Hormonal Receptor Immunochemistry Heterogeneity and 18F-FDG Metabolic Heterogeneity: Preliminary Results of Their Relationship and Prognostic Value in Luminal Non-Metastatic Breast Cancers

Abstract

Introduction We aimed to investigate whether 18 F-FDG PET metabolic heterogeneity reflects the heterogeneity of estrogen receptor (ER) and progesterone receptor (PR) expressions within luminal non-metastatic breast tumors and if it could help in identifying patients with worst event-free survival (EFS). Materials and methods On 38 PET high-resolution breast bed positions, a single physician drew volumes of interest encompassing the breast tumors to extract SUV max , histogram parameters and textural features. High-resolution immunochemistry (IHC) scans were analyzed to extract Haralick parameters and descriptors of the distribution shape. Correlation between IHC and PET parameters were explored using Spearman tests. Variables of interest to predict the EFS status at 8 years (EFS-8y) were sought by means of a random forest classification. EFS-8y analyses were then performed using univariable Kaplan-Meier analyses and Cox regression analysis. When appropriate, Mann-Whitney tests and Spearman correlations were used to explore the relationship between clinical data and tumoral PET heterogeneity variables. Results For ER expression, correlations were mainly observed with 18 F-FDG histogram parameters, whereas for PR expression correlations were mainly observed with gray-level co-occurrence matrix (GLCM) parameters. The strongest correlations were observed between skewness_ ER and uniformity_ HISTO (ρ = −0.386, p = 0.017) and correlation_ PR and entropy_ GLCM (ρ = 0.540, p = 0.001), respectively. The median follow-up was 6.5 years and the 8y-EFS was 71.0%. Random forest classification found age, clinical stage, SUV max , skewness_ ER , kurtosis_ ER , entropy_ HISTO , and uniformity_ HISTO to be variables of importance to predict the 8y-EFS. Univariable Kaplan-Meier survival analyses showed that skewness_ ER was a predictor of 8y-EFS (66.7 ± 27.2 versus 19.1 ± 15.2, p = 0.018 with a cut-off value set to 0.163) whereas other IHC and PET parameters were not. On multivariable analysis including age, clinical stage and skewness_ ER , none of the parameters were independent predictors. Indeed, skewness_ ER was significantly higher in youngest patients (ρ = −0.351, p = 0.031) and in clinical stage III tumors (p = 0.023). Conclusion A heterogeneous distribution of ER within the tumor in IHC appeared as an EFS-8y prognosticator in luminal non-metastatic breast cancers. Interestingly, it appeared to be correlated with PET histogram parameters which could therefore become potential non-invasive prognosticator tools, provided these results are confirmed by further larger and prospective studies.

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