
Dr Chao Li is a Senior Research Fellow with expertise in both healthcare and AI innovation, with comprehensive experience in developing image-based AI and multi-omics approaches to model neurological diseases. Dr Li is particularly interested in developing cost-effective AI models and translating these models into healthcare management to promote personalised medicine. His research is surrouding the below themes: 1. Image-based AI for precision mental health. 2. Image-based AI for precision surgical and interventional oncology. 3. Multi-omics AI for disease characterisation and precision medicine. 4. Efficacy and safety assessment of AI innovations for clinical translation and enterprise.
Publications
Abstracts from the BNOS 2019 Meeting July 3–5, 2019 London, UK
– Neuro-oncology
(2019)
21,
NP
Intra-tumoural perfusion habitats showed prognostic value in glioblastoma patients
– Neuro-oncology
(2019)
21,
iv3
Intratumoral Heterogeneity of Glioblastoma Infiltration Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging.
– Neurosurgery
(2019)
85,
524
(DOI: 10.1093/neuros/nyy388)
P14.129 Predicting glioblastoma invasion using multiparametric MRI and a bi-level machine learning approach
– Neuro-Oncology
(2019)
21,
99
(DOI: 10.1093/neuonc/noz126.364)
14th Meeting of the European Association of Neuro-Oncology September 19–22, 2019 Lyon, France
– Neuro-oncology
(2019)
21,
NP
Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma
– European Radiology
(2019)
29,
4718
(DOI: 10.1101/235861)
Multimodal MRI characteristics of the glioblastoma infiltration beyond contrast enhancement.
– Therapeutic Advances in Neurological Disorders
(2019)
12,
175628641984466
(DOI: 10.1177/1756286419844664)
Decoding the inter-dependence of multiparametric magnetic resonance imaging to reveal patient subgroups correlated with survivals
– Neoplasia (New York, N.Y.)
(2019)
21,
442
(DOI: 10.1016/j.neo.2019.03.005)
Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival
– Radiotherapy and Oncology
(2019)
134,
17
(DOI: 10.1016/j.radonc.2019.01.008)
Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging
– Journal of neurosurgery
(2019)
132,
1
(DOI: 10.3171/2018.12.JNS182926)
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