Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study
Year: 2020
Type: article
Study design: Observational Study
Abstract: Purpose: To develop and to validate a risk-predicted nomogram for downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4a breast lesions. Patients and Methods:We enrolled 680 patients with breast lesions that were diagnosed as BI-RADS category 4a by conventional ultrasound from December 2018 to June 2019.All 4a lesions were randomly divided into development and validation groups at the ratio of 3:1.In the development group consisting of 499 cases, the multiple clinical and ultrasound predicted factors were extracted, and dual-predicted nomograms were constructed by multivariable logistic regression analysis, named clinical nomogram and ultrasound nomogram, respectively.Patients were twice classified as either "high risk" or "low risk" in the two nomograms.The performance of these dual nomograms was assessed by an independent validation group of 181 cases.Receiver Operating Characteristic (ROC) curve and diagnostic value were calculated to evaluate the applicability of the new model.Results: After multiple logistic regression analysis, the clinical nomogram included 2 predictors: age and the first-degree family members with breast cancer.The area under the curve (AUC) value for the clinical nomogram was 0.661 and 0.712 for the development and validation groups, respectively.The ultrasound Ivyspring International
Source: Journal of Cancer
DOI: 10.7150/jca.51302
Authors: Zihan Niu, Jia-Wei Tian, Hai-Tao Ran, Wei-Dong Ren, Cai Chang, Jian-Jun Yuan, Chun-Song Kang, You-Bin Deng, Hui Wang, Bao-Ming Luo, Sheng-Lan Guo, Qi Zhou, En-Sheng Xue, Wei-Wei Zhan, Qing Zhou, Jie Li, Ping Zhou, Chun-Quan Zhang, Man Chen, Ying Gu, Jin-Feng Xu, Wu Chen, Yu-Hong Zhang, Hong-Qiao Wang, Jian-Chu Li, Hong-Yan Wang, Yu-Xin Jiang
Institutions: Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Harbin Medical University, Second Affiliated Hospital of Harbin Medical University, Second Affiliated Hospital of Chongqing Medical University, Sheng Jing Hospital, Shanghai Medical College of Fudan University, Fudan University, Fudan University Shanghai Cancer Center, Henan Provincial People's Hospital, Shanxi Medical University, Shanxi Dayi Hospital, Shanxi Academy of Medical Sciences, Tongji Hospital, Huazhong University of Science and Technology, Jilin University, Affiliated Hospital of Qingdao University, Sun Yat-sen University, Sun Yat-sen Memorial Hospital, First Affiliated Hospital of GuangXi Medical University, Second Affiliated Hospital of Xi'an Jiaotong University, Renmin Hospital of Wuhan University, Xi'an Jiaotong University, Fujian Medical University, Union Hospital, Shanghai Jiao Tong University, Ruijin Hospital, Shandong University, Qilu Hospital of Shandong University, Third Xiangya Hospital, Second Affiliated Hospital of Nanchang University, Tongren Hospital, Affiliated Hospital of Guizhou Medical University, Jinan University, ShenZhen People’s Hospital, First Hospital of Shanxi Medical University, Dalian Medical University, Second Affiliated Hospital of Dalian Medical University
Language: English
Subfield: Pathology and Forensic Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,337
Funder: Peking Union Medical College
OpenAlex ID: W3096361237
