Development of machine learning-based clinical decision support system for hepatocellular carcinoma

Autor: Namkug Kim, Han Chu Lee, Yung Sang Lee, Kang Mo Kim, Danbi Lee, Jihye Yun, Gwang Hyeon Choi, Beomhee Park, Young Hwa Chung, Jonggi Choi, Ju Hyun Shim
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Survival Status
Male
Carcinoma
Hepatocellular
Radiofrequency ablation
Predictive medicine
MEDLINE
lcsh:Medicine
Machine learning
computer.software_genre
Clinical decision support system
Article
law.invention
Machine Learning
Gastrointestinal cancer
03 medical and health sciences
Medical research
0302 clinical medicine
law
Republic of Korea
Carcinoma
Medicine
Humans
lcsh:Science
Cancer
Aged
Neoplasm Staging
Retrospective Studies
Multidisciplinary
Hepatology
business.industry
Liver Neoplasms
lcsh:R
Gastroenterology
Retrospective cohort study
Hepatitis B
Middle Aged
medicine.disease
Decision Support Systems
Clinical
Computational biology and bioinformatics
Treatment Outcome
030220 oncology & carcinogenesis
Hepatocellular carcinoma
030211 gastroenterology & hepatology
Female
lcsh:Q
Artificial intelligence
business
computer
Zdroj: Scientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
Scientific Reports
ISSN: 2045-2322
Popis: There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from the currently used BCLC staging system. We develop a machine learning-based clinical decision support system (CDSS) for recommending initial treatment option in HCC and predicting overall survival (OS). From hospital records of 1,021 consecutive patients with HCC treated at a single centre in Korea between January 2010 and October 2010, we collected information on 61 pretreatment variables, initial treatment, and survival status. Twenty pretreatment key variables were finally selected. We developed the CDSS from the derivation set (N = 813) using random forest method and validated it in the validation set (N = 208). Among the 1,021 patients (mean age: 56.9 years), 81.8% were male and 77.0% had positive hepatitis B BCLC stages 0, A, B, C, and D were observed in 13.4%, 26.0%, 18.0%, 36.6%, and 6.3% of patients, respectively. The six multi-step classifier model was developed for treatment decision in a hierarchical manner, and showed good performance with 81.0% of accuracy for radiofrequency ablation (RFA) or resection versus not, 88.4% for RFA versus resection, and 76.8% for TACE or not. We also developed seven survival prediction models for each treatment option. Our newly developed HCC-CDSS model showed good performance in terms of treatment recommendation and OS prediction and may be used as a guidance in deciding the initial treatment option for HCC.
Databáze: OpenAIRE
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