Abdominal radiology (New York) · 2025
Predicting perineural invasion of intrahepatic cholangiocarcinoma based on CT: a multicenter study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Lin Yingyu, Liu Ziwei, Li Jianpeng, Feng Shi-Ting, Dong Zhi, Tang Mimi, Song Chenyu, Peng Zhenpeng, Cai Huasong, Hu Qiugen, Zou Yujian, Zhou Xiaoqi
Аннотация
OBJECTIVE: This study explored the feasibility of preoperatively predicting perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) through machine learning based on clinical and CT image features, which may help in individualized clinical decision making and modification of further treatment strategies.
MATERIALS AND METHODS: This study enrolled 199 patients with histologically confirmed ICC from three institutions for final analysis. 111 patients from Institution I were recruited as the training cohort and internal validation cohort. Significant clinical and CT image features for predicting PNI were screened using the least absolute shrinkage and selection operator (LASSO) to construct machine learning models. 72 patients from Institutions II and III were recruited as two external validation cohorts, and 16 patients from Institution I were enrolled as a prospective cohort to assess model performance.
RESULTS: Tumor location (perihilar), intrahepatic bile duct dilatation, and arterial enhancement pattern were selected using LASSO for model construction. Machine learning models were developed based on these three features using five algorithms: multilayer perceptron, random forest, support vector machine, logistic regression, and XGBoost. The AUCs of the models exceeded 0.86, 0.84, 0.79, and 0.72 in the training cohort, internal validation cohort, external validation cohorts, and prospective cohort, respectively.
CONCLUSIONS: Machine learning models based on CT were accurate in predicting PNI of ICC, which may help in treatment decision making.
MATERIALS AND METHODS: This study enrolled 199 patients with histologically confirmed ICC from three institutions for final analysis. 111 patients from Institution I were recruited as the training cohort and internal validation cohort. Significant clinical and CT image features for predicting PNI were screened using the least absolute shrinkage and selection operator (LASSO) to construct machine learning models. 72 patients from Institutions II and III were recruited as two external validation cohorts, and 16 patients from Institution I were enrolled as a prospective cohort to assess model performance.
RESULTS: Tumor location (perihilar), intrahepatic bile duct dilatation, and arterial enhancement pattern were selected using LASSO for model construction. Machine learning models were developed based on these three features using five algorithms: multilayer perceptron, random forest, support vector machine, logistic regression, and XGBoost. The AUCs of the models exceeded 0.86, 0.84, 0.79, and 0.72 in the training cohort, internal validation cohort, external validation cohorts, and prospective cohort, respectively.
CONCLUSIONS: Machine learning models based on CT were accurate in predicting PNI of ICC, which may help in treatment decision making.