Background/Aim Molecular prognostication provides clinically applicable prognostic information for patients with uveal melanoma. Most ocular oncologists recommend intensive metastatic surveillance for patients with high-risk tumours. However, socioeconomic variables may limit a patient’s ability to adhere to recommended surveillance. We aim to analyse socioeconomic data from patients with uveal melanoma who underwent molecular prognostication to determine which variables influence adherence.
Methods This was a retrospective review of 107 consecutive patients who were diagnosed and treated for uveal melanoma from January 2014 to June 2015. Patients were categorised into low/unknown risk and high risk for metastasis. The low-risk group was followed with hepatic ultrasonography every 6 months. The high-risk group was followed with more frequent hepatic imaging or incorporation of hepatic CT/MRI into the surveillance protocol. Adherence to surveillance recommendations was recorded for the first 2 years following primary treatment. Socioeconomic data including age at diagnosis, baseline systemic staging, gene expression profile status, marital status, insurance, distance of primary residence, median household income and Charlson Comorbidity Index score were recorded. Frequency/modality of imaging and metastatic status were also recorded.
Results High-risk patients were more likely to develop metastasis than low-risk/unknown-risk patients (p<0.001). High-risk patients were more likely to have scans at baseline (p=0.008) and to have expected scans relative to low-risk/unknown-risk patients (p<0.001). There was no significant relationship between the likelihood of adhering to recommended surveillance and the other variables analysed.
Conclusions Prognostic risk level is a significant predictor of surveillance and remains significant after adjustment for socioeconomic variables. Adherence to surveillance recommendations for high-risk patients may translate into improved survival.
- uveal melanoma
- metastatic surveillance
- gene expression profiling
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