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• Clinical Article •
Prediction of the primary lesion origin of hepatic hypervascular metastases based on MRI multi-lesion habitat radiomics
WANG Jing  JIA Pingfan  WANG Xiaochun 

Cite this article as: WANG J, JIA P F, WANG X C. Prediction of the primary lesion origin of hepatic hypervascular metastases based on MRI multi-lesion habitat radiomics[J]. Chin J Magn Reson Imaging, 2026, 17(4): 70-78. DOI:10.12015/issn.1674-8034.2026.04.010.


[Abstract] Objective To develop and validate a multi-lesion habitat radiomics (ML-HR) model based on late arterial phase MRI and evaluate its value in non-invasively predicting the gastrointestinal (GI) versus non-GI origin of hypervascular liver metastases (HLM).Materials and Methods The clinical and contrast-enhanced MRI Data of 111 HLM patients from two centers were retrospectively included and randomly divided into the training set and the validation set in a 7∶3 ratio. The volume of interest (VOI) of all lesions was delineated on the late-stage arterial images. Local radiomics features were extracted and subregions were divided. Fourteen machine learning algorithms were adopted to respectively construct the traditional single-lesion radiomics (SLR) model, the traditional multi-lesion radiomics (MLR) model and the multi-lesion habitat radiomics (ML-HR) model. To identify whether HLM originates from the GI. The optimal algorithm is screened and the best model is determined through the receiver operating characteristic curve.Results A total of 111 patients (241 lesions) were included, among which the training set (n = 77) and the validation set (n = 34) were included. Decision tree (DT), radial basis function support vector machine (rbf_SVM), and eXtreme Gradient Boosting (XGBoost) were identified as the optimal algorithms for SLR, MLR, and ML-HR models, respectively. The ML-HR model has the best performance. The AUC of the training set is 0.952 (95% confidence interval: 0.904 to 0.988), and that of the validation set is 0.901 (95% confidence interval: 0.765 to 0.997), which is significantly better than the traditional model (P < 0.05).Conclusions The ML-HR model can effectively and non-invasively predict the GI versus non-GI origin of HLM, providing a reliable imaging basis for clinical personalized medicine.
[Keywords] hypervascular liver metastases；gastroIntestinal；habitat radiomics；radiomics；magnetic resonance imaging；personalized medicine

WANG Jing1, 2   JIA Pingfan3   WANG Xiaochun1*  

1 Department of Magnetic Resonance Imaging, the First Hospital of Shanxi Medical University, Taiyuan 030001, China

2 Department of Imaging, Heji Hospital Affiliated to Changzhi Medical College, Changzhi 046000, China

3 Department of Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi 046000, China

Corresponding author: WANG X C, E-mail: 2010xiaochun@163.com

Conflicts of interest   None.

Received  2025-12-18
Accepted  2026-03-19
DOI: 10.12015/issn.1674-8034.2026.04.010
Cite this article as: WANG J, JIA P F, WANG X C. Prediction of the primary lesion origin of hepatic hypervascular metastases based on MRI multi-lesion habitat radiomics[J]. Chin J Magn Reson Imaging, 2026, 17(4): 70-78. DOI:10.12015/issn.1674-8034.2026.04.010.

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