Differentiating Breast Tumors from Background Parenchymal Enhancement at Contrast-Enhanced Mammography: The Role of Radiomics-A Pilot Reader Study.

Ioana Boca (Bene),Cristiana Augusta Ciortea,Lorena Alexandra Lisencu,Sorin Marian Dudea,Anca Ileana Ciurea,Paul Andrei Ștefan

doi:10.3390/diagnostics11071248

Ioana Boca (Bene), Cristiana Augusta Ciortea + Show 4 more

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https://doi.org/10.3390/diagnostics11071248

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Abstract

Background: The purpose of this study was to assess the effectiveness of the radiomic analysis of contrast-enhanced spectral mammography (CESM) in discriminating between breast cancers and background parenchymal enhancement (BPE). Methods: This retrospective study included 38 patients that underwent CESM examinations for clinical purposes between January 2019–December 2020. A total of 57 malignant breast lesions and 23 CESM examinations with 31 regions of BPE were assessed through radiomic analysis using MaZda software. The parameters that demonstrated to be independent predictors for breast malignancy were exported into the B11 program and a k-nearest neighbor classifier (k-NN) was trained on the initial groups of patients and was tested using a validation group. Histopathology results obtained after surgery were considered the gold standard. Results: Radiomic analysis found WavEnLL_s_2 parameter as an independent predictor for breast malignancies with a sensitivity of 68.42% and a specificity of 83.87%. The prediction model that included CH1D6SumAverg, CN4D6Correlat, Kurtosis, Perc01, Perc10, Skewness, and WavEnLL_s_2 parameters had a sensitivity of 73.68% and a specificity of 80.65%. Higher values were obtained of WavEnLL_s_2 and the prediction model for tumors than for BPEs. The comparison between the ROC curves provided by the WaveEnLL_s_2 and the entire prediction model did not show statistically significant results (p = 0.0943). The k-NN classifier based on the parameter WavEnLL_s_2 had a sensitivity and specificity on training and validating groups of 71.93% and 45.16% vs. 60% and 44.44%, respectively. Conclusion: Radiomic analysis has the potential to differentiate CESM between malignant lesions and BPE. Further quantitative insight into parenchymal enhancement patterns should be performed to facilitate the role of BPE in personalized clinical decision-making and risk assessment.

Highlights

Contrast-enhanced spectral mammography (CESM) represents a growing imaging technique in the detection of breast cancer, with levels of sensitivity and specificity similar to those of contrast-enhanced magnetic resonance imaging (MRI) and even better tolerated by the patients [1,2,3,4]
contrast-enhanced spectral mammography (CESM) has been proven to be excellent as a problem-solving method in local staging of breast cancer and in the evaluation of the response to neoadjuvant chemotherapy (NAC) by predicting the pathologic complete response, it can be used as a replacement technique in patients with contraindications for performing breast MRI [5,6]
CESM examinations were not performed in patients with the following conditions: an impaired renal function defined as estimated glomerular filtration rate < 60 mL/min/ 1.73 m2, history of iodinated contrast allergy, pregnancy or breastfeeding, poor asthma control, and medical conditions that may make the patient more likely to develop a severe contrast reaction

Summary

Introduction

Contrast-enhanced spectral mammography (CESM) represents a growing imaging technique in the detection of breast cancer, with levels of sensitivity and specificity similar to those of contrast-enhanced magnetic resonance imaging (MRI) and even better tolerated by the patients [1,2,3,4]. The purpose of this study was to assess the effectiveness of the radiomic analysis of contrast-enhanced spectral mammography (CESM) in discriminating between breast cancers and background parenchymal enhancement (BPE). A total of 57 malignant breast lesions and 23 CESM examinations with 31 regions of BPE were assessed through radiomic analysis using MaZda software. Results: Radiomic analysis found WavEnLL_s_2 parameter as an independent predictor for breast malignancies with a sensitivity of 68.42% and a specificity of 83.87%. Further quantitative insight into parenchymal enhancement patterns should be performed to facilitate the role of BPE in personalized clinical decision-making and risk assessment

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