Li Chengzhou, Bao Yanfang, Wang Yanmei, Chen Juan, Yang Rong, Song Qiong
Department of Nuclear Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, ShangHai, China (C.L., Y.B., J.C., R.Y., Q.S.).
GE Healthcare China, Pudong New Town, Shanghai, China (Y.W.).
Acad Radiol. 2025 Feb;32(2):1078-1085. doi: 10.1016/j.acra.2024.09.022. Epub 2024 Sep 20.
Histological subtypes of lung cancers are critical for clinical treatment decision. The aim of this study is to compare the diagnostic performance of multiple radiomics models in differentiating PGL and MIA in pulmonary GGN, in order to identify the most optimal diagnostic model.
Patients presenting with GGNs on lung CT, confirmed as PGL or MIA through surgical pathology between October 2015 and June 2023, were included. The GGNs were randomly divided into training and validation sets at a 7:3 ratio. Clinical imaging characteristics were analyzed by univariate and multivariate logistic regression to identify independent risk factors for predicting MIA, leading to the development of a clinical model. ITK-SNAP and Pyradiomics were employed for segmentation and radiomics feature extraction. Subsequently, radiomics and combined models were established. The diagnostic performance of the three models was compared using ROC curves and quantitatively assessed by AUC, accuracy, specificity, and sensitivity.
A total of 116 cases of GGNs with pathologically confirmed PGLs and MIAs were included. The clinical model identified three independent predictors. The radiomics model identified seven distinct radiomic features. A combined model was constructed by integrating clinical imaging features with radiomic features. In the training set, the combined model demonstrated a higher AUC than the radiomics model, with AUCs of 0.87 and 0.85 respectively. In the validation set, the radiomics model outperformed the combined model with an AUC of 0.83 versus 0.82. Notably, the radiomics model achieved the highest accuracy and specificity, while the combined model demonstrated the highest sensitivity. However, both models performed significantly better than the clinical model.
The independent radiomics model can serve as a rapid, non-invasive diagnostic tool for differentiating between the PGL and MIA.
肺癌的组织学亚型对于临床治疗决策至关重要。本研究旨在比较多种放射组学模型在鉴别肺部磨玻璃结节（GGN）中肺原位腺癌（PGL）和微浸润腺癌（MIA）的诊断性能，以确定最优诊断模型。
纳入2015年10月至2023年6月期间肺部CT表现为GGN且经手术病理确诊为PGL或MIA的患者。GGN按7:3比例随机分为训练集和验证集。通过单因素和多因素逻辑回归分析临床影像特征，以识别预测MIA的独立危险因素，从而构建临床模型。采用ITK-SNAP和Pyradiomics进行分割和放射组学特征提取。随后，建立放射组学模型和联合模型。使用ROC曲线比较三种模型的诊断性能，并通过AUC、准确性、特异性和敏感性进行定量评估。
共纳入116例经病理证实的GGN伴PGL和MIA病例。临床模型确定了三个独立预测因子。放射组学模型识别出七个不同的放射组学特征。通过将临床影像特征与放射组学特征整合构建联合模型。在训练集中，联合模型的AUC高于放射组学模型，分别为0.87和0.85。在验证集中，放射组学模型的AUC为0.83，优于联合模型的0.82。值得注意的是，放射组学模型的准确性和特异性最高，而联合模型的敏感性最高。然而，两种模型的表现均显著优于临床模型。
独立的放射组学模型可作为鉴别PGL和MIA的快速、非侵入性诊断工具。
