Practical Geriatric AssessmentPractical Geriatric Assessment in Older Adults With Non-Small Cell Lung Cancer Undergoing Stereotactic Body Radiation Therapy

National guidelines recommend that older adults with cancer undergo a special health assessment before starting cancer treatment. This type of assessment evaluates physical function, nutrition, social support, psychological well-being, medical conditions (both cancer-related and non-cancer-related), and cognitive function. The results can help doctors make better treatment decisions and determine whether additional support services-such as nutrition counseling, physical therapy, or social work-would be beneficial. Even though these assessments are recommended, they are not typically used because they need to be performed by a specialist and can take over an hour to complete. Given these challenges, a 10-15-minute assessment called the Practical Geriatric Assessment (PGA) was recently developed. The PGA can be completed by any healthcare provider and helps identify older adults who may need extra support alongside their cancer treatment. While the PGA has the potential to make geriatric assessments more accessible, the investigators do not yet know whether patients will find it useful or easy to complete.

Additionally, it is unclear whether using the PGA will lead to more referrals for recommended supportive care services. This study aims to address these questions. The investigators will evaluate whether using the PGA impacts the number of patients referred to recommended supportive care services. Investigators will also evaluate how participants feel about completing the PGA, including how easy or difficult it is, and to assess the feasibility of implementing this survey on a larger scale. Finally, the investigators will use facial photographs and audio-visual data from the PGA to develop and evaluate artificial intelligence algorithm(s) to identify vulnerable patients who might benefit from additional supportive care services; namely, FaceAge, a validated deep learning model capable of estimating biological age from still facial images.