Classification of cancer cells using computational analysis of dynamic morphology (original) (raw)
2018, Computer methods and programs in biomedicine
Detection of metastatic tumor cells is important for early diagnosis and staging of cancer. However, such cells are exceedingly difficult to detect from blood or biopsy samples at the disease onset. It is reported that cancer cells, and especially metastatic tumor cells, show very distinctive morphological behavior compared to their healthy counterparts on aptamer functionalized substrates. The ability to quickly analyze the data and quantify the cell morphology for an instant real-time feedback can certainly contribute to early cancer diagnosis. A supervised machine learning approach is presented for identification and classification of cancer cell gestures for early diagnosis. We quantified the morphologically distinct behavior of metastatic cells and their healthy counterparts captured on aptamer-functionalized glass substrates from time-lapse optical micrographs. As a proof of concept, the morphologies of human glioblastoma (hGBM) and astrocyte cells were used. The cells were ca...
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