Dr Chao Li is a Principal 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 surrounding 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
RADIOMIC FEATURES FROM PHYSIOLOGICAL MRI SHOWS IMPROVED ACCURACY OVER STRUCTURAL MRI IN PREDICTING MGMT PROMOTER METHYLATION IN GLIOBLASTOMA
– Neuro-Oncology
(2018)
20,
352
(doi: 10.1093/neuonc/noy129.039)
Ventricle contact is associated with lower survival and increased peritumoral perfusion in glioblastoma.
– J Neurosurg
(2018)
131,
717
(doi: 10.3171/2018.5.JNS18340)
RADIOMIC FEATURES FROM PHYSIOLOGICAL MRI SHOWS IMPROVED ACCURACY OVER STRUCTURAL MRI IN PREDICTING MGMT PROMOTER METHYLATION IN GLIOBLASTOMA
– Neuro-oncology
(2018)
20,
v352
Intratumoral Heterogeneity of Glioblastoma Infiltration Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging.
– Neurosurgery
(2018)
85,
524
(doi: 10.1093/neuros/nyy388)
Intratumoral Heterogeneity of Tumor Infiltration of Glioblastoma Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging
(2017)
187450
(doi: 10.1101/187450)
Subventricular Zone Involvement Characterized by Diffusion Tensor Imaging in Glioblastoma
– World neurosurgery
(2017)
105,
697
(doi: 10.1016/j.wneu.2017.06.075)
Low Perfusion Compartments in Glioblastoma Quantified by Advanced Magnetic Resonance Imaging and Correlated with Patient Survival
(2017)
(doi: 10.1101/180521)
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