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Predictive model for iris melanoma
  1. Arun Singh1,
  2. Alexander Melendez-Moreno2,
  3. Jørgen Krohn3,4,
  4. Emily C Zabor5
  1. 1Department of Ophthalmic Oncology, Cleveland Clinic Main Campus Hospital, Cleveland, Ohio, USA
  2. 2Cleveland Clinic Main Campus Hospital, Cleveland, Ohio, USA
  3. 3Department of Clinical Medicine, Bergen University College, Bergen, Norway
  4. 4Department of Ophthalmology, Haukeland University Hospital, Bergen, Norway
  5. 5Department of Quantitative Health Sciences & Taussig Cancer Institute, Cleveland Clinic Main Campus Hospital, Cleveland, Ohio, USA
  1. Correspondence to Dr Arun Singh, Department of Ophthalmic Oncology, Cleveland Clinic Main Campus Hospital, Cleveland, Ohio, USA; singha{at}


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.

  • Iris
  • Neoplasia
  • Pathology

Data availability statement

Data are available on reasonable request.

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Data availability statement

Data are available on reasonable request.

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  • Contributors AS: study design, data interpretation, drafting manuscript, critical revision of manuscript AM-M: data collection, interpretation, drafting, critical revision of manuscript. JK: data analysis and interpretation, critical revision of manuscript. ECZ: statistical analysis, data interpretation, drafting manuscript.

  • Funding This work was supported by a Research to Prevent Blindness Challenge Grant (Department of Ophthalmology, Cleveland Clinic Lerner College of Medicine) and Cole Family Endowment for Ophthalmic Oncology.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.