%0 Journal Article
%T 影像组学对肺结节诊断的研究进展
Advances in Radiomics for Pulmonary Nodule Diagnosis
%A 李佳阳
%J Advances in Clinical Medicine
%P 3579-3584
%@ 2161-8720
%D 2025
%I Hans Publishing
%R 10.12677/acm.2025.1541331
%X 影像组学作为新兴的医学影像分析技术，通过高通量特征提取与人工智能算法，显著提升了肺结节诊断的精准性与客观性，成为肺结节早期筛查与诊疗的重要工具。肺结节作为肺癌早期筛查的核心指标，其精准诊断直接影像临床决策与患者预后。传统依赖医师经验的影像学评估和具有侵入性的病理活检存在诊断效能瓶颈。影像组学通过提取肺结节的影像学特征，可以提高对肺结节诊断的准确性和可视性。本文综述了影像组学对肺结节诊断的研究进展，包括肺结节良恶性及浸润性的诊断，影像组学复合模型对肺结节的预测，探讨了增强CT影像组学的应用前景。
As an emerging medical imaging analysis technology, radiomics has significantly improved the accuracy and objectivity of pulmonary nodule diagnosis through high-throughput feature extraction and artificial intelligence algorithms, becoming an important tool for early screening and diagnosis of pulmonary nodules. As a core indicator for early screening of lung cancer, pulmonary nodules provide accurate diagnosis that directly affects imaging clinical decision-making and patient prognosis. Traditional imaging evaluations that rely on physician experience and invasive pathological biopsies have diagnostic efficacy bottlenecks. Imaging omics can improve the accuracy and visibility of pulmonary nodule diagnosis by extracting imaging features of pulmonary nodules. This article reviews the research progress of radiomics in the diagnosis of pulmonary nodules, including the diagnosis of benign, malignant, and invasive pulmonary nodules, the prediction of pulmonary nodules by radiomics composite models, and explores the application prospects of enhanced CT radiomics.
%K 影像组学，
%K 肺结节，
%K 诊断，
%K 预后
Radiomics
%K Pulmonary Nodules
%K Diagnosis
%K Prognosis
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=113534