AUTHOR=Wang Yubizhuo , Shao Jiayuan , Wang Pan , Chen Lintao , Ying Mingliang , Chai Siyuan , Ruan Shijian , Tian Wuwei , Cheng Yongna , Zhang Hongbin , Zhang Xiuming , Wang Xiangming , Ding Yong , Liang Wenjie , Wu Liming TITLE=Deep Learning Radiomics to Predict Regional Lymph Node Staging for Hilar Cholangiocarcinoma JOURNAL=Frontiers in Oncology VOLUME=11 YEAR=2021 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.721460 DOI=10.3389/fonc.2021.721460 ISSN=2234-943X ABSTRACT=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 metastasis 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.