In:
Combinatorial Chemistry & High Throughput Screening, Bentham Science Publishers Ltd., Vol. 26 ( 2023-08-24)
Abstract:
There is a lack of a reliable outcome prediction model for patients evaluating the feasibility of postoperative adjuvant transarterial chemoembolization (PA-TACE) therapy. Our goal was to develop an easy-to-use tool specifically for these patients. Methods: From January 2013 to June 2017, patients with hepatocellular carcinoma from the Liver Center of the First Affiliated Hospital of Chongqing Medical University received postoperative adjuvant Transarterial chemoembolization (TACE) therapy after liver cancer resection. A Cox proportional hazards model was established for these patients, followed by internal validation (enhanced bootstrap resampling technique) to further evaluate the predictive performance and discriminanceevaluate the predictive performance and discriminance, and compare it with other predictive models. The prognostic factors considered included tumour number, maximum tumor diameter, Edmondson-Steiner (ES) grade, Microvascular invasion (MVI) grade, Ki67, age, sex, hepatitis B surface antigen, cirrhosis, Alpha-fetoprotein(AFP), Albumin-bilirubin (ALBI) grade, Child-pugh grade, body mass index (BMI), Neutrophil-lymphocyte ratio (NLR), Platelet-to-lymphocyte ratio (PLR). Results: The endpoint of the study was overall survival. The median overall survival was 36 (95%CI: 34.0-38.0 ) months, with 1-year, 2-year and 3-year survival rates being 96.3%, 84.0% and 75.3%, respectively. Tumour number, MVI grade, and BMI was incorporated into the model, which had good differentiation and accuracy. Internal validation (enhanced bootstrap ) suggested that Harrell’s C statistic is 0.72. The model consistently outperforms other currently available models. Conclusion: This model may be an easy-to-use tool for screening patients suitable for PA-TACE treatment and guiding the selection of clinical protocols. But further research and external validation are required.
Type of Medium:
Online Resource
ISSN:
1386-2073
DOI:
10.2174/1386207326666230824090204
Language:
English
Publisher:
Bentham Science Publishers Ltd.
Publication Date:
2023
SSG:
15,3
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