Conjunctival squamous carcinoma, which is regarded as a low-grade malignancy, usually originates at the limbus. Most cases remain superficial to the sclera. Intraocular invasion is rarely reported. We describe a woman misdiagnosed as conjunctivitis and pterygium before a clinical diagnosis of conjunctival squamous cell carcinoma. The diagnosis was made by histopathological examination of the biopsy specimen. Examination revealed an elevated mass on the nasal limbus extending intraocularly. White flaky mass occupied approximately 3/7 space of the anterior chamber. Exenteration was performed for control of local lesion. Histopathologic analysis confirmed that intraocular involvement occurs through the emissary vessels near the area of limbus. The case highlights the need for accurate diagnosis and prompt intervention. A brief review of the clinical and histopathologic features of conjunctival squamous cell carcinoma is also presented.

Sebaceous eyelid adenocarcinomas showed a decrease in the membranous expression of E-cadherin and of β-catenin. These changes were associated with poor tumor differentiation and an increase in the tumor infiltration and inflammation, pointing at a potential role of a low E-cadherin and low β-catenin expression for poor prognosis of sebaceous eyelid adenocarcinomas. Correspondingly, the cell proliferation index and the expression of E-cadherin were inversely correlated with each other. The findings suggest that the immunohistochemical detection of a low E-cadherin and low β-catenin expression may help in the examination and staging of sebaceous eyelid adenocarcinomas.

This study is aimed at developing an intelligent algorithm based on deep learning and discussing its application for the classification and diagnosis of retinal vein occlusions (RVO) using fundus images. A total of 501 fundus images of healthy eyes and patients with RVO were used for model training and testing to investigate an intelligent diagnosis system. The images were first classified into four categories by fundus disease specialists: (i) healthy fundus (group 0), (ii) branch RVO (BRVO) (group 1), (iii) central RVO (CRVO) (group 2), and (iv) macular branch RVO (MBRVO) (group 3), before being diagnosed using the ResNet18 network model. Intelligent diagnoses were compared with clinical diagnoses. The specificity of the intelligent diagnosis system under each attention mechanism was 100% in group 0 and also revealed a high sensitivity of over 95%, F 1 score of over 97%, and an accuracy of over 97% in this group. For the other three groups, the specificities of diagnosis ranged from 0.45 to 0.91 with different attention mechanisms, in which the ResNet18+coordinate attention (CA) model had the highest specificities of 0.91, 0.88, and 0.83 for groups 1, 2, and 3, respectively. It also provided a high accuracy of over 94% with a coordinate attention mechanism in all four groups. The intelligent diagnosis and classifier system developed herein based on deep learning can determine the presence of RVO and classify disease according to the site of occlusion. This proposed system is expected to provide a new tool for RVO diagnosis and screening and will help solve the current challenges due to the shortage of medical resources.

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