|Table of Contents|

Clinical study of predicting overall survival of patients with retroperitoneal liposarcoma based on machine learning algorithms

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:
2023 07
Page:
1291-1296
Research Field:
Publishing date:

Info

Title:
Clinical study of predicting overall survival of patients with retroperitoneal liposarcoma based on machine learning algorithms
Author(s):
WANG Peng1; DING Xiaoling2; XIE Mingjie1; WANG Xingchao1; ZHU Donghui1; YU Tiannan3; CHEN Erlin1
1.General Surgery;2.Department of Gastroenterology;3.Urology Department,Affiliated Hospital of Nantong University，Jiangsu Nantong 226001,China.
Keywords:
retroperitoneal liposarcoma; machine learning; predict; overall survival
PACS:
R730.262
DOI:
10.3969/j.issn.1672-4992.2023.07.021
Abstract:
Objective:To compare the prediction effects of different types of machine learning algorithms on the overall survival of retroperitoneal liposarcoma(RP-LPS) patients,and choose the best algorithm to guide clinical diagnosis and treatment.Methods:The cases(2 147 cases) diagnosed with retroperitoneal liposarcoma from 2000 to 2019 were collected by SEER*Stat software in the United States as a training set for research.We selected patients(55 cases) with retroperitoneal liposarcoma diagnosed in Nantong University Affiliated Hospital from 2014 to 2019 as an external validation set,using machine learning algorithms including support vector machine,adaptive boosting,decision tree,random forest,and neural network.The prediction performance of these algorithms in the training set and the validation set was compared respectively.Results:By comparing the prediction performance indicators of each machine learning algorithm,including accuracy,sensitivity,AUC,F1 score,etc.,it was concluded that the prediction effect of the adaptive improvement algorithm was the best,in the training set,the accuracy rate was 69.1%,the sensitivity was 76.5% and AUC was 0.70.In the external validation set,the accuracy was 74.5%,the sensitivity was 72.0% and AUC was 0.74.Compared with the traditional TNM model,it also shows better prediction performance.Conclusion:The machine learning algorithm provides more accurate and personalized prognostic information about retroperitoneal liposarcoma than traditional prediction models,which can be used to assist doctors in judging the prognosis and treatment effect of patients,and formulate a personalized diagnosis and treatment plans.

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Memo

Memo:
National Natural Science Foundation of China（No.82172931）;国家自然科学基金资助项目(编号:82172931)
Last Update: 2023-02-28