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

Frontiers in Oncology

Wuwei Tian¹  Hongbin Zhang¹  Shijian Ruan²  Yongna Cheng²  Liming Wu²  Siyuan Chai³  Yong Ding⁴  Mingliang Ying⁵  Yubizhuo Wang⁶  Pan Wang⁶  Lintao Chen⁷  Xiuming Zhang⁷  Wenjie Liang⁷  Xiangming Wang⁷  Jiayuan Shao⁸ 

[1] Electronic Engineering, Zhejiang University, Hangzhou, China;;College of Information Science &Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China;Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China;Department of Radiology, Jinhua Municipal Central Hospital, Jinhua, China;Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China;Department of Radiology, Yiwu Central Hospital, Yiwu, China;Polytechnic Institute, Zhejiang University, Hangzhou, China;

关键词: radiomics;    hilar cholangiocarcinoma;    computed tomography;    lymph node;    deep learning;   

DOI  :  10.3389/fonc.2021.721460

来源: DOAJ

【 摘 要 】

BackgroundOur aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients. Methods and MaterialsOf 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.ResultsThe 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.ConclusionsTwo classifiers derived from computed tomography images performed well in predicting LN staging in HC and will be reliable evaluation tools to improve decision-making.

【 授权许可】

Unknown