CEM radiomics for distinguishing lesion from background parenchymal enhancement in patients with invasive breast cancer

Bino A. Varghese; Melissa Perkins; Steven Cen; Xiaomeng Lei; Jacquelyn Fields; Janet Jamie; Bhushan Desai; Mariam Thomas; Darryl H. Hwang; Sandy Lee; Linda H. Larsen; Mary Yamashita

doi:10.1117/12.2670371

6 March 2023 CEM radiomics for distinguishing lesion from background parenchymal enhancement in patients with invasive breast cancer

Bino A. Varghese, Melissa Perkins, Steven Cen, Xiaomeng Lei, Jacquelyn Fields, Janet Jamie, Bhushan Desai, Mariam Thomas, Darryl H. Hwang, Sandy Lee, Linda H. Larsen, Mary Yamashita

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Proceedings Volume 12567, 18th International Symposium on Medical Information Processing and Analysis; 125670H (2023) https://doi.org/10.1117/12.2670371
Event: 18th International Symposium on Medical Information Processing and Analysis, 2022, Valparaíso, Chile

Abstract

In this IRB approved retrospective study 41 women with biopsy-proven invasive breast cancers (IBC) were imaged using contrast-enhanced mammography (CEM), prior to any treatment. Size-matched regions of interest (ROIs) were manually contoured by an experienced breast radiologist on the CEM capturing the breast lesion and breast parenchymal enhancement (BPE), respectively. Radiomics analysis was performed using LifEx software and 109 radiomics metrics spanning 6 different texture families were extracted from each ROI. Predictive models of lesion malignancy were developed using multiple classifiers and used to subclassify breast cancers based on their hormone receptor status. The 10- fold cross validation was used to construct the decision classifier and performance was assessed. CEM radiomics models based on Random Forest, Real Adaboost, and ElasticNet classifiers achieved an AUC of 0.83, 0.82 and 0.74, respectively in discriminating malignant breast lesions from varying amounts of BPE. Accounting for the varying levels of BPE, revealed a reduction in AUC-based prediction of lesion vs. BPE as the qualitative assessment of BPE increased from minimal to moderate (AUCs of 0.89 vs 0.74). Further analyses of the IBC based on their hormone receptor status showed that triple negative breast lesions showed statistically significant differences in multiple radiomics metrics compared to ER+ PR+ HER2- and HER2+. The predicted probability of the radiomics model was significantly different across three receptor-based subtypes and between high and low nuclear grade breast cancers. CEM Radiomics demonstrated good discrimination (AUC>0.8) of malignant breast lesions despite varying BPE levels and supports breast lesion subtyping.

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Bino A. Varghese, Melissa Perkins, Steven Cen, Xiaomeng Lei, Jacquelyn Fields, Janet Jamie, Bhushan Desai, Mariam Thomas, Darryl H. Hwang, Sandy Lee, Linda H. Larsen, and Mary Yamashita "CEM radiomics for distinguishing lesion from background parenchymal enhancement in patients with invasive breast cancer", Proc. SPIE 12567, 18th International Symposium on Medical Information Processing and Analysis, 125670H (6 March 2023); https://doi.org/10.1117/12.2670371

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KEYWORDS

Radiomics

Breast

Breast cancer

Semantics

Mammography

Performance modeling

Statistical analysis

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