"Breast cancer pre-clinical screening using infrared thermography and artificial intelligence: a prospective, multicentre, diagnostic accuracy cohort study"

作者全名："Wang, Xuefei; Chou, Kuanyu; Zhang, Guochao; Zuo, Zhichao; Zhang, Ting; Zhou, Yidong; Mao, Feng; Lin, Yan; Shen, Songjie; Zhang, Xiaohui; Wang, Xuejing; Zhong, Ying; Qin, Xue; Guo, Hailin; Wang, Xiaojie; Xiao, Yao; Yi, Qianchuan; Yan, Cunli; Liu, Jian; Li, Dongdong; Liu, Wei; Liu, Mengwen; Ma, Xiaoying; Tao, Jiangtao; Sun, Qiang; Zhai, Jidong; Huang, Likun"

作者地址："[Wang, Xuefei; Zhou, Yidong; Mao, Feng; Lin, Yan; Shen, Songjie; Zhang, Xiaohui; Wang, Xuejing; Zhong, Ying; Guo, Hailin; Wang, Xiaojie; Sun, Qiang] Peking Union Med Coll Hosp, Dept Breast Surg, Beijing, Peoples R China; [Liu, Mengwen] Peking Union Med Coll, Dept Radiol, Beijing, Peoples R China; [Zhang, Guochao] Chinese Acad Med Sci & Peking Union Med Coll, Canc Hosp, Natl Canc Ctr, Natl Clin Res Ctr Canc, Beijing, Peoples R China; [Chou, Kuanyu; Zhai, Jidong] Tsinghua Univ, Beijing, Peoples R China; [Zuo, Zhichao] Xiangtan Cent Hosp, Dept Radiol, Xiangtan, Peoples R China; [Xiao, Yao] Longhui Peoples Hosp, Anesthesia Operat Ctr, Changsha, Hunan, Peoples R China; [Zhang, Ting; Huang, Likun] Shanxi Prov Peoples Hosp, Community Hlth Serv Guidance Ctr, Taiyuan, Shanxi, Peoples R China; [Yan, Cunli] Baoji Maternal & Child Hlth Hosp, Dept Breast Surg, Baoji, Shaanxi, Peoples R China; [Qin, Xue] Langfang Peoples Hosp, Dept Oncol, Langfang, Hebei, Peoples R China; [Yi, Qianchuan] Chongqing Med Univ, Dept Gen Surg, Univ Town Hosp, Chongqing, Peoples R China; [Liu, Jian] ZhaLanTun Hosp Tradit Chinese Med, Dept Gen Surg, Inner Mongolia, Peoples R China; [Li, Dongdong; Liu, Wei] Karamay Ctr Hosp, Dept Radiol & Otolaryngol, Karamay, Xinjiang, Peoples R China; [Ma, Xiaoying] Qinghai Prov Peoples Hosp, Dept Breast Surg, Xining, Qinghai, Peoples R China; [Tao, Jiangtao] Shenzhen Peoples Hosp, Dept Gen Surg, Shenzhen, Guangdong, Peoples R China; [Sun, Qiang] Chinese Acad Med Sci & Peking Union Med Coll, Breast Surg Dept, 3 Dongdan, Beijing, Peoples R China; [Zhai, Jidong] Tsinghua Univ, Beijing, Peoples R China; [Huang, Likun] Shanxi Prov Peoples Hosp, Community Hlth Serv Guidance Ctr, Taiyuan, Shanxi, Peoples R China"

通信作者："Sun, Q (通讯作者)，Chinese Acad Med Sci & Peking Union Med Coll, Breast Surg Dept, 3 Dongdan, Beijing, Peoples R China.; Zhai, JD (通讯作者)，Tsinghua Univ, Beijing, Peoples R China.; Huang, LK (通讯作者)，Shanxi Prov Peoples Hosp, Community Hlth Serv Guidance Ctr, Taiyuan, Shanxi, Peoples R China."

来源：INTERNATIONAL JOURNAL OF SURGERY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001089168200016

JCR分区：Q1

影响因子：12.5

年份：2023

卷号：109

期号：10

开始页：3021

结束页：3031

文献类型：Article

关键词：accuracy; artificial intelligence; breast cancer; infrared thermography; screening

摘要："Background: Given the limited access to breast cancer (BC) screening, the authors developed and validated a mobile phone-artificial intelligence-based infrared thermography (AI-IRT) system for BC screening.Materials and methods: This large prospective clinical trial assessed the diagnostic performance of the AI-IRT system. The authors constructed two datasets and two models, performed internal and external validation, and compared the diagnostic accuracy of the AI models and clinicians. Dataset A included 2100 patients recruited from 19 medical centres in nine regions of China. Dataset B was used for independent external validation and included 102 patients recruited from Langfang People's Hospital.Results: The area under the receiver operating characteristic curve of the binary model for identifying low-risk and intermediate/high-risk patients was 0.9487 (95% CI: 0.9231-0.9744) internally and 0.9120 (95% CI: 0.8460-0.9790) externally. The accuracy of the binary model was higher than that of human readers (0.8627 vs. 0.8088, respectively). In addition, the binary model was better than the multinomial model and used different diagnostic thresholds based on BC risk to achieve specific goals.Conclusions: The accuracy of AI-IRT was high across populations with different demographic characteristics and less reliant on manual interpretations, demonstrating that this model can improve pre-clinical screening and increase screening rates."

基金机构："CAMS Innovation Fund for Medical Sciences (CIFMS) [2021-I2M-CT-B-018]; National High-Level Hospital Clinical Research Funding [2022-PUMCH-A-018, 2022-PUMCH-C-043]; Fundamental Research Funds for the Central Universities [3332021012]; Tsinghua University-Peking Union Medical College Hospital Initiative Scientific Research Program [2019Z]; Beijing Hope Run Special Fund of the Cancer Foundation of China [LC2021B12]"

基金资助正文："This work was supported by the CAMS Innovation Fund for Medical Sciences (CIFMS) (No. 2021-I2M-C & T-B-018),National High-Level Hospital Clinical Research Funding (2022-PUMCH-A-018, 2022-PUMCH-C-043), Fundamental Research Funds for the Central Universities (No. 3332021012), Tsinghua University-Peking Union Medical College Hospital Initiative Scientific Research Program (No. 2019Z), and Beijing Hope Run Special Fund of the Cancer Foundation of China (LC2021B12)."