Cancer Genomics is the study of genetic alterations and mutations that drive cancer initiation and progression. NECLA applies cancer genomics in its biomedical AI efforts, developing machine learning models to analyze complex genomic and pathology data. Projects such as digital pathology and ePathologist leverage AI to uncover genomic markers, aiding precision oncology, improving early detection, and advancing personalized treatment strategies that support global healthcare innovation.

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Pathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma

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Tumor-infiltrating lymphocytes (TILs) are a provocative biomarker in melanoma, influencing diagnosis, prognosis, and immunotherapy outcomes; however, traditional pathologistreadTIL assessment on hematoxylin and eosin–stained slides is prone to interobserver variability, leading to inconsistent clinical decisions. Therefore, development of newer TIL scoring approachesthat produce more reliable and consistent readouts is important.

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