Prediction of Inflammatory Breast Cancer Survival Outcomes Using Computed Tomography-Based Texture Analysis

Abstract

Background: Although inflammatory breast cancer (IBC) has poor overall survival (OS), there is little information about using imaging features for predicting the prognosis. Computed tomography (CT)-based texture analysis, a non-invasive technique to quantify tumor heterogeneity, could be a potentially useful imaging biomarker. The aim of the article was to investigate the usefulness of chest CT-based texture analysis to predict OS in IBC patients. Methods: Of the 3,130 patients with primary breast cancers between 2006 and 2016, 104 patients (3.3%) with IBC were identified. Among them, 98 patients who underwent pre-treatment contrast-enhanced chest CT scans, got treatment in our institution, and had a follow-up period of more than 2 years were finally included for CT-based texture analysis. Texture analysis was performed on CT images of 98 patients, using commercially available software by two breast radiologists. Histogram-based textural features, such as quantification of variation in CT attenuation (mean, standard deviation, mean of positive pixels [MPP], entropy, skewness, and kurtosis), were recorded. To dichotomize textural features for survival analysis, receiver operating characteristic curve analysis was used to determine cutoff points. Clinicopathologic variables, such as age, node stage, metastasis stage at the time of diagnosis, hormonal receptor positivity, human epidermal growth factor receptor 2 positivity, and molecular subtype, were assessed. A Cox proportional hazards model was used to determine the association of textural features and clinicopathologic variables with OS. Results: During a mean follow-up period of 47.9 months, 41 of 98 patients (41.8%) died, with a median OS of 20.0 months. The textural features of lower mean attenuation, standard deviation, MPP, and entropy on CT images were significantly associated with worse OS, as was the M1 stage among clinicopathologic variables (all P-values < 0.05). In multivariate analysis, lower mean attenuation (hazard ratio [HR], 3.26; P = 0.003), lower MPP (HR, 3.03; P = 0.002), and lower entropy (HR, 2.70; P = 0.009) on chest CT images were significant factors independent from the M1 stage for predicting worse OS. Conclusions: Lower mean attenuation, MPP, and entropy on chest CT images predicted worse OS in patients with IBC, suggesting that CT-based texture analysis provides additional predictors for OS.

Keywords: breast neopalsms; computed tomgraphy; histogram; overall survival; texture.

Figure 1

A flow diagram of study population.

Figure 2

Findings in a 74-year-old woman who died 23 months after the diagnosis of luminal-like breast cancer in the left breast. At the time of diagnosis, she did not have a distant metastasis. (A) An axial post-contrast CT image shows an irregular enhancing mass (arrow) with a central area of low attenuation, suggesting necrosis. (B) A texture analysis image shows a region of interest (blue line), texture maps, and texture parameters on the CT image. (C) A CT texture histogram is obtained; from which, the different statistical-based metrics are extracted. Mean, MPP, entropy, and SD were lower for the patient with poor a prognosis. MPP, mean of positive pixels; SD, standard deviation.

Figure 3

Findings in a 49-year-old woman who was alive 65 months after the diagnosis of luminal-like breast cancer in the right breast. At the time of the diagnosis, she did not have a distant metastasis. (A) An axial post-contrast CT image shows an irregular mass (arrow) with strong enhancement. (B) A texture analysis image shows a region of interest (blue line), texture maps, and texture parameters on the CT image. (C) A CT texture histogram is obtained; from which, the different statistical-based metrics are extracted. Mean, MPP, entropy, and SD were higher for the patient with a good prognosis. MPP, mean of positive pixels; SD, standard deviation.

Figure 4

Kaplan–Meier curves. There were significant differences in overall survival according to attenuation of 66.79 HU in mean on post-contrast the CT image (P < 0.001) (A), attenuation of 67.54 HU in MPP on the CT image (P < 0.001) (B), SD of 38.84 on the CT image (P = 0.015) (C), and 5.01 in entropy on the CT image (P = 0.004) (D). HU, Hounsfield units; MPP, mean of positive pixels; SD, standard deviation.