Prediction of spread through air spaces in lung adenocarcinoma based on CT radiomics and comparison of different peritumoral expansion regions
10.12354/j.issn.1000-8179.2025.20250388
- VernacularTitle:基于CT影像组学的肺腺癌气腔播散预测与不同瘤周扩展区域的比较
- Author:
Ma ZHENGXIAO
1
;
Zhuo YUE
;
Huang CHAO
;
Shi LEI
;
Bao ZHEN
;
Su DAN
Author Information
1. 温州医科大学研究生培养基地(浙江省肿瘤医院)(杭州市 310022);浙江省肿瘤医院病理科
- Publication Type:Journal Article
- Keywords:
lung adenocarcinoma(LUAD);
spread through air spaces(STAS);
radiomics;
machine learning
- From:
Chinese Journal of Clinical Oncology
2025;52(8):392-400
- CountryChina
- Language:Chinese
-
Abstract:
Objective:This study aimed to evaluate the value of CT-based radiomics machine learning models in predicting spread through air spaces(STAS)in lung adenocarcinoma(LUAD)and to determine the optimal peritumoral analysis region.Methods:Data from 378 pa-tients who underwent non-small cell lung cancer surgery at Zhejiang Cancer Hospital between January 2013 to January 2017 were retro-spectively analyzed.Logistic regression,random forest,and XGBoost models were constructed using regions extending 0,3,6,9,and 12 mm outward from the tumor margin.Results:The XGBoost model using the 6 mm peritumoral region performed best on the test set,with an AUC-ROC of 0.855(95%CI:0.756-0.950),followed by the XGBoost model using the 9 mm region.Decision curve analysis(DCA)indicated that the XGBoost models for the 6 mm and 9 mm regions had higher net clinical benefits.Feature analysis revealed that some wavelet trans-form features significantly contributed to STAS prediction.Conclusions:This preliminary study suggests that CT-based radiomics machine learning models have predictive value for STAS.The XGBoost model based on the 6 mm peritumoral region demonstrated the best perform-ance,and holds promise in assisting preoperative assessment.
