Five-Year Follow-up of Microincisional Vitrectomy Surgery, Endolaser Tumor Ablation, and Gene-Expression Profiling in Small Uveal Melanoma

Confirmatory Cytopathology and Potential Impact on the Predictive Value of Gene Expression Profiling in Patients With Uveal Melanoma

Purpose: To determine whether the availability of a cytopathology-confirming diagnosis is correlated with the prognostic accuracy of a gene expression profiling assay. Methods: A single-center retrospective review was performed of patients diagnosed with uveal melanoma who had a fine-needle aspiration biopsy and gene expression profiling before proton therapy from 2012 to 2020. The development of metastases was compared in patients with gene expression profiling and cytopathology (gene expression profiling+cytopathology group) and patients with gene expression profiling only (gene expression profiling only group). Results: Of 141 patients with gene expression profiling, 98 (69.5%) had cytopathology results and 43 (30.5%) did not. The median tumor thickness was greater in the gene expression profiling+cytopathology group (5.0 mm) than in the gene expression profiling only group (3.1 mm) (P = .0003). The distribution of gene expression profiling class in these 2 groups, respectively, was class 1A, 38 (38.8%) vs 20 (46.5%); class 1B, 20 (20.4%) vs 15 (34.9%); class 2, 40 (40.8%) vs 8 (18.6%). Class 1A tumors metastasized in 4 patients (10.5%) in the gene expression profiling+cytopathology group and 3 patients (15.0%) in the gene expression profiling only group. Class 1B tumors metastasized in 3 patients (15.0%) and 1 patient (6.7%), and class 2 tumors metastasized in 18 patients (45.0%) and 5 patients (62.5%) in these 2 groups, respectively. The median months from initial treatment to metastasis diagnosis within each gene expression profiling class for the gene expression profiling+cytopathology and gene expression profiling only groups, respectively, was class 1A, 36.7 vs 33.6 (P = .86); class 1B, 37.8 vs 68.6 (P = 1.0); class 2, 19.0 vs 15.8 (P = .70). Conclusions: We found no evidence that the lack of confirmatory cytology negatively affects the accuracy of gene expression profiling, and no significant differences were found in the overall rates of metastasis between patients with and patients without cytopathology or rates within each class of gene expression profiling.

Automated segmentation for early detection of uveal melanoma

OBJECTIVE

Uveal melanoma is the most common intraocular malignancy in adults. Current screening and triaging methods for melanocytic choroidal tumours face inherent limitations, particularly in regions with limited access to specialized ocular oncologists. This study explores the potential of machine learning to automate tumour segmentation. We develop and evaluate a machine-learning model for lesion segmentation using ultra-wide-field fundus photography.

METHOD

A retrospective chart review was conducted of patients diagnosed with uveal melanoma, choroidal nevi, or congenital hypertrophy of the retinal pigmented epithelium at a tertiary academic medical centre. Included patients had a single ultra-wide-field fundus photograph (Optos PLC, Dunfermline, Fife, Scotland) of adequate quality to visualize the lesion of interest, as confirmed by a single ocular oncologist. These images were used to develop and test a machine-learning algorithm for lesion segmentation.

RESULTS

A total of 396 images were used to develop a machine-learning algorithm for lesion segmentation. Ninety additional images were used in the testing data set along with images of 30 healthy control individuals. Of the images with successfully detected lesions, the machine-learning segmentation yielded Dice coefficients of 0.86, 0.81, and 0.85 for uveal melanoma, choroidal nevi, and congenital hypertrophy of the retinal pigmented epithelium, respectively. Sensitivities for any lesion detection per image were 1.00, 0.90, and 0.87, respectively. For images without lesions, specificity was 0.93.

CONCLUSION

Our study demonstrates a novel machine-learning algorithm's performance, suggesting its potential clinical utility as a widely accessible method of screening choroidal tumours. Additional evaluation methods are necessary to further enhance the model's lesion classification and diagnostic accuracy.

Predicting Malignant Transformation of Choroidal Nevi Using Machine Learning

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.