Hormonal Receptor Immunochemistry Heterogeneity and 18F-FDG Metabolic Heterogeneity: Preliminary Results of Their Relationship and Prognostic Value in Luminal Non-Metastatic Breast Cancers - Aide, Nicolas; Elie, Nicolas; Blanc-Fournier, Cécile; Levy, Christelle; Salomon, Thibault; Lasnon, Charline (Frontiers in Oncology, 2021)
Introduction: We aimed to investigate whether 18F-FDG metabolic heterogeneity reflects the heterogeneity of estrogen (ER) and progesterone receptors (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 SUVmax, 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 18F-FDG histogram parameters, whereas for PR expression correlations were mainly observed with grey 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, SUVmax, 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. 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.