Deep Learning Radiomics to Predict Regional Lymph Node Staging for Hilar Cholangiocarcinoma

Background Our aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients. Methods and Materials Of the 179 enrolled HC patients, 90 were pathologically diagnosed with lymph node metastasis. Quantitative radiomic features and deep learning features were extracted. An LN metastasis status classifier was developed through integrating support vector machine, high-performance deep learning radiomics signature, and three clinical characteristics. An LN metastass stratification classifier (N1 vs. N2) was also proposed with subgroup analysis. Results The average areas under the receiver operating characteristic curve (AUCs) of the LN metastasis status classifier reached 0.866 in the training cohort and 0.870 in the external test cohorts. Meanwhile, the LN metastasis stratification classifier performed well in predicting the risk of LN metastasis, with an average AUC of 0.946. Conclusions Two classifiers derived from computed tomography images performed well in predicting LN staging in HC and will be reliable evaluation tools to improve decision-making.

Automated summary

Two classifiers based on computed tomography images were developed successfully, showing high performance in predicting lymph node staging for hilar cholangiocarcinoma patients, which will serve as reliable evaluation tools to enhance decision-making in clinical practice.