Differences in Tumour Aggressiveness Based on Molecular Subtype and Race Measured by [ 18 F]FDG PET Metabolic Metrics in Patients with Invasive Carcinoma of the Breast.
Sofiullah AbubakarStuart MoreNaima TagAfusat OlabinjoAhmed IsahIsmaheel O LawalPublished in: Diagnostics (Basel, Switzerland) (2023)
Breast cancer in women of African descent tends to be more aggressive with poorer prognosis. This is irrespective of the molecular subtype. [ 18 F]FDG PET/CT metrics correlate with breast cancer aggressiveness based on molecular subtype. This study investigated the differences in [ 18 F]FDG PET/CT metrics of locally advanced invasive ductal carcinoma (IDC) among different racial groups and molecular subtypes. Qualitative and semiquantitative readings of [ 18 F]FDG PET/CT acquired in women with locally advanced IDC were performed. Biodata including self-identified racial grouping and histopathological data of the primary breast cancer were retrieved. Statistical analysis for differences in SUVmax, MTV and TLG of the primary tumour and the presence of regional and distant metastases was conducted based on molecular subtype and race. The primary tumour SUVmax, MTV, TLG and the prevalence of distant metastases were significantly higher in Black patients compared with other races ( p < 0.05). The primary tumour SUVmax and presence of distant metastases in the luminal subtype and the primary tumour SUVmax and TLG in the basal subtype were significantly higher in Black patients compared with other races ( p < 0.05). The significantly higher PET parameters in Black patients with IDC in general and in those with luminal and basal carcinoma subtypes suggest a more aggressive disease phenotype in this race.
Keyphrases
- end stage renal disease
- locally advanced
- ejection fraction
- newly diagnosed
- chronic kidney disease
- squamous cell carcinoma
- lymph node
- rectal cancer
- computed tomography
- prognostic factors
- systematic review
- peritoneal dialysis
- type diabetes
- radiation therapy
- pet imaging
- patient reported outcomes
- electronic health record
- machine learning
- risk factors
- young adults
- pregnant women
- patient reported
- artificial intelligence
- deep learning