Integrating CT Radiomics and Machine Learning for Preoperative Assessment of STAS Grading and Prognostic Stratification in Lung Adenocarcinoma

IF 4.7 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology Pub Date : 2026-10-01 Epub Date: 2026-07-17 DOI:10.1016/j.acra.2026.06.057

Qiuxing Chen MS, Siqi Qu MS, Fang Chen MS, Shulin Li MS, Zhihe Yang MS, Qijia Han MB, Zhu Ai MS, Peng Wang MD, Kun Ma PhD, Zhiming Xiang MD

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引用次数: 0

Abstract

Rationale and Objectives

The distance of spread through air spaces (STAS) dissemination is associated with prognosis in lung adenocarcinoma (LUAD). This study aimed to develop a computed tomography (CT) radiomics-based machine learning model to predict STAS grade.

Materials and Methods

Retrospective data included 293 LUAD patients for training/internal validation and 92 from the National Lung Screening Trial for external validation. STAS, defined as tumor cells beyond the tumor edge, was stratified by a 2500 µm threshold into grade I and II. Cox regression assessed associations between STAS grade and recurrence-free survival (RFS). Radiomics features were selected by minimum redundancy maximum relevance (MRMR), and 11 machine learning classifiers predicted STAS presence and grade. Independent clinic-radiological predictors were identified by univariable and multivariable analyses, and logistic regression combined these with the best radiomics model to build a combined model. Performance was evaluated by area under the curve (AUC) and accuracy (ACC).

Results

STAS grade II was independently associated with poorer RFS (P = 0.037), while grade I was not. For STAS detection, the random forest model achieved AUCs of 0.888, 0.886 and 0.779 with accuracies of 81.5%, 85.2%, and 69.6%; the combined model performed similarly (AUCs 0.877, 0.894, 0.802; accuracies 77.6%, 85.2%, 72.8%). For STAS grading, logistic regression yielded AUCs of 0.845, 0.852, and 0.798 (accuracies 78.2%, 70.4%, 77.1%), with the combined model comparable (AUCs 0.845, 0.855, 0.796; accuracies 78.2%, 70.4%, 77.1%).

Conclusion

STAS grading improves prognostic stratification in LUAD. CT-based radiomics models allow noninvasive prediction of STAS grade, aiding surgical decisions and prognosis.

查看原文本刊更多论文

结合CT放射组学和机器学习在肺腺癌术前评估STAS分级和预后分层。

理由和目的：肺腺癌（LUAD）通过空气间隙（STAS）扩散的距离与预后相关。本研究旨在开发一种基于计算机断层扫描（CT）放射学的机器学习模型来预测STAS分级。材料和方法：回顾性数据包括293例LUAD患者进行培训/内部验证，92例来自国家肺筛查试验进行外部验证。STAS定义为肿瘤边缘以外的肿瘤细胞，以2500µm阈值分为I级和II级。Cox回归评估了STAS分级与无复发生存期（RFS）之间的关系。通过最小冗余最大相关性（MRMR）选择放射组学特征，并使用11个机器学习分类器预测STAS的存在和等级。通过单变量和多变量分析确定独立的临床-放射学预测因子，并将其与最佳放射组学模型结合逻辑回归构建组合模型。用曲线下面积（AUC）和准确度（ACC）评价其性能。结果：STAS II级与较差RFS独立相关（P = 0.037），而I级与较差RFS无独立相关。对于STAS检测，随机森林模型的auc分别为0.888、0.886和0.779，准确率分别为81.5%、85.2%和69.6%；联合模型的表现相似（auc分别为0.877、0.894、0.802；准确率分别为77.6%、85.2%、72.8%）。对于STAS分级，logistic回归的auc分别为0.845、0.852和0.798（准确率分别为78.2%、70.4%和77.1%），与组合模型的auc比较（auc分别为0.845、0.855和0.796，准确率分别为78.2%、70.4%和77.1%）。结论：STAS分级可改善LUAD患者的预后分层。基于ct的放射组学模型允许无创预测STAS分级，帮助手术决策和预后。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Academic Radiology 医学-核医学

CiteScore

7.60

自引率

10.40%

发文量

432

审稿时长

18 days

期刊介绍： Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.