To use optical coherence tomography angiography (OCTA) to study longitudinal subclinical choroidal neovascularization (CNV) changes and their correlation with progression to exudation in age-related macular degeneration (AMD). This study included a total of 34 patients with unilateral neovascular AMD who were evaluated prospectively using OCTA to detect subclinical CNV in their fellow eye. Eyes with baseline subclinical CNV were followed with serial OCTA for a minimum of one year (15.2±3.27 months) to monitor the development of exudation. Of the 34 fellow eyes studied, five were found to have baseline subclinical CNV. One of the five cases of baseline subclinical CNV converted to exudative AMD during the follow up period. The average surface area of baseline subclinical CNV on OCTA was 0.131±0.096 mm² which progressed to 0.136±0.104 mm² at the final follow up (P = 0.539). Geographic atrophy grew at a rate of 0.82±1.20mm²/year in four eyes without subclinical CNV and 0.02mm²/year in one eye with subclinical CNV. The rate of conversion to exudative AMD in eyes with subclinical CNV of 20% in our study is similar to previous reports and suggests the importance of vigilance in these eyes. The lower growth rate of geographic atrophy may suggest a protective effect of subclinical CNV that deserves further study.

Pancreatic cancer remains a daunting foe despite a vast number of accumulating molecular analyses regarding the mutation and expression status of a variety of genes. Indeed, most pancreatic cancer cases uniformly present with a mutation in the KRAS allele leading to enhanced RAS activation. Yet our understanding of the many epigenetic/environmental factors contributing to disease incidence and progression is waning. Epidemiologic data suggest that diet may be a key factor in pancreatic cancer development and potentially a means of chemoprevention at earlier stages. While diets high in ω3 fatty acids are typically associated with tumor suppression, diets high in ω6 fatty acids have been linked to increased tumor development. Thus, to better understand the contribution of these polyunsaturated fatty acids to pancreatic carcinogenesis, we modeled early stage disease by targeting mutant KRAS to the exocrine pancreas and administered diets rich in these fatty acids to assess tumor formation and altered cell-signaling pathways. We discovered that, consistent with previous reports, the ω3-enriched diet led to reduced lesion penetrance via repression of proliferation associated with reduced phosphorylated AKT (pAKT), whereas the ω6-enriched diet accelerated tumor formation. These data provide a plausible mechanism underlying previously observed effects of fatty acids and suggest that administration of ω3 fatty acids can reduce the pro-survival, pro-growth functions of pAKT. Indeed, counseling subjects at risk to increase their intake of foods containing higher amounts of ω3 fatty acids could aid in the prevention of pancreatic cancer.

Due to its accessibility and ability for real-time image acquisition of ocular structures, ultrasound has high utility in the visualization of the eye, especially in ocular oncology. In this minireview, we summarize the technical rationale and applications of ultrasound modalities, A-scan, B-scan, high-frequency ultrasound biomicroscopy (UBM), and Doppler measurement. A-scan ultrasound uses a transducer of 7–11 MHz, making it useful for determining the echogenicity of ocular tumors (7–8 MHz) and measuring the axial length of the eye (10–11 MHz). B-scan ultrasound operates at 10–20 MHz, which can be used for measuring posterior ocular tumors while UBM operates at 40–100 MHz to evaluate anterior ocular structures. Doppler ultrasonography allows for the detection of tumor vascularization. While ultrasonography has numerous clinical applications due to its favorable penetration compared with optical coherence tomography, it is still limited by its relatively lower resolution. Ultrasound also requires an experienced sonographer due to the need for accurate probe localization to areas of interest.

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. 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. 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. 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.

Advancements in machine learning and deep learning have the potential to revolutionize the diagnosis of melanocytic choroidal tumors, including uveal melanoma, a potentially life-threatening eye cancer. Traditional machine learning methods rely heavily on manually selected image features, which can limit diagnostic accuracy and lead to variability in results. In contrast, deep learning models, particularly convolutional neural networks (CNNs), are capable of automatically analyzing medical images, identifying complex patterns, and enhancing diagnostic precision. This review evaluates recent studies that apply machine learning and deep learning approaches to classify uveal melanoma using imaging modalities such as fundus photography, optical coherence tomography (OCT), and ultrasound. The review critically examines each study’s research design, methodology, and reported performance metrics, discussing strengths as well as limitations. While fundus photography is the predominant imaging modality being used in current research, integrating multiple imaging techniques, such as OCT and ultrasound, may enhance diagnostic accuracy by combining surface and structural information about the tumor. Key limitations across studies include small dataset sizes, limited external validation, and a reliance on single imaging modalities, all of which restrict model generalizability in clinical settings. Metrics such as accuracy, sensitivity, and area under the curve (AUC) indicate that deep learning models have the potential to outperform traditional methods, supporting their further development for integration into clinical workflows. Future research should aim to address current limitations by developing multimodal models that leverage larger, diverse datasets and rigorous validation, thereby paving the way for more comprehensive, reliable diagnostic tools in ocular oncology.