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The British Journal of Radiology|January 24, 2024
Computer-extracted global radiomic features can predict the radiologists' first impression about the abnormality of a screening mammogramSomphone Siviengphanom, Sarah J Lewis, Patrick C Brennan, et al.Academic Radiology|November 20, 2021
Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future NeedsSomphone Siviengphanom, Ziba Gandomkar, Sarah J Lewis, et al.Journal of Digital Imaging|May 30, 2023
Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal CasesSomphone Siviengphanom, Ziba Gandomkar, Sarah J Lewis, et al.Journal of Imaging Informatics in Medicine|October 15, 2024
A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most DifficultSomphone Siviengphanom, Patrick C Brennan, Sarah J Lewis, et al.Clinical Breast Cancer|February 15, 2023
Do Reader Characteristics Affect Diagnostic Efficacy in Screening Mammography? A Systematic ReviewDennis Jay Wong, Ziba Gandomkar, Sarah Lewis, et al.Scientific Reports|October 12, 2021
Global processing provides malignancy evidence complementary to the information captured by humans or machines following detailed mammogram inspectionZiba Gandomkar, Somphone Siviengphanom, Ernest U Ekpo, et al.Plos One|April 25, 2023
Reliability of radiologists' first impression when interpreting a screening mammogramZiba Gandomkar, Somphone Siviengphanom, Mo'ayyad Suleiman, et al.Pageof 1