PET2015UZ: Prognostic value of pre-treatment 18FDG-PET in operable breast cancer
Vincent Vinh-Hung, Hendrik Everaert, Mark De Ridder
18F-Fluoro-deoxyglucose
Breast cancer
Surgery
Survival
Prognostic value
Positron emission tomography
Abstract
This retrospective-observational study hypothesizes that preoperative 18FDG-PET for breast cancer has significant prognostic value for the prediction of survival. Data from patients who had breast surgery and had a preoperative FDG-PET examination at the UZ Brussel in 2002-2008 and in 2009-2015 will be analyzed without restriction on age or sex. Data collection for the cohort 2002-2008 has been finalized and will be shared on Mendeley (Reserved DOI: 10.17632/sfvtmrd8z9.1 ). Data for the cohort 2009-2015 will be collected by end of 2020.
Detailed and referred to in:
# Breast cancer preoperative 18FDG-PET, overall survival prognostic separation compared with the lymph node ratio.
# Vinh-Hung V, Everaert H, Gorobets O, Van Parijs H, Verfaillie G, Vanhoeij M, Storme G, Fontaine C, Lamote J, Perrin J, Farid K, Nguyen NP, Verschraegen C, De Ridder M.
# Breast Cancer. 2021 Jul;28(4):956-968. doi: 10.1007/s12282-021-01234-z
# PMID: 33689151
https://link.springer.com/article/10.1007%2Fs12282-021-01234-z
# Is there a utility of [18F]FDG-PET before surgery in breast cancer? A 15-years overall survival analysis.
# Perrin, Farid K, Van Parijs H, Gorobets O, Vinh-Hung V, Nguyen NP, Djassemi N, De Ridder M, Everaert H.
# World J Clin Oncol. Pending.
Study registration:
Before start
Contact Vincent: vh@onco.be or anhxang@gmail.com for data layout.
Attachments
Steps
Review medical records
Check inclusion criteria
Participating center
Period of diagnosis: 2002-2015
Primary breast cancer
Histologically confirmed
Operable - curative surgery
Pre-treatment FDG-PET or PET/CT
Check exclusion
Previous history of cancer
The breast tumor is a primary sarcoma
Surgery was done for palliation, for symptom control
No histopathological confirmation of cancer
Non-invasive carcinoma
Metastatic disease demonstrated by imaging modes other than FDG-PET
Data collection
Clinical-pathological characteristics
Age at diagnosis
Lymphovascular invasion
Breast inflammation
Breast skin invasion
Tumor laterality
Tumor location
Clinical tumor size
Pathological tumor size
Number of examined axillary lymph nodes
Number of involved axillary lymph nodes
Neoadjuvant therapy
Sex
Type of surgery
Adjuvant chemotherapy
Adjuvant hormone therapy
Adjuvant radiation therapy
Disease presentation (screening or symptomatic)
Laboratory markers
Source material of initial pathology (cytological/biopsy/excision)
Histological tumor type
Pathological grade
Hormone receptor status
Her2/neu status
FDG-PET characteristics
Type of exam (PET only / PET-CT)
Pattern of PET positivity (visual pathologically increased uptake):
breast,
axillary-supraclavicular region,
internal mammary nodes,
distant.
Standard uptake value (SUV) based on regions of interests:
SUVmax global (whole body).
SUVmax within breast, right and left.
SUVmax axillary-supraclavicular region, right and left.
SUVmax internal mammary nodes.
Outcomes
Local (in-breast) recurrence
Regional (axillary, supraclavicular, internal mammary nodes) recurrence
New primary tumor (breast/non-breast)
Censor status at last follow-up (alive/died)
Disease status at last follow-up (NED, no evidence of disease/WD, with cancer)
Cause of death
Data analyses
Statistics
Descriptive statistics.
Clinical- pathological characteristics and patterns of PET uptake.
Relationships:
between the characteristics and the patterns of PET uptake.
Disease-free survival (DFS):
event defined as any local-regional or distant recurrence, new primary tumor, or death from any cause.
Overall survival (OS):
event defined as death from any cause.
Explorative analyses:
Multivariate Cox regression analyses of DFS and OS.
Evaluate the prognostic value of patterns of PET positivity using the Akaike information criterion (AIC) and using indexes of the proportion of variation explained by covariates.
Handling of missing data:
If missing in <10% of the cases, impute using multivariate imputation by chained equations. If missing in 10% or more, consider separate analyses.