are warranted to confirm the incremental benefit of radiologist-informed features in external validation.