Theranostics 2020; 10(24):11026-11048. doi:10.7150/thno.44053

Research Paper

ALICE: a hybrid AI paradigm with enhanced connectivity and cybersecurity for a serendipitous encounter with circulating hybrid cells

Kok Suen Cheng1, Rongbin Pan2, Huaping Pan2, Binglin Li2, Stephene Shadrack Meena2, Huan Xing2, Ying Jing Ng2, Kaili Qin2, Xuan Liao2, Benson Kiprono Kosgei2, Zhipeng Wang1, Ray P.S. Han1,2✉

1. College of Engineering, Peking University, Beijing 100871, China.
2. Jiangzhong Cancer Research Center, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, China 330004.

This is an open access article distributed under the terms of the Creative Commons Attribution License ( See for full terms and conditions.
Cheng KS, Pan R, Pan H, Li B, Meena SS, Xing H, Ng YJ, Qin K, Liao X, Kosgei BK, Wang Z, Han RPS. ALICE: a hybrid AI paradigm with enhanced connectivity and cybersecurity for a serendipitous encounter with circulating hybrid cells. Theranostics 2020; 10(24):11026-11048. doi:10.7150/thno.44053. Available from

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A fully automated and accurate assay of rare cell phenotypes in densely-packed fluorescently-labeled liquid biopsy images remains elusive.

Methods: Employing a hybrid artificial intelligence (AI) paradigm that combines traditional rule-based morphological manipulations with modern statistical machine learning, we deployed a next generation software, ALICE (Automated Liquid Biopsy Cell Enumerator) to identify and enumerate minute amounts of tumor cell phenotypes bestrewed in massive populations of leukocytes. As a code designed for futurity, ALICE is armed with internet of things (IOT) connectivity to promote pedagogy and continuing education and also, an advanced cybersecurity system to safeguard against digital attacks from malicious data tampering.

Results: By combining robust principal component analysis, random forest classifier and cubic support vector machine, ALICE was able to detect synthetic, anomalous and tampered input images with an average recall and precision of 0.840 and 0.752, respectively. In terms of phenotyping enumeration, ALICE was able to enumerate various circulating tumor cell (CTC) phenotypes with a reliability ranging from 0.725 (substantial agreement) to 0.961 (almost perfect) as compared to human analysts. Further, two subpopulations of circulating hybrid cells (CHCs) were serendipitously discovered and labeled as CHC-1 (DAPI+/CD45+/E-cadherin+/vimentin-) and CHC-2 (DAPI+ /CD45+/E-cadherin+/vimentin+) in the peripheral blood of pancreatic cancer patients. CHC-1 was found to correlate with nodal staging and was able to classify lymph node metastasis with a sensitivity of 0.615 (95% CI: 0.374-0.898) and specificity of 1.000 (95% CI: 1.000-1.000).

Conclusion: This study presented a machine-learning-augmented rule-based hybrid AI algorithm with enhanced cybersecurity and connectivity for the automatic and flexibly-adapting enumeration of cellular liquid biopsies. ALICE has the potential to be used in a clinical setting for an accurate and reliable enumeration of CTC phenotypes.

Keywords: ALICE, cell phenotyping software, hybrid artificial intelligence, image forgery detection, circulating hybrid cells