Improved diagnosis of thyroid cancer aided with deep learning applied to sonographic text reports:a retrospective,multi-cohort,diagnostic study  

作　　者：Qiang Zhang Sheng Zhang Jianxin Li Yi Pan Jing Zhao Yixing Feng Yanhui Zhao Xiaoqing Wang Zhiming Zheng Xiangming Yang Lixia Liu Chunxin Qin Ke Zhao Xiaonan Liu Caixia Li Liuyang Zhang Chunrui Yang Na Zhuo Hong Zhang Jie Liu Jinglei Gao Xiaoling Di Fanbo Meng Wei Ji Meng Yang Xiaojie Xin Xi Wei Rui Jin Lun Zhang Xudong Wang Fengju Song Xiangqian Zheng Ming Gao Kexin Chen Xiangchun Li 

机构地区：[1]Department of Maxillofacial and Otorhinolaryngology Oncology,National Clinical Research Center for Cancer,Key Laboratory of Molecular Cancer Epidemiology of Tianjin,Key Laboratory of Cancer Prevention and Therapy of Tianjin,Tianjin Medical University Cancer Institute and Hospital,Tianjin Medical University,Tianjin 300060,China [2]Department of Diagnostic and Therapeutic Ultrasonography,National Clinical Research Center for Cancer,Key Laboratory of Molecular Cancer Epidemiology of Tianjin,Key Laboratory of Cancer Prevention and Therapy of Tianjin,Tianjin Medical University Cancer Institute and Hospital,Tianjin Medical University,Tianjin 300060,China [3]Department of Ultrasonography,Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University,Weihai 262400,China [4]Department of Pathology,National Clinical Research Center for Cancer,Key Laboratory of Molecular Cancer Epidemiology of Tianjin,Key Laboratory of Cancer Prevention and Therapy of Tianjin,Tianjin Medical University Cancer Institute and Hospital,Tianjin Medical University,Tianjin 300060,China [5]Department of Ultrasonography,Affiliated Hospital of Chifeng University,Chifeng 024000,China [6]Department of Ultrasonography,Integrated Traditional Chinese and Western Medicine Hospital of Jilin city Jilin Province,Jilin 132000,China [7]Department of Ultrasonography,Dezhou Municipal Hospital,Dezhou 253000,China [8]Department of Ultrasound Room of Functions Branch,Affiliated Hospital of Hebei University,Baoding 071000,China [9]Department of Thyroid and Breast Surgery,Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University,Weihai 262400,China [10]Department of General Surgery,Tianjin Medical University General Hospital,Tianjin 300000,China [11]Department of Thyroid and Breast Surgery,Tianjin 4th Centre Hospital,Tianjin 300000,China [12]Department of Thyroid Surgery,Affiliated Hospital of Chengde Medical University,Chengde 067000,China [13]Department of Pathology [14]Department of Ultrasonography,The Second Hospital of Tianjin Medica

出　　处：《Cancer Biology & Medicine》2022年第5期733-741,共9页癌症生物学与医学（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(Grant No.31801117 to Dr.X.Li and 82073287 to Dr.Zhang);the Program for Changjiang Scholars and Innovative Research Team in University in China(Grant No.IRT_14R40 to Dr.K.Chen);the Chinese National Key Research and Development Project(Grant No.2018YFC1315601).

摘　　要：Objective:Large volume radiological text data have been accumulated since the incorporation of electronic health record(EHR)systems in clinical practice.We aimed to determine whether deep natural language processing algorithms could aid radiologists in improving thyroid cancer diagnosis.Methods:Sonographic EHR data were obtained from the EHR database.Pathological reports were used as the gold standard for diagnosing thyroid cancer.We developed thyroid cancer diagnosis based on natural language processing(THCaDxNLP)to interpret unstructured sonographic text reports for thyroid cancer diagnosis.We used the area under the receiver operating characteristic curve(AUROC)as the primary metric to measure the performance of the THCaDxNLP.We compared the performance of thyroid ultrasound radiologists aided with THCaDxNLP vs.those without THCaDxNLP using 5 independent test sets.Results:We obtained a total number of 788,129 sonographic radiological reports.The number of thyroid sonographic data points was 132,277,18,400 of which were thyroid cancer patients.Among the 5 test sets,the numbers of patients per set were 439,186,82,343,and 171.THCaDxNLP achieved high performance in identifying thyroid cancer patients(the AUROC ranged from 0.857–0.932).Thyroid ultrasound radiologists aided with THCaDxNLP achieved significantly higher performances than those without THCaDxNLP in terms of accuracy(93.8%vs.87.2%;one-sided t-test,adjusted P=0.003),precision(92.5%vs.86.0%;P=0.018),and F1 metric(94.2%vs.86.4%;P=0.007).Conclusions:THCaDxNLP achieved a high AUROC for the identification of thyroid cancer,and improved the accuracy,sensitivity,and precision of thyroid ultrasound radiologists.This warrants further investigation of THCaDxNLP in prospective clinical trials.

关 键 词：Thyroid cancer sonographic text report deep learning natural language process 

分 类 号：R736.1[医药卫生—肿瘤]