Comparison of MOLES and MelAInoma for Differentiating Small Choroidal Melanomas from Nevi

TL;DR

This study compares two tools for identifying small eye melanomas, finding that an AI system called MelAInoma performs slightly better than a manual scoring system.

Contribution

The study demonstrates that MelAInoma, an AI-based tool, provides slightly stronger diagnostic associations than the MOLES system using fewer data.

Findings

MelAInoma scores showed a stronger association with melanoma diagnosis than MOLES scores.

Both tools are effective but capture partly different information.

MelAInoma provides fully reproducible results and could aid in triaging eye lesions.

Abstract

Small eye melanomas can be difficult to distinguish from harmless pigmented spots in the back of the eye, called choroidal nevi. Because the risk of cancer spread increases as melanomas grow, early and accurate identification is important, but unnecessary referrals and treatment should also be avoided. Tools that support non-expert clinicians in deciding which lesions need specialist evaluation are therefore increasingly important. In this study, we compared two such tools: the MOLES scoring system, which is manually applied using several eye imaging methods, and MelAInoma, an artificial intelligence system that analyzes a single color photograph of the eye. We examined how closely the two methods agree and how well each is associated with expert diagnosis of melanoma. Both tools were useful, but they captured partly different information. MelAInoma showed a slightly stronger association with diagnosis despite using less imaging data, suggesting a potential role as a complementary aid for lesion triage.

Background: Early identification of small choroidal melanomas is important, as metastatic risk increases with tumor size. However, distinguishing small melanomas from benign choroidal nevi is challenging and may lead to unnecessary referrals and overtreatment. Both the MOLES scoring system and the deep learning algorithm MelAInoma have been developed to support assessment of pigmented choroidal lesions in non-expert settings. This study aims to compare the association between MOLES and MelAInoma scores and to assess their relative association with expert melanoma versus nevus diagnosis. Methods: In this retrospective cohort study, 86 patients with small pigmented choroidal lesions (29 melanomas and 57 nevi) diagnosed at a national ocular oncology referral center were included. MOLES scores were assigned by ocular oncologists based on multimodal examination, whereas MelAInoma scores were generated solely from color fundus photographs. Associations between scores were assessed using linear regression and the Jonckheere–Terpstra test. Univariable and multivariable binary logistic regression was used to evaluate associations with melanoma diagnosis. Results: MelAInoma scores increased monotonically with higher MOLES categories (p = 0.0001). Linear regression showed a statistically significant association between MOLES and MelAInoma scores, but with substantial dispersion (R2 = 0.16). In univariable logistic regression, both MOLES and MelAInoma scores were associated with increased odds of melanoma diagnosis. MelAInoma showed a stronger association with diagnosis than MOLES (R2 = 0.38 vs. 0.27). In multivariable analysis including both scores, each remained independently associated with melanoma diagnosis. Conclusions: Both MOLES and MelAInoma are effective for differentiating small choroidal melanomas from nevi. Although the scores are statistically associated, they capture partly distinct information. MelAInoma demonstrates slightly stronger association with melanoma diagnosis and provides fully reproducible output, supporting its role as a complementary aid in lesion triage.

Figures