Identification of drug-resistant cancer cells in flow cytometry combining 3D holographic tomography with machine learning

Abstract

Identifying drug-resistant cancer cells is of fundamental importance to afford disease and find the most effective therapies for the patients. Recently, label-free imaging flow cytometry has been deeply investigated in cell recognition. In particular, the combination of flow cytometry and machine learning allows for achieving high accuracy in cell identification and high throughput. Despite the encouraging results, the potentialities of digital holography (DH) in flow-cytometry modality have not been exploited in full. Up to now, only 2D phase maps have been used in all previously reported research about the use of DH for analyzing flowing cells. Here we show that having access to the whole 3D information of each flowing cell can improve the cells identification. We used digital holographic flow cytometry to collect images of flowing cells and reconstructed their 3D tomographic phase. And for the first time, we extracted scores of meaningful morphometric features from the 3D and 2D phase maps through machine learning methods and finally compare their classification performance. The results show that 3D features can achieve higher classification accuracy with respect to sole 2D analysis demonstrating that 3D morphology information can yield advantages in recognizing drug-resistant endometrial cancer cells, thus allowing a significant step forward in performance of label-free cell classification.