European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology · 2026
Preoperative prediction of perineural invasion in intrahepatic cholangiocarcinoma with interpretable machine learning based on MRI.
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Zhou Xiaoqi, Chen Meicheng, Xu Danyang, Hu Jing, Liu Ziwei, Song Chenyu, Tang Mimi, Wang Jifei, Chen Yuying, Luo Yanji, Peng Zhenpeng, Feng Shi-Ting
Аннотация
INTRODUCTION: To develop radiomics and deep learning (DL) based interpretable models using MRI for preoperative prediction of perineural invasion (PNI) in intrahepatic cholangiocarcinoma (ICC).
MATERIALS AND METHODS: A total of 165 pathologically confirmed ICC patients with preoperative MRI were retrospectively enrolled from two centers (center1, training set, n = 115; validation set, n = 14; internal test set, n = 15; center 2, external test set, n = 21). Radiomics and DL models were constructed for single-phase (pre-contrast, arterial phase, portal venous phase, hepatobiliary phase [HBP]) and multi-phase MRI using the Shukun AI platform and PNI-MambaNet. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC). Gradient-weighted class activation mapping (Grad-CAM) heatmaps visualized the regions prioritized by the DL models.
RESULTS: The PNI positive rate was 42.4 % (61/144) and 28.6 % (6/21) in the two centers. Radiomics HBP models achieved the highest AUC in the internal test set, while multi-phase model performed best in the external test set (AUC: HBP, 0.778 and 0.733 for the internal and external test sets, respectively; multi-phase, 0.759 and 0.778). For DL models, multi-phase model achieved the highest AUC in the internal test set, while HBP model performed best in the external test set (AUC: HBP, 0.926 and 0.856; multi-phase, 0.944 and 0.844). DL models outperformed radiomics models in the external test set, with Grad-CAM visualizing tumor margin regions as the interest area.
CONCLUSIONS: DL models based on MRI effectively predict PNI in ICC, with visualizations enhancing clinical interpretability and potential application.
MATERIALS AND METHODS: A total of 165 pathologically confirmed ICC patients with preoperative MRI were retrospectively enrolled from two centers (center1, training set, n = 115; validation set, n = 14; internal test set, n = 15; center 2, external test set, n = 21). Radiomics and DL models were constructed for single-phase (pre-contrast, arterial phase, portal venous phase, hepatobiliary phase [HBP]) and multi-phase MRI using the Shukun AI platform and PNI-MambaNet. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC). Gradient-weighted class activation mapping (Grad-CAM) heatmaps visualized the regions prioritized by the DL models.
RESULTS: The PNI positive rate was 42.4 % (61/144) and 28.6 % (6/21) in the two centers. Radiomics HBP models achieved the highest AUC in the internal test set, while multi-phase model performed best in the external test set (AUC: HBP, 0.778 and 0.733 for the internal and external test sets, respectively; multi-phase, 0.759 and 0.778). For DL models, multi-phase model achieved the highest AUC in the internal test set, while HBP model performed best in the external test set (AUC: HBP, 0.926 and 0.856; multi-phase, 0.944 and 0.844). DL models outperformed radiomics models in the external test set, with Grad-CAM visualizing tumor margin regions as the interest area.
CONCLUSIONS: DL models based on MRI effectively predict PNI in ICC, with visualizations enhancing clinical interpretability and potential application.