现代仪器与医疗

基于机器学习对亚实性IA期肺腺癌气道内播散的预测价值

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刘欢欢 ^1a , 陈业 ^1b , 周露 ² , 齐宏亮 ^1b^,^*

现代仪器与医疗 | 医学工程 2025,31(1): 37-42

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现代仪器与医疗 |医学工程 2025 , 31 (1) : 37 -42

基于机器学习对亚实性IA期肺腺癌气道内播散的预测价值

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刘欢欢^1a, 陈业^1b, 周露², 齐宏亮^1b^,^*

作者信息

^1a.南方医科大学南方医院　放疗中心，广州　510515

^1b.南方医科大学南方医院　医学工程科，广州　510515

^2.湘潭市中心医院放射科，湘潭　411100

通讯作者:

齐宏亮，男，博士研究生，高级工程师，研究方向：影像组学

作者简介:

刘欢欢，女，本科，主管护师，研究方向：放疗CT定位

Applications of machine learning models in the prediction of tumor spread through air spaces in patients with stage IA part-solid lung adenocarcinoma

Huanhuan Liu^1a, Ye Chen^1b, Lu Zhou², Hongliang Qi^1b^,^*

Affiliations

^1a.Department of Radiotherapy, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China

^1b.Department of Medical Engineering, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China

^2.Department of Radiology, Xiangtan Central Hospital, Xiangtan 411100, China

出版时间: 2025-02-10 doi: 10.11876/mimt202501007

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摘要

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目的

探讨IA期亚实性肺腺癌中气道内播散（Spread Through Air Spaces，STAS）的临床特征及CT影像表现，并基于机器学习算法构建预测模型。

方法

本研究对2021年1月—2023年6月期间在湘潭市中心医院收治的IA期亚实性肺腺癌患者进行了回顾性分析。根据术后的病理结果，将患者分为STAS（+）组和STAS（-）组。使用包括Logistic回归、支持向量机（Support Vector Machine，SVM）、K近邻（K-Nearest Neighbour，KNN）、极端梯度提升（Extreme Gradient Boosting，Xgboost）、最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator，LASSO）、随机森林（Random Forest，RF）在内的五种机器学习算法来构建IA期亚实性肺腺癌的STAS预测模型。

结果

在106例IA期亚实性肺腺癌患者中，有16例（15%）被诊断为STAS。在对五种机器学习算法进行比较后，Xgboost在诊断准确性方面表现最佳，曲线下面积：0.969（95% CI：0.915～0.993）。Xgboost算法确定了IA期亚实性肺腺癌中STAS的以下关键预测因子，包括实性成分比例更高、实性部分的CT值、病灶大小、周围磨玻璃影（Ground-Glass Opacity，GGO）边界的模糊程度以及实性成分体积。

结论

通过分析患者的临床及影像学数据，Xgboost能够有效预测IA期亚实性肺腺癌的STAS，有利于临床决策。

关键词

IA期亚实性肺腺癌 / 气道内播散 / 机器学习 / 预测模型

Abstract

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Objective

This study aims to explore the clinical features and CT imaging manifestations of spread through air spaces (STAS) in stage IA subsolid lung adenocarcinoma and to construct a predictive model based on machine learning algorithms.

Methods

We retrospectively analyzed patients with stage IA subsolid lung adenocarcinoma admitted to Xiangtan Central Hospital from January 2021 to June 2023. Patients were classified into STAS(+) and STAS(-) groups based on postoperative pathological results. We employed five machine learning algorithms—Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (Xgboost), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF)—to develop a predictive model for STAS in stage IA subsolid lung adenocarcinoma.

Results

Among the 106 patients with stage IA subsolid lung adenocarcinoma, 16 (15%) were diagnosed with STAS. After comparing the five machine learning algorithms, Xgboost demonstrated the highest diagnostic accuracy, achieving an area under the curve (AUC) of 0.969 (95% CI: 0.915-0.993). The Xgboost algorithm identified several key predictors of STAS in stage IA subsolid lung adenocarcinoma: higher proportion of solid component, CT value of the solid component, lesion size, degree of blurring in the surrounding ground-glass opacity (GGO) border, and volume of the solid component.

Conclusion

Through the analysis of clinical and imaging data, the Xgboost algorithm can effectively predict STAS in stage IA subsolid lung adenocarcinoma, providing valuable insights for clinical decision-making.

Key words

Stage IA subsolid lung adenocarcinoma / Spread through air spaces / Machine learning / Predictive model

引用本文

刘欢欢, 陈业, 周露, 齐宏亮. 基于机器学习对亚实性IA期肺腺癌气道内播散的预测价值. 现代仪器与医疗, 2025 , 31 (1) : 37 -42 . DOI: 10.11876/mimt202501007

Huanhuan Liu, Ye Chen, Lu Zhou, Hongliang Qi. Applications of machine learning models in the prediction of tumor spread through air spaces in patients with stage IA part-solid lung adenocarcinoma[J]. Modern Instruments & Medical Treatment, 2025 , 31 (1) : 37 -42 . DOI: 10.11876/mimt202501007

基金

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南方医科大学南方医院院长基金项目(2022B030)

参考文献

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参考文献引证文献

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2025年第31卷第1期

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文章信息

doi: 10.11876/mimt202501007

首发时间：2026-10-23
出版时间：2025-02-10

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基金

南方医科大学南方医院院长基金项目(2022B030)

作者信息

^1a.南方医科大学南方医院　放疗中心，广州　510515

^1b.南方医科大学南方医院　医学工程科，广州　510515

^2.湘潭市中心医院放射科，湘潭　411100

通讯作者:

齐宏亮，男，博士研究生，高级工程师，研究方向：影像组学

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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