Mortality evaluation and life expectancy prediction of patients with Hepatocellular carcinoma with data minding

Author:

Liu Che-Yu1,Cheng Chen-Yang2,Yang Szu-Ying1,Chai Jyh-Wen1,Chen Wei-Hao3,Chang Pi-Yi1

Affiliation:

1. Taichung Veterans General Hospital

2. National Taipei University of Technology

3. National Yang Ming Chiao Tung University

Abstract

Abstract Background: The complexity of systemic variables and comorbidities make it difficult to determine the best treatment for patients with hepatocellular carcinoma (HCC). It is impossible to perform a multidimensional evaluation of every patient, but guidelines based on analyses of said complexities would be the next best option. Traditional statistics are inadequate for developing predictive models with many variables; however, data mining is well-suited to the task. Patients and Methods and finding: The clinical profiles and data of a total of 537 patients diagnosed with Barcelona Clinic Liver Cancer stages B and C from 2009 to 2019 were retrospectively analyzed using 4 decision-tree algorithms. 19 treatments, 7 biomarkers, and 4 states of hepatitis were tested to see which combinations would result in survival times greater than a year. 2 of the algorithms produced complete models through single trees, which made only them suitable for clinical judgement. A combination of alpha fetoprotein ≤ 210.5 mcg/L, glutamic oxaloacetic transaminase ≤ 1.13 µkat/L, and total bilirubin ≤ 0.0283 mmol/L was shown to be a good predictor of survival > 1 year, and the most effective treatments for such patients were radio-frequency ablation (RFA) and transarterial chemoembolization (TACE) with radiation therapy (RT). In patients without this combination, the best treatments were RFA, TACE with RT and targeted drug therapy, and TACE with targeted drug therapy and immunotherapy. The main limitation of this study was small sample. With small sample size, we may developed a less reliable model system, failing to produce any clinically important results or outcomes Conclusion: Data mining can produce models to help clinicians predict survival time at the time of initial HCC diagnosis and then choose the most suitable treatment.

Publisher

Research Square Platform LLC

Reference32 articles.

1. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries;Sung H;CA: a cancer journal for clinicians,2021

2. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA: A Cancer Journal for Clinicians. 2022;72(1):7–33.

3. Applicability of BCLC stage for prognostic stratification in comparison with other staging systems: single centre experience from long-term clinical outcomes of 1717 treatment-naïve patients with hepatocellular carcinoma;Kim BK;Liver International,2012

4. BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update;Reig M;Journal of Hepatology,2022

5. Comparative analysis of decision tree classification algorithms;Priyam A;International Journal of current engineering and technology,2013

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3