Hybrid 18F-FDG-PET/MRI Measurement of Standardized Uptake Value Coupled with Yin Yang 1 Signature in Metastatic Breast Cancer. A Preliminary Study

Abstract

Purpose: Detection of breast cancer (BC) metastasis at the early stage is important for the assessment of BC progression status. Image analysis represents a valuable tool for the management of oncological patients. Our preliminary study combined imaging parameters from hybrid 18F-FDG-PET/MRI and the expression level of the transcriptional factor Yin Yang 1 (YY1) for the detection of early metastases. Methods: The study enrolled suspected n = 217 BC patients that underwent 18F-FDG-PET/MRI scans. The analysis retrospectively included n = 55 subjects. n = 40 were BC patients and n = 15 imaging-negative female individuals were healthy subjects (HS). Standard radiomics parameters were extracted from PET/MRI image. RNA was obtained from peripheral blood mononuclear cells and YY1 expression level was evaluated by real time reverse transcription polymerase chain reactions (qRT-PCR). An enzyme-linked immuosorbent assay (ELISA) was used to determine the amount of YY1 serum protein. Statistical comparison between subgroups was evaluated by Mann-Whitney U and Spearman’s tests. Results: Radiomics showed a significant positive correlation between Greg-level co-occurrence matrix (GLCM) and standardized uptake value maximum (SUVmax) (r = 0.8 and r = 0.8 respectively) in BC patients. YY1 level was significant overexpressed in estrogen receptor (ER)-positive/progesteron receptor-positive/human epidermal growth factor receptor2-negative (ER+/PR+/HER2-) subtype of BC patients with synchronous metastasis (SM) at primary diagnosis compared to metachronous metastasis (MM) and HS (p < 0.001) and correlating significantly with 18F-FDG-uptake parameter (SUVmax) (r = 0.48). Conclusions: The combination of functional 18F-FDG-PET/MRI parameters and molecular determination of YY1 could represent a novel integrated approach to predict synchronous metastatic disease with more accuracy than 18F-FDG-PET/MRI alone.

TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review

“…The majority of radiogenomic literature on breast cancer [34] relies almost exclusively on DCE-MRI (dynamic contrast-enhanced MRI) imaging features and, in some cases, on positron emission tomography/magnetic resonance (PET/MR) imaging [35,36]. Ten TCGA-TCIA-based studies were included in our review, and most of them aimed to capture associations between imaging features and breast cancer intrinsic molecular subtypes.…”

The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status

“…A closely related field is 'radiogenomics', which explores the associations between imaging phenotype (radiomic data) and disease genotype (genomic patterns) [13,14]. Two public data resources such as The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), provide cancer genomic profiling and medical images counterpart, respectively [15,16], to promote cross-disciplinary research including radiogenomic studies [17][18][19].…”

What Is the Role of Imaging in Cancers?

“…Table 1 shows a summary of the topics and the papers included for each topic [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. The majority of papers are focused on prostate cancer (PCa) and radiomics.…”

“…In the last years, a large amount of data has been published, such as in the current issue of Cancers , in order to apply this in diverse settings of disease. Castaldo et al [ 30 ] and Schiano et al [ 32 ] evaluated the role of radiomics in breast cancer. The first group of authors concluded the ability of radiomic features by MRI to discriminate major breast cancer molecular subtypes, thus potentially guiding a personalized treatment [ 30 ].…”

“…The first group of authors concluded the ability of radiomic features by MRI to discriminate major breast cancer molecular subtypes, thus potentially guiding a personalized treatment [ 30 ]. The second group by Schiano et al [ 32 ] showed that the combination of radiomic features by FDG PET/MRI and molecular data are able to predict the synchronous metastatic disease more accurately than a single information. Fujima et al [ 33 ] found a correlation between the clinical outcome and machine-learning algorithm using various MRI-derived data in patients with sinonasal squamous cell carcinomas.…”