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IF 3.8 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology Pub Date : 2024-12-12 DOI:10.1016/j.acra.2024.11.059

Jieke Liu, Yong Li, Yu Long, Yongji Zheng, Junqiang Liang, Wei Lin, Ling Guo, Haomiao Qing, Peng Zhou

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引用次数: 0

摘要

理由和目的：高分级模式、内脏胸膜侵犯、淋巴管侵犯、气隙扩散和淋巴结转移是肺腺癌（LUAD）的高危因素，与不良预后相关。本研究旨在构建并验证一个放射学模型和一个来自低剂量 CT（LDCT）的放射学模型，用于预测实性结节和部分实性结节中的高危 LUAD：本研究回顾性入选了2018年7月至2022年12月来自四个中心的658例病理确诊的LUAD，分为训练集（n=411）、内部验证集（n=139）和外部验证集（n=108）。通过多变量逻辑回归，获得包括最大直径、合并/肿瘤比值（CTR）和语义特征在内的放射学特征和影像学特征，构建放射学模型和影像学模型。利用接收者操作特征曲线下面积（AUC）来评估模型的诊断性能：结果：我们选择了三个放射学特征（GLCM_Correlation、GLSZM_SmallAreaEmphasis 和 GLDM_LargeDependenceHighGrayLevelEmphasis）和四个放射学特征（最大直径、CTR、棘点和胸膜压痕）来建立模型。在内部验证集和外部验证集中，放射学模型的AUC分别为0.916和0.938，明显高于放射学模型的AUC（0.916 vs. 0.868，P=0.014；0.938 vs. 0.880，P=0.002）：我们基于 LDCT 的放射学模型能够无创识别实性结节和部分实性结节中的高危 LUAD，并具有良好的诊断性能，可能有助于肺癌筛查中的病例特异性决策。

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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Predicting High-risk Lung Adenocarcinoma in Solid and Part-solid Nodules on Low-dose CT: A Multicenter Study.

Rationale and objectives: High-grade patterns, visceral pleural invasion, lymphovascular invasion, spread through air spaces, and lymph node metastasis are high-risk factors and associated with poor prognosis in lung adenocarcinomas (LUADs). This study aimed to construct and validate a radiomic model and a radiographic model derived from low-dose CT (LDCT) for predicting high-risk LUADs in solid and part-solid nodules.

Materials and methods: This study retrospectively enrolled 658 pathologically confirmed LUADs from July 2018 to December 2022 from four centers, which were divided into training set (n=411), internal validation set (n=139), and external validation set (n=108). Radiomic features and radiographic features including maximal diameter, consolidation/tumor ratio (CTR), and semantic features, were obtained to construct a radiomic model and a radiographic model through multivariable logistic regression. Area under receiver operating characteristic curve (AUC) was utilized to assess the diagnostic performance of the models.

Results: Three radiomic features (GLCM_Correlation, GLSZM_SmallAreaEmphasis, and GLDM_LargeDependenceHighGrayLevelEmphasis) and four radiographic features (maximal diameter, CTR, spiculation, and pleural indentation) were selected to build models. The radiomic model yielded AUCs of 0.916 in the internal validation set and 0.938 in the external validation set, which were significantly higher than the AUCs of the radiographic model (0.916 vs. 0.868, P=0.014 and 0.938 vs. 0.880, P=0.002).

Conclusion: Our LDCT-based radiomic model enabled non-invasive identification of high-risk LUADs in solid and part-solid nodules with good diagnostic performance and might assist in case-specific decision-making in lung cancer screening.

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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.