Operative Complications of Posterior Uveal Melanoma Surgery

Ebert, Jared J.; Nicola, Maura Di; Williams, Basil K.

doi:10.1097/iio.0000000000000428

Cited by 3 publications

(2 citation statements)

References 100 publications

(310 reference statements)

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“…The most common symptoms are blurry vision (37.8%), flashing lights (8.6%), visual field defect (6.1%), pain (2.4%), and metamorphopsia (2.2%), and patients can be asymptomatic during diagnosing time (30.2%) [ 23 ]. Choroidal melanomas can lead to exudative retinal detachment based on the tumour shape [ 24 ].…”

Section: Historymentioning

confidence: 99%

Choroidal Melanoma: A Mini Review

2023

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Choroidal melanoma is a rare malignant tumour, yet it is the most common primary intra-ocular neoplasm and second on the list of top ten most malignant melanoma sites in the body. Clinical presentation can be non-specific and includes photopsia, floaters, progressive visual field loss, and blurry vision. The tumour is quite often diagnosed clinically during fundus examination; however, the most valued diagnostic tests are A- and B-scan ultrasonography (US). Several factors affect prognosis, including the patient’s age, tumour size, histological features, and presence of metastases. Still, with primary treatment and tight surveillance, around 50% of choroidal melanoma patients metastasise.

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Section: Historymentioning

confidence: 99%

Choroidal Melanoma: A Mini Review

2023

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“… 4 – 6 The risks associated with biopsy in small choroidal tumors are especially high. 7 , 8 Therefore, clinicians base their diagnosis on careful clinical examination and multimodal imaging, including fundus photography, autofluorescence, optical coherence tomography (OCT), and ultrasonography, to evaluate patients with melanocytic choroidal tumors. However, indeterminate lesions where a definite diagnosis cannot be made are often observed to monitor for tumor growth with serial examination and imaging.…”

Section: Introductionmentioning

confidence: 99%

Predicting Malignant Transformation of Choroidal Nevi Using Machine Learning

Iddir,

Love,

et al. 2023

Preprint

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Objective This study aims to assess a machine learning (ML) algorithm using multimodal imaging to accurately identify risk factors for uveal melanoma (UM) and aid in the diagnosis of melanocytic choroidal tumors. Subjects and Methods This study included 223 eyes from 221 patients with melanocytic choroidal lesions seen at the eye clinic of the University of Illinois at Chicago between 01/2010 and 07/2022. An ML algorithm was developed and trained on ultra-widefield fundus imaging and B-scan ultrasonography to detect risk factors of malignant transformation of choroidal lesions into UM. The risk factors were verified using all multimodal imaging available from the time of diagnosis. We also explore classification of lesions into UM and choroidal nevi using the ML algorithm. Results The ML algorithm assessed features of ultra-widefield fundus imaging and B-scan ultrasonography to determine the presence of the following risk factors for malignant transformation: lesion thickness, subretinal fluid, orange pigment, proximity to optic nerve, ultrasound hollowness, and drusen. The algorithm also provided classification of lesions into UM and choroidal nevi. A total of 115 patients with choroidal nevi and 108 patients with UM were included. The mean lesion thickness for choroidal nevi was 1.6 mm and for UM was 5.9 mm. Eleven ML models were implemented and achieved high accuracy, with an area under the curve of 0.982 for thickness prediction and 0.964 for subretinal fluid prediction. Sensitivity/specificity values ranged from 0.900/0.818 to 1.000/0.727 for different features. The ML algorithm demonstrated high accuracy in identifying risk factors and differentiating lesions based on the analyzed imaging data. Conclusions This study provides proof of concept that ML can accurately identify risk factors for malignant transformation in melanocytic choroidal tumors based on a single ultra-widefield fundus image or B-scan ultrasound at the time of initial presentation. By leveraging the efficiency and availability of ML, this study has the potential to provide a non-invasive tool that helps to prevent unnecessary treatment, improve our ability to predict malignant transformation, reduce the risk of metastasis, and potentially save patient lives.

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