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• Clinical Article •
Development and validation of a model for predicting pathological grade of intrahepatic mass-forming cholangiocarcinoma based on intratumoral and peritumoral features on MRI
LI Xiaomeng  XING Lihong  ZHUO Liyong  LIU Xueyan  DAI Shuo  DONG Jinghui  ZHANG Yanyan  LI Hongjun  YIN Xiaoping 

Cite this article as: LI X M, XING L H, ZHUO L Y, et al. Development and validation of a model for predicting pathological grade of intrahepatic mass-forming cholangiocarcinoma based on intratumoral and peritumoral features on MRI[J]. Chin J Magn Reson Imaging, 2025, 16(2): 51-58. DOI:10.12015/issn.1674-8034.2025.02.008.


[Abstract] Objective To explore the value of intratumoral and different peritumoral radiomics features based on T2-weighted imaging (T2WI) and diffusion weighted imaging (DWI), as well as clinical imaging factors, in preoperative prediction of the pathological grade of intrahepatic mass-forming cholangiocarcinoma (IMCC).Materials and Methods A retrospective analysis was conducted on the clinical and preoperative MRI data of 197 patients with IMCC confirmed by postoperative pathology. The region of interest (ROI) of the tumor was delineated on axial T2WI and DWI images, and extended outward by 3 mm, 5 mm, 10 mm, 15 mm, and 20 mm respectively to obtain peritumoral ROIs of different ranges. Radiomics features were extracted by PyRadiomics. Features were screened through homogeneity of variance test, independent sample t-test, recursive feature elimination algorithm and least absolute shrinkage and selection operator. Logistic regression (LR) classifier and 5-fold cross-validation method were used for modeling and verification. Clinical imaging model, intratumoral omics model, peritumoral omics model, intratumoral + peritumoral omics model, dual-sequence fusion model and multimodal combined model were established. The predictive efficacies of each of the above models were compared to select the best model. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the performance of the model. DeLong test was used to compare the differences in AUC. Calibration curve was used to evaluate the fitting ability of the model, and decision curve was used to assess the clinical value of the model.Results In the peritumoral omics models, the DWI 3 mm model shows the best performance, with AUCs of 0.836 and 0.777 in the training set and validation set respectively. Gender, age, lesion location, and vascular involvement are independent predictors of the pathological grade of IMCC. The AUCs of the clinical imaging model in the training set and validation set are 0.658 and 0.614 respectively. The intratumoral + 3 mm peritumoral 3 mm model has the best predictive efficacy, with AUCs of 0.892 and 0.814 in the training set and validation set respectively, which is superior to the dual-sequence fusion model and the multimodal combined model.Conclusions The intratumoral + 3 mm peritumoral radiomics model based on DWI sequence shows the best predictive ability. It can noninvasively predict the pathological grade of IMCC before surgery and provide theoretical guidance for clinical treatment decisions.
[Keywords] intrahepatic mass-forming cholangiocarcinoma；radiomics；peritumoral；pathological grading；magnetic resonance imaging

LI Xiaomeng1, 2   XING Lihong1, 2   ZHUO Liyong1, 2   LIU Xueyan3   DAI Shuo3   DONG Jinghui4   ZHANG Yanyan5   LI Hongjun5   YIN Xiaoping1, 2*  

1 Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China

2 Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding 071000, China

3 School of Mathematical Sciences, Liaocheng University, Liaochen 252000, China

4 Radiology Department, the Fifth Medical Center of Chinese PLA General Hospital, Beijing 100039, China

5 Department of Radiology, Beijing You'an Hospital, Capital Medical University, Beijing 100069, China

Corresponding author: YIN X P, E-mail: yinxiaoping78@sina.com

Conflicts of interest   None.

Received  2024-09-27
Accepted  2025-02-10
DOI: 10.12015/issn.1674-8034.2025.02.008
Cite this article as: LI X M, XING L H, ZHUO L Y, et al. Development and validation of a model for predicting pathological grade of intrahepatic mass-forming cholangiocarcinoma based on intratumoral and peritumoral features on MRI[J]. Chin J Magn Reson Imaging, 2025, 16(2): 51-58. DOI:10.12015/issn.1674-8034.2025.02.008.

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