Zhang Guojin, Shang Lan, Li Shenglin, Zhang Jing, Zhang Zhuoli, Zhang Xin, Qian Rong, Yang Ke, Li Xin, Liu Yiming, Wu Ying, Pu Hong, Cao Yuntai, Man Qiong, Kong Weifang
Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.
Eur Radiol. 2025 Jun 11. doi: 10.1007/s00330-025-11721-y.
To develop and validate a deep learning model based on three-dimensional features (DL_3D) for distinguishing lung adenocarcinoma (LUAD) from tuberculoma (TBM).
A total of 1160 patients were collected from three hospitals. A vision transformer network-based DL_3D model was trained, and its performance in differentiating LUAD from TBM was evaluated using validation and external test sets. The performance of the DL_3D model was compared with that of two-dimensional features (DL_2D), radiomics, and six radiologists. Diagnostic performance was assessed using the area under the receiver operating characteristic curves (AUCs) analysis.
The study included 840 patients in the training set (mean age, 54.8 years [range, 19-86 years]; 514 men), 210 patients in the validation set (mean age, 54.3 years [range, 18-86 years]; 128 men), and 110 patients in the external test set (mean age, 54.7 years [range, 22-88 years]; 51 men). In both the validation and external test sets, DL_3D exhibited excellent diagnostic performance (AUCs, 0.895 and 0.913, respectively). In the test set, the DL_3D model showed better performance (AUC, 0.913; 95% CI: 0.854, 0.973) than the DL_2D (AUC, 0.804, 95% CI: 0.722, 0.886; p < 0.001), radiomics (AUC, 0.676, 95% CI: 0.574, 0.777; p < 0.001), and six radiologists (AUCs, 0.692 to 0.810; p value range < 0.001-0.035).
The DL_3D model outperforms expert radiologists in distinguishing LUAD from TBM.
Question Can a deep learning model perform in differentiating LUAD from TBM on non-enhanced CT images? Findings The DL_3D model demonstrated higher diagnostic performance than the DL_2D model, radiomics model, and six radiologists in differentiating LUAD and TBM. Clinical relevance The DL_3D model could accurately differentiate between LUAD and TBM, which can help clinicians make personalized treatment plans.
开发并验证一种基于三维特征的深度学习模型（DL_3D），用于区分肺腺癌（LUAD）和结核瘤（TBM）。
从三家医院共收集了1160例患者。训练了基于视觉Transformer网络的DL_3D模型，并使用验证集和外部测试集评估其在区分LUAD和TBM方面的性能。将DL_3D模型的性能与二维特征（DL_2D）、影像组学以及六位放射科医生的性能进行比较。使用受试者工作特征曲线下面积（AUC）分析评估诊断性能。
研究包括训练集中的840例患者（平均年龄54.8岁[范围19 - 86岁]；男性514例）、验证集中的210例患者（平均年龄54.3岁[范围18 - 86岁]；男性128例）和外部测试集中的110例患者（平均年龄54.7岁[范围22 - 88岁]；男性51例）。在验证集和外部测试集中，DL_3D均表现出优异的诊断性能（AUC分别为0.895和0.913）。在测试集中，DL_3D模型的表现（AUC为0.913；95%CI：0.854，0.973）优于DL_2D（AUC为0.804，95%CI：0.722，0.886；p < 0.001）、影像组学（AUC为0.676，95%CI：0.574，0.777；p < 0.001）以及六位放射科医生（AUC为0.692至0.810；p值范围< 0.001 - 0.035）。
在区分LUAD和TBM方面，DL_3D模型的表现优于放射科专家。
问题 深度学习模型能否在非增强CT图像上区分LUAD和TBM？ 研究结果 在区分LUAD和TBM方面，DL_3D模型的诊断性能高于DL_2D模型、影像组学模型和六位放射科医生。 临床意义 DL_3D模型能够准确区分LUAD和TBM，有助于临床医生制定个性化治疗方案。
