Purpose To develop a multidisciplinary algorithmic approach to management of women aged ≥70 years with clinically staged T1N0 hormone receptor–positive breast cancer, including geriatric assessments predicting life expectancy and the likelihood of functional decline in the near future, in the context of a program-wide quality improvement initiative, to better select patients for therapeutic interventions. Methods and Materials Two geriatric assessment tools, the Combined Lee-Schonberg Index and the Vulnerable Elderly Scale, were introduced into our clinical workflow to predict long-term mortality and likelihood of functional decline. Scores from these tools, along with patient preferences and clinical features, were incorporated into a preoperative algorithm addressing the use of sentinel lymph node biopsy (SLNB), and a postoperative algorithm addressing the use of adjuvant radiation therapy (RT). Results The algorithms were approved for use in August 2015. Twenty-four patients were identified by in-clinic screening and have been managed using the algorithms as a guide. Mean patient age was 80 years (range, 71-89 years). Per the preoperative algorithm, consideration of omission of SLNB was an option in 11 of 24 patients (46%), and in total 18 of 24 (75%) opted against SLNB. Per the postsurgical algorithm, consideration of omission of adjuvant RT was an option for 19 of 24 patients (79%), and in total 17 of 24 (71%) opted to forego RT. Conclusion Incorporation of simple geriatric assessments seems to have had a marked impact on decision making regarding both surgical and adjuvant therapies for women aged ≥70 years with early-stage hormone-positive breast cancer compared with historical patterns, with ≥71% omission of both SLNB and adjuvant RT in patients managed according to an institutional quality improvement initiative.
|Original language||English (US)|
|Number of pages||7|
|Journal||International Journal of Radiation Oncology Biology Physics|
|State||Published - Jul 15 2017|
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Cancer Research