AimTo develop a predictive model for the diagnosis of iris melanoma.
Methods: Retrospective consecutive case series that included 100 cases of pathologically confirmed iris melanoma and 112 cases of Iris naevus, either pathological confirmation or documented stability of >1 year. Patient demographic data, features of clinical presentation, tumour characteristics and follow-up were collected. Iris melanoma with ciliary body extension was excluded. Lasso logistic regression with 10-fold cross-validation was used to select the tuning parameter. Discrimination was assessed with the area under the curve (AUC) and calibration by a plot.
Results: There was a significant asymmetry in the location of both nevi and melanoma with preference for inferior iris quadrants (83, 74%) and (79, 79%), respectively (p=0.50). Tumour seeding, glaucoma and hyphaema were present only in melanoma. The features that favoured the diagnosis of melanoma were size (increased height (OR 3.35); increased the largest basal diameter (OR 1.64)), pupillary distortion (ectropion uvea or corectopia (OR 2.55)), peripheral extension (angle or iris root involvement (OR 2.83)), secondary effects (pigment dispersion (OR 1.12)) and vascularity (OR 6.79). The optimism-corrected AUC was 0.865. The calibration plot indicated good calibration with most of the points falling near the identity line and the confidence band containing the identity line through most of the range of probabilities.
Conclusions: The predictive model provides direct diagnostic prediction of the lesion being iris melanoma expressed as probability (%). Use of a prediction calculator (app) can enhance decision-making and patient counselling. Further refinements can be undertaken with additional datasets, forming the basis for automated diagnosis.
Keywords: Iris; Neoplasia; Pathology.
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