ArticleOcular oncology and pathology2022
A Prediction Model to Discriminate Small Choroidal Melanoma from Choroidal Nevus.
Article in Ocular oncology and pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Artificial Intelligence-Driven Differentiation Between Uveal Melanoma and Nevus Based on Fundus Photographs: A Systematic Review and Meta-Analysis.Translational vision science & technology · 2026Pooled it
- A systematic review and meta-analysis of enucleation versus pre-enucleation radiotherapy in choroidal melanoma mortality: evidence from 10 comparative studies.International ophthalmology · 2025Pooled it
- Imaging-based machine learning for the diagnosis and prognosis of uveal melanoma: a systematic review and meta analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Structured Clinicopathologic Data as a Prerequisite for Generalizable AI in Ocular Oncology.Ocular oncology and pathology · 2026Article
- Artificial Intelligence in Ocular Oncology: Differentiating Choroidal Melanocytic Lesions.Ophthalmology science · 2026Article
- Development of a deep learning model to classify choroidal melanoma risk factors based on color fundus photographs.AJO international · 2025Article
- Artificial Intelligence in the Detection and Risk Stratification of Choroidal Melanoma: A Critical Comparative Synthesis and Future Directions.Healthcare (Basel, Switzerland) · 2025Review
- Clinical Applications of Artificial Intelligence in Uveal Melanoma.Anticancer research · 2025Review
- The Small Choroidal Melanoma: Diagnosis, Treatment Outcomes, and Unending Controversy.Seminars in ophthalmology · 2025Review
- Assessing the Clinical Utility of Multimodal Large Language Models in the Diagnosis and Management of Pigmented Choroidal Lesions.Translational vision science & technology · 2025Article
- Non-invasive detection of choroidal melanoma via tear-derived protein corona on gold nanoparticles: a machine learning approach.Scientific reports · 2025Article
- Clinical Applications of Optical Coherence Tomography and Optical Coherence Tomography Angiography in Uveal Melanoma: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence in the diagnosis of uveal melanoma: advances and applications.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Uveal Melanoma: Comprehensive Review of Its Pathophysiology, Diagnosis, Treatment, and Future Perspectives.Biomedicines · 2024Review
- Advances in multimodal imaging for diagnosis of pigmented ocular fundus lesions.Canadian journal of ophthalmology. Journal canadien d'ophtalmologie · 2024Review
- Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review.Investigative ophthalmology & visual science · 2023Review
- Predicting Choroidal Nevus Transformation to Melanoma Using Machine Learning.Ophthalmology scienceArticle
- Differentiating Choroidal Melanomas and Nevi Using a Self-Supervised Deep Learning Model Applied to Clinical Fundoscopy Images.Ophthalmology scienceArticle
- Growth of indeterminate choroidal melanocytic tumors: Time to malignant transformation.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to develop a validated machine learning model to diagnose small choroidal melanoma. Design: This is a cohort study. Subjects Participants and/or Controls: The training data included 123 patients diagnosed as small choroidal melanocytic tumor (5.0-16.0 mm in largest basal diameter and 1.0 mm-2.5 mm in height; Collaborative Ocular Melanoma Study criteria). Those diagnosed as melanoma ( Methods: In the training data, lasso logistic regression was used to select variables for inclusion in the final model for the association with melanoma versus choroidal nevus. Internal and external validation was performed to assess model performance. Main Outcome Measures: The main outcome measure is the predicted probability of small choroidal melanoma. Results: Distance to optic disc ≥3 mm and drusen were associated with decreased odds of melanoma, whereas male versus female sex, increased height, subretinal fluid, and orange pigment were associated with increased odds of choroidal melanoma. The area under the receiver operating characteristic "discrimination value" for this model was 0.880. The top four variables that were most frequently selected for inclusion in the model on internal validation, implying their importance as predictors of melanoma, were subretinal fluid, height, distance to optic disc, and orange pigment. When tested against the validation data, the prediction model could distinguish between choroidal nevus and melanoma with a high discrimination of 0.861. The final prediction model was converted into an online calculator to generate predicted probability of melanoma. Conclusions: To minimize diagnostic uncertainty, a machine learning-based diagnostic prediction calculator can be readily applied for decision-making and counseling patients with small choroidal melanoma.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.