To 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. Data are available on reasonable request.

中文翻译：

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虹膜黑色素瘤的预测模型


开发用于虹膜黑色素瘤诊断的预测模型。方法 回顾性连续病例系列，包括 100 例经病理证实的虹膜黑色素瘤和 112 例虹膜痣，无论是病理确认还是记录的 >1 年稳定性。收集患者人口统计数据、临床表现特征、肿瘤特征和随访情况。排除睫状体延伸的虹膜黑色素瘤。使用具有 10 倍交叉验证的 Lasso logistic 回归来选择调整参数。使用曲线下面积 （AUC） 评估鉴别力，并通过图进行校准。结果 痣和黑色素瘤的位置存在显着不对称性，分别优先于虹膜下象限 （83， 74%） 和 （79， 79%） （p = 0.50）。肿瘤播散、青光眼和前房积血仅存在于黑色素瘤中。支持黑色素瘤诊断的特征是大小（身高增加 （OR 3.35）;最大基底直径增加 （OR 1.64））、瞳孔变形（睑外翻葡萄膜或核心睑缘 （OR 2.55））、外周延伸（角或虹膜根受累 （OR 2.83））、继发效应（色素分散 （OR 1.12））和血管分布 （OR 6.79）。乐观校正后的 AUC 为 0.865。校准图表明校准良好，大多数点都落在同一线附近，而在大多数概率范围内都包含同一线的置信带。结论 预测模型提供病变为虹膜黑色素瘤的直接诊断预测，以概率 （%） 表示。使用预测计算器 （app） 可以增强决策和患者咨询。可以使用额外的数据集进行进一步优化，从而为自动诊断奠定基础。 数据可应合理要求提供。