Prediction of CT Radiomics in the Short-Term Efficacy of Gefitinib in Advanced Lung Adenocarcinoma
10.3969/j.issn.1005-5185.2024.11.005
- VernacularTitle:CT影像组学对晚期肺腺癌吉非替尼近期疗效的预测价值
- Author:
Yan WANG
1
;
Shufang ZHAO
;
Wenyu LI
;
Fengying JI
Author Information
1. 西安市第九医院医学影像中心,陕西 西安 710000
- Keywords:
Lung neoplasms;
Adenocarcinoma;
Radiomics;
Tomography,X-ray computed;
Gefitinib;
Treatment outcome;
Forecasting
- From:
Chinese Journal of Medical Imaging
2024;32(11):1118-1122,1133
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To evaluate the short-term efficacy of Gefitinib based on the non-contrast CT radiomics model in patients with advanced lung adenocarcinoma before treatment.Materials and Methods Seventy-one patients with advanced lung adenocarcinoma and treated with Gefitinib in the First Affiliated Hospital of Harbin Medical University from January 2020 to May 2022 were retrospectively analyzed,the efficacy of the treatment was evaluated in the third month after receiving the treatment,and all patients were divided into the treatment-effective group(n=41)and the treatment-ineffective group(n=30)according to the criteria for evaluating the efficacy of solid tumors.All patients were randomized into the training group and the validation group according to 7∶3,the least absolute shrinkage and selection operator were used in the training group to screen the best radiomics features,and eight models were established,including K-proximity,Logistic regression,support vector machine,adaptive enhancement,gradient boosting,random forest,ensemble algorithm and Gaussian naive Bayes.The optimal model was selected by comparing the area under the receiver operating characteristic curve(AUC)and accuracy.The optimal model performance was tested in the validation group,the clinical usability of the best model was tested using decision curves,the accuracy of the predictive models was visualized using calibration curves.Results The seven best radiomics features were obtained by least absolute shrinkage and selection operator regression screening,the best model Logistic regression was selected in a variety of models,the AUC value of Logistic regression in the validation group was 0.774(95%CI 0.536-0.951),with a sensitivity of 0.846,a specificity of 0.556,an accuracy of 0.727,a recall of 0.846,and an F1-score of 0.733.Conclusion It is possible to screen out the sensitive population of advanced lung adenocarcinoma for targeted therapy based on CT radiomics models.
