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Lung cancer, in a majority of cases associated with smoking, ranks as the second leading type of cancer globally. The predominant forms are non-small cell carcinoma and small cell carcinoma. Lung cancer is diagnosed based on biopsies, surgical resection specimens, or cytology. Standard work-up of histopathological lung cancer samples includes Immunohistochemistry (IHC) staining, which allows the visualization of specific proteins expressed on cellular structures in the sample. The present use case to focus on tumor/stroma and immune cell evaluation in non-small cell lung cancer is clinically relevant. As computational methods become increasingly adopted in clinical settings, they are frequently employed, for instance, to quantify tumor cell content prior to processing lung cancer biopsies for molecular pathology analyses. Moreover, computational methods play a key role in evaluating immune cells and detecting immune checkpoint markers in distinct tissue sections. These analyses are essential for designing targeted immuno-oncology treatments. Current pathological analysis of these samples is both time-intensive and challenging, often hinging on the expertise of a few highly skilled pathologists. This reliance can introduce variability in diagnoses, possibly leading to inconsistent patient outcomes. An automated solution using computer vision, however, has the potential to assist pathologists in achieving a more accurate and consistent diagnosis. Our paper introduces a novel approach that leverages deep unsupervised learning techniques to autonomously label regions within IHC-stained samples. By extracting radiomic features from small patches in whole slide images and utilizing Self-Organizing Maps, we developed a robust clustering model. Additionally, we introduced a novel database of IHC-stained lung cancer pathological images. Our findings indicate that unsupervised clustering is a promising approach to meet the increasing demand for high-quality annotations in the emerging field of computational pathology.
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