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Updated: Jun 12, 2025

Implantation and Evaluation of Melanoma in the Murine Choroid via Optical Coherence Tomography

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Published on: December 2, 2022

Predicting Choroidal Nevus Transformation to Melanoma Using Machine Learning.

Prashant D Tailor¹, Piotr K Kopinski¹, Haley S D'Souza¹

¹Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, 55905.

Ophthalmology Science

|September 25, 2024

Summary

This summary is machine-generated.

Machine learning models accurately predict choroidal nevus to melanoma transformation using multimodal imaging. The SAINTS model showed high accuracy in both training and external validation sets, identifying key predictive features.

Keywords:

Artificial Intelligence Choroidal melanoma Choroidal nevus Machine learning Ocular oncology

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Area of Science:

Ophthalmology
Medical Imaging
Machine Learning
Oncology

Background:

Choroidal nevi are common melanocytic tumors of the eye.
Predicting transformation to melanoma is crucial for patient management.
Multimodal imaging offers detailed characterization of ocular lesions.

Purpose of the Study:

To develop and validate machine learning (ML) models for predicting choroidal nevus to melanoma transformation.
To assess model performance using multimodal imaging data.
To identify key imaging features predictive of nevus transformation.

Main Methods:

Retrospective multicenter study involving patients with choroidal nevus.
Utilized multimodal imaging: fundus photography, autofluorescence, OCT, and ultrasonography.
Developed and optimized XGBoost, LGBM, Random Forest, and Extra Tree models, validated externally.

Main Results:

The Simple AI Nevus Transformation System (SAINTS) XGBoost model achieved high performance (pooled AUROC 0.864 in test, 0.931 in validation).
Key predictive features included tumor thickness, basal diameter, shape, distance to optic nerve, and subretinal fluid.
A model using nevi with ≥5 years follow-up showed improved AUPRC.

Conclusions:

ML models can accurately and generally predict choroidal nevus to melanoma transformation.
Multimodal imaging is valuable for risk stratification.
SAINTS model provides a robust tool for clinical decision-making.