Global mammographic radiomic signature can predict radiologists’ difficult-to-interpret normal cases

Somphone Siviengphanom; Ziba Gandomkar; Sarah J. Lewis; Patrick C. Brennan

doi:10.1117/12.2645377

3 April 2023 Global mammographic radiomic signature can predict radiologists’ difficult-to-interpret normal cases

Somphone Siviengphanom, Ziba Gandomkar, Sarah J. Lewis, Patrick C. Brennan

Author Affiliations +

Proceedings Volume 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment; 1246708 (2023) https://doi.org/10.1117/12.2645377
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

This study investigated whether a global radiomic signature (i.e., a set of global radiomic features) from mammograms can predict radiologists’ difficult-to-interpret normal cases. Retrospective non-identifiable data collected from 342 radiologists interpreting 81 normal mammograms were used to group cases as difficult-to-interpret (41 cases) and easy-to-interpret (40 cases) based on one-third of cases having the correspondingly highest and lowest difficulty scores. A set of 34 global radiomic features per image were extracted based on regions of interests delineated using lattice- and squared-based approaches, and normalised. Three machine learning classification models were constructed: 1). CC, using the 34 global radiomic features derived from craniocaudal images only, and 2). MLO, using the features from mediolateral oblique images only, both based on a random forest method for differentiating difficult-to-interpret from easy-to-interpret normal cases, and 3). CC+MLO model using the median predictive scores from both CC and MLO models. We trained and validated the models using leave-one-out-cross-validation approach. Performances of the models were measured by the area under the receiver operating characteristic curve (AUC). The CC+MLO model outperformed (0.73 AUC, 0.62 to 0.83) the CC (0.70 AUC, 0.62 to 0.78) and MLO (0.68 AUC, 0.60 to 0.76) models. The results showed that the global mammographic radiomic signature has the ability to predict radiologists’ difficult-to-interpret normal cases.

Conference Presentation

Citation Download Citation

Somphone Siviengphanom, Ziba Gandomkar, Sarah J. Lewis, and Patrick C. Brennan "Global mammographic radiomic signature can predict radiologists’ difficult-to-interpret normal cases", Proc. SPIE 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment, 1246708 (3 April 2023); https://doi.org/10.1117/12.2645377

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