Deep Learning-Assisted Assessing of Single Circulating Tumor Cell Viability via Cellular Morphology
Yiyao Yang1, Zhaoliang Wang2,3, Tingting Hao1
1State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, School of Material Science and Chemical Engineering, Ningbo University, Ningbo 315211, PR China.
Abstract:
Circulating tumor cells (CTCs) are closely associated with cancer metastasis and recurrence, so the assessment of CTC viability is crucial for diagnosis, prognosis evaluation, and efficacy judgment of cancer. Due to the extreme scarcity of CTCs in human blood, it is difficult to accurately evaluate the viability of a single CTC. In this study, a deep learning model based on a convolutional neural network was constructed and trained to extract the morphological features of CTCs with different viabilities defined by cell counting kit-8, achieve accurate CTC identification, and assess the viability of a single CTC. Being efficient, accurate, and noninvasive, it has a broad application prospect in biomedical directions.
