What’s in a Name? Large Choroidal Nevus, Small Choroidal Melanoma, or Indeterminate Melanocytic Tumor

Singh, Arun D.; Grossniklaus, Hans E.

doi:10.1159/000516536

Cited by 12 publications

(2 citation statements)

References 39 publications

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“…Aside from the pigment itself that would be appreciated grossly, the presence of orange pigment and subretinal fluid favors a diagnosis of uveal melanoma, whereas drusen and intraretinal cystic spaces are signs of a long-standing stable lesion like a benign nevus. 20 Gonioscopy may also be used to assess involvement of the anterior chamber angle.…”

Section: Diagnosismentioning

confidence: 99%

Uveal Melanoma

Sadowsky

Delijani

Lim

et al. 2022

Georgetown Medical Review

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Uveal melanoma, a malignant proliferation of melanocytes in the uvea, is the most common primary tumor of the eye in adults. The purpose of this review is to discuss the risk factors, clinical presentation, diagnosis, prognosis, and management, while providing information about novel viewpoints and tools for diagnosis and treatment of uveal melanoma. Blue-light exposure, BAP1 polymorphisms, complete monosomy 3, and history of ocular melanocytosis are the greatest risk factors for the development of uveal melanoma. Uveal melanoma can first present either on routine eye exam or upon presentation with flashes, floaters, and visual field defects. Diagnosis of uveal melanoma is made with anterior segment ocular coherence tomography or ultrasound biomicroscopy. Fine-needle biopsy of the lesion often follows diagnosis, with gene expression profiling being the best prognostic indicator for uveal melanoma. The gold standard for treatment of uveal melanoma is plaque brachytherapy. Other treatment possibilities for uveal melanoma include enucleation, charged-particle radiation therapy, transpupillary thermotherapy and photodynamic thermotherapy. Uveal melanoma may be fatal if untreated and subsequently metastasizes. Hopefully, earlier detection methods including the use of circulating tumor cells, circulating tumor DNA, and circulating MicroRNA and subsequent treatment using novel targeted therapies like high-intensity focused ultrasound, iontophoresis and electrochemotherapy would be crucial to improving prognosis in the future.

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Section: Diagnosismentioning

confidence: 99%

Uveal Melanoma

Sadowsky

Delijani

Lim

et al. 2022

Georgetown Medical Review

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“…Uveal melanoma (UM) and choroidal nevi are melanocytic choroidal tumors with shared clinical characteristics, posing a significant challenge in their differentiation 1,2 . However, accurately distinguishing between these two lesions is crucial for appropriate management.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for automated diagnosis of uveal melanoma

Dadzie,

Iddir,

Abtahi

et al. 2024

Ophthalmic Technologies XXXIV

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Accurate differentiation of uveal melanoma and choroidal nevi is critical for optimal patient care, preventing unnecessary procedures for benign lesions while ensuring timely intervention for potentially malignant cases. This study aimed to validate deep learning classification of these lesions and to evaluate the impact of different color fusion options on classification performance. To evaluate the effect of color fusion options on the classification performance, we tested early fusion, intermediate fusion, and late fusion using ultra-widefield retinal images. Specificity, sensitivity, F1-score, accuracy, and the area under the curve (AUC) of a receiver operating characteristic (ROC) were used to assess the performance of the deep learning model. The results show that the color fusion options significantly impacted the deep learning classification performance, with intermediate fusion emerging as the best strategy, outperforming both singlecolor learning and the other fusion strategies. The intermediate fusion strategy had an accuracy of 89.72%, sensitivity of 85.05%, specificity of 91.64, F1 score of 0.8492 and an AUC of 0.9335. These compelling results emphasize the vast potential of deep learning to enhance the accuracy of diagnosis and classification of UM and choroidal nevi, leading to improved patient outcomes and optimized treatment strategies. By harnessing the power of deep learning and color fusion strategies, this study not only provides valuable insights into the application of these approaches in the field of ophthalmology but also highlights their critical significance in automating the classification of UM and choroidal nevi.

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