Quantification of Women Who Could Benefit from Hormone Therapy after Endometrial Cancer Treatment: An Analysis of SEER Data.
Ambrogio Pietro LonderoAnjeza XholliSerena BertozziMaria OrsariaMichele PaudiceLaura MariuzziAngelo CagnacciPublished in: Current oncology (Toronto, Ont.) (2022)
Our primary aim was to estimate the magnitude of stage I endometrial cancer (EC) survivors that could benefit from hormonal therapy (HT). Our secondary aims were to assess EC incidence in women below 50 and below 60 over the years, and analyze the overall survival and any influencing factors. We analyzed the endometrioid EC data from the Surveillance, Epidemiology, and End Results (SEER) program according to women's age, tumor stage, and grade. We analyzed the proportions of EC survivors below 50 and below 60 years of age and stratified those age groups by race. For age distribution and survival analysis SEER, 18 registries' research data (2000-2018) were analyzed. We analyzed the SEER 12 registries' research data (1992-2019) for incidence time trends. Our investigation found a 14% and 40% cumulative prevalence of stage I EC that occurs in women below 50 or 60 years, respectively. EC's prevalence has progressively risen in recent decades, but cancer-specific mortality remains low. The increasing number of women affected by EC in premenopause or early postmenopause face an 18 years-survival rate of 96.86% and 95.73%, respectively. A significant proportion of low-grade EC survivors can potentially benefit from HT treatment, and this requires awareness of other aspects of their health or quality of life, in addition to cancer treatments.
Keyphrases
- endometrial cancer
- polycystic ovary syndrome
- risk factors
- low grade
- electronic health record
- pregnancy outcomes
- young adults
- big data
- public health
- healthcare
- insulin resistance
- papillary thyroid
- cervical cancer screening
- breast cancer risk
- high grade
- stem cells
- type diabetes
- mental health
- machine learning
- coronary artery disease
- cardiovascular events
- cardiovascular disease
- data analysis
- squamous cell
- free survival
- combination therapy
- bone marrow
- mesenchymal stem cells
- social media
- health information
- climate change
- skeletal muscle
- deep learning