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Recurrence and Survival Prediction for Liver Cancer
Project type
Collaboration Project
Date
Jul 2019- Jun 2021
Location
Taiwan
Skills
- Matlab: data structuring, converting RTSS (data structure for medical images) to jpg, tumour contouring, generating radiomics features, data cleaning
- Python (sklearn, numpy, matplotlib): ML modeling, data cleaning, generating plots
- Basic Linux command lines: evolutionary learning (modelling and optimisation)
- Microsoft Excel: data cleaning
- Microsoft PowerPoint: weekly and monthly meeting
This project was hosted by National Yang Ming Chiao Tung University and Taipei Veterans General Hospital and supported by the Ministry of Science and Technology, Taiwan,
"Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection"
- Developing an evolutionary learning-derived method GARSL, genetic algorithm for predicting recurrence after surgery of liver cancer
- Converting medical images (CT) into radiomics features and combining them with clinical laboratory testing features
- Using evolutionary learning to optimise and predict 5-year recurrence for HCC patients who underwent partial resection operation and 3-year survival for HCC transarterial chemoembolisation(TACE) patients
