Construction of multiclassification joint model to predict pathological classification of pulmonary ground-glass nodules based on radiomics
10.12354/j.issn.1000-8179.2024.20241026
- VernacularTitle:基于影像组学建立多分类联合模型预测肺GGN病理分型
- Author:
Ji KAI
1
;
Yue JUNYAN
;
Liu HAIPENG
;
Sun MENGZHOU
;
Liang XIAOYUN
;
Zhang JING
Author Information
1. 新乡医学院第一附属医院放射科,新乡市肺结节精准诊疗重点实验室(河南省 新乡市 453100)
- Keywords:
pulmonary nodule;
ground-glass nodule(GGN);
radiomics;
pathological classifications;
multiclassification
- From:
Chinese Journal of Clinical Oncology
2024;51(19):1016-1022
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To assess the predictive value of a combined multiclassification model for computed tomography(CT)in the patholo-gical analysis of ground-glass nodules(GGN).Methods:Pulmonary GGN lesions that were pathologically confirmed as invasive adenocar-cinoma(IAC),minimally invasive adenocarcinoma(MIA),adenocarcinoma in situ(AIS),and preinvasive lesions(PILs),were collected from pa-tients who were treated at The First Affiliated Hospital of Xinxiang Medical University between February 2019 and March 2023.A total of 324 nodules were retrospectively collected from 285 patients,and divided into three groups:infiltrating IAC,MIA,and PILs.Radiomics and clinical-CT features were selected through recursive feature elimination and univariate Logistic regression.Seven models were constructed using Logistic regression(LR),support vector machine(SVM),random forest(RF),and integrative learning(stacking).Results:The hybrid model combining clinical-CT-radiomics features and an integrative strategy showed superior predictive performance,with an accuracy of 0.791,precision of 0.788,specificity of 0.857,recall of 0.790,and F1-Score of 0.789.Conclusions:The multiclassification joint model based on CT-radiomics is effective in predicting pathological classification of pulmonary GGNs.This model aids in accurate imaging diagnosis and can provide a basis for optimizing treatment plans.
