Observational studyInternational ophthalmology2026
Uveal melanoma survival prediction system: a multi-center database study.
Observational study in International ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThis study aimed to develop and validate a competing risk nomogram to predict overall survival (OS) in uveal Melanoma(UM) patients.
methodsThis study divided 14,688 UM patients from the SEER database from 2000 to 2020 into training cohorts and internal test cohorts after excluding cases with incomplete information. Univariate and multivariate cox regression analyses were used to identify potential risk factors for UM and construct the clinical predictive model nomogram. The predictive capacity was evaluated using calibration curves, receiver operating characteristic curves (ROCs), and decision curve analysis (DCA).
resultsA total of 4184 eligible patients were included for analysis, with 2929 cases (70%) in the training group and 1255 cases (30%) in the internal test group. Multivariable cox regression analysis confirmed six independent risk factors (chemotherapy, radiation, total number of malignant tumors, pathological type, grade, diagnostic confirmation). The nomogram demonstrated excellent discriminative ability, with AUCs in the training and internal test groups at 1 year, 3 years, and 5 years being 0.89 and 0.88, 0.93 and 0.93, 0.91 and 0.9, respectively. The calibration plot indicated that predictions based on the nomogram were well-matched to actual clinical practice, and the DCA plot showed good clinical utility for the nomogram. Additionally, Kaplan-Meier curve analysis indicated that, in both the train set and the test set, the high score group had significantly poorer survival outcomes than the low score group (P < 0.001).
conclusionEmploying the nomogram method for survival prognosis assessment in UM patients yields high accuracy. This can further enhance the precise evaluation of UM patient survival prognosis, providing guidance for personalized treatment.
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