|Table of Contents|

Feasibility of predicting the efficacy of radiotherapy and chemotherapy for non-small cell lung cancer based on radiomics and artificial intelligence

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

Issue:
2022 06
Page:
1079-1084
Research Field:
Publishing date:

Info

Title:
Feasibility of predicting the efficacy of radiotherapy and chemotherapy for non-small cell lung cancer based on radiomics and artificial intelligence
Author(s):
LIU Yafeng1; WU Jing1; 2; ZHOU Jiawei1; XING Yingru3; XIE Jun3; DING Xuansheng1; 4; HU Dong1; 2
1.Anhui University of Science and Technology Medical College,Anhui Huainan 232001,China;2.Key Laboratory of Industrial Dust Prevention and Control and Occupational Safety and Health,Ministry of Education,Anhui University of Science and Technology School of Medicine,Anhui Huainan 232001,China;3.Cancer Hospital Affiliated to Anhui University of Science and Technology,Anhui Huainan 232001,China;4.School of Pharmacy,China Pharmaceutical University,Jiangsu Nanjing 210009,China.
Keywords:
lung cancer; imaging omics; artificial intelligence; therapeutic response
PACS:
R734.2
DOI:
10.3969/j.issn.1672-4992.2022.06.027
Abstract:
Objective:According to the radiomic features,a prediction model was established and verified to predict the possibility of partial response(PR) of non-small cell lung cancer(NSCLC) patients after receiving sequential chemoradiotherapy(SCRT) or concurrent chemoradiotherapy(CCRT).Methods:Patients diagnosed with NSCLC and receiving SCRT or CCRT from January 2016 to June 2020 were retrospectively collected.Eligible patients were included in this study and randomly divided into a training group and a validation group.The one-way analysis of variance and the LEAST Absolute Shrinkage and Selection Operator(LASSO) algorithm were used to screen the optimal radiographic features in the training set.Machine learning(Logistic egression,LR.Decision tree,DT.AdaBoost) model building.The area under the receiver operating characteristic curve curve(AUC),sensitivity and specificity were used to evaluate the model performance,the nomogram was used to visualize the model,and the decision curve analysis(DCA) method was used to test the application efficiency of the model.Results:A total of 75 patients were included and randomly divided into two groups,52 in the training group and 23 in the validation group.Six radiomics features were selected after univariate analysis of variance and LASSO regression analysis,and predictive models were constructed using machine learning classifier.The model AUCs for LR,DT and AdaBoost were 0.919,0.773 and 0.832 in the training group and 0.795,0.723 and 0.638 in the validation group.The use of LR models to construct decision curves suggested that a risk threshold of 0.1~0.92 increased the net benefit for the patient.Conclusion:This study developed and verified an radiomics model,which can predict the remission probability of NSCLC patients after SCRT/CCRT.

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Memo

Memo:
National Natural Science Foundation of China(No.81971483）；国家自然科学基金资助项目(编号：81971483)；安徽省高校协同创新项目(编号：GXXT-2020-058)；安徽理工大学研究生创新基金项目(编号：2020CX2084)
Last Update: 1900-01-01