Next-generation sequencing (NGS) has revolutionized cancer genomics, offering unparalleled insights into the molecular landscape of various malignancies, including ocular cancers. This review explores the role of NGS in ocular oncology, highlighting ...
Retinal fundus photographs are now widely used in developing artificial intelligence (AI) systems for the detection of various fundus diseases. However, the application of AI algorithms in intraocular tumors remains limited due to the scarcity of lar...
Proceedings of the National Academy of Sciences of the United States of America
Jul 31, 2025
Ocular adnexal lymphoma (OAL) is the most common orbital malignancy in adults. Advanced tools for precise diagnosis and prognosis of OAL are in demand. Here, the nanoparticle-enhanced laser desorption/ionization mass spectrometry was applied for the ...
To provide an artificial intelligence (AI) method using in vivo confocal microscopy (IVCM) to differentiate ocular surface squamous neoplasia (OSSN) from other lesions and compare the performance of well-known AI-related solutions. A dataset of 2,774...
OBJECTIVES: To evaluate the value of deep-learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI).
BACKGROUND: Breast cancer (BC) is caused by the uncontrolled proliferation of breast epithelial cells followed by malignant transformation, and it has the highest incidence among female malignant tumors. The metastasis of BC occurs through direct and...
PURPOSE: To evaluate nnU-net's performance in automatically segmenting and volumetrically measuring ocular adnexal lymphoma (OAL) on multi-sequence MRI.
This paper pioneers the exploration of ocular cancer, and its management with the help of Artificial Intelligence (AI) technology. Existing literature presents a significant increase in new eye cancer cases in 2023, experiencing a higher incidence ra...
OBJECTIVES: To evaluate the value of deep learning (DL) combining multimodal radiomics and clinical and imaging features for differentiating ocular adnexal lymphoma (OAL) from idiopathic orbital inflammation (IOI).
PURPOSE: To assess the performance of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics analysis for discriminating between uveal melanoma (UM) and other intraocular masses.
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