pone.0357972.t003 - <p> </p>

While these models are more physiologically relevant, they present challenges that have not been previously considered. Certain aspects of 3D cultures, such as cell death, need to be modelled as they have a profound impact on spheroid survival and therefore on cancer treatment testing.</p> <p>Aim</p><p>To identify necrosis in HT-29 and Hs766-T cancer spheroids using an accurate, cost-effective, and fast machine learning-based system that relies only on brightfield (BF) images.</p> <p>Approach</p><p>In this study, three staining procedures with dyes were performed in colorectal and pancreatic cancer spheroids to highlight necrosis.

The microscopic images of these stains serve as a ground truth for different machine learning models, which can subsequently learn to detect necrosis from the brightfield image, i.e., without the need for staining.</p> <p>Results</p><p>The results show a strong correlation between dark spots in the spheroid and their staining as necrotic, making regression models an appropriate solution. In general, the supervised models performed better than the unsupervised ones.