Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China.
Department of Radiology, The Second Hospital of Dalian Medical University, Dalian, Liaoning Province, China.
PLoS One. 2024 Oct 4;19(10):e0309033. doi: 10.1371/journal.pone.0309033. eCollection 2024.
To develop a better radiomic model for the differential diagnosis of benign and lung adenocarcinoma lesions presenting as larger solid nodules and masses based on multiscale computed tomography (CT) radiomics.
This retrospective study enrolled 205 patients with solid nodules and masses from Center 1 between January 2010 and February 2022 and Center 2 between January 2019 and February 2022. After applying the inclusion and exclusion criteria, we retrospectively enrolled 165 patients from two centers and assigned them to the training dataset (n = 115) or the test dataset (n = 50). Radiomics features were extracted from volumes of interest on CT images. A gradient boosting decision tree (GBDT) was used for data dimensionality reduction to perform the final feature selection. Four models were developed using clinical data, conventional imaging features and radiomics features, namely, the clinical and image model (CIM), the plain CT radiomics model (PRM), the enhanced CT radiomics model (ERM) and the combined model (CM). Model performance was evaluated to determine the best model for identifying benign and lung adenocarcinoma presenting as larger solid nodules and masses.
In the training dataset, the areas under the curve (AUCs) for the CIM, PRM, ERM, and CM were 0.718, 0.806, 0.819, and 0.917, respectively. The differential diagnostic capability of the ERM was better than that of the PRM and the CIM. The CM was optimal. Intermediate and junior radiologists and respiratory physicians achieved improved obviously diagnostic results with the radiomics model. The senior radiologists showed slight improved diagnostic results after using the radiomics model.
Radiomics may have the potential to be used as a noninvasive tool for the differential diagnosis of benign and lung adenocarcinoma lesions presenting as larger solid nodules and masses.
基于多尺度 CT 放射组学，开发一种更好的用于鉴别表现为较大实性结节和肿块的良性和肺腺癌病变的放射组学模型。
本回顾性研究纳入了 205 例来自中心 1（2010 年 1 月至 2022 年 2 月）和中心 2（2019 年 1 月至 2022 年 2 月）的实性结节和肿块患者。应用纳入和排除标准后，我们从两个中心回顾性纳入了 165 例患者，将其分为训练数据集（n=115）或测试数据集（n=50）。从 CT 图像的感兴趣区域提取放射组学特征。梯度提升决策树（GBDT）用于数据降维，以进行最终的特征选择。使用临床数据、常规成像特征和放射组学特征构建了四个模型，分别为临床和影像模型（CIM）、平扫 CT 放射组学模型（PRM）、增强 CT 放射组学模型（ERM）和联合模型（CM）。评估模型性能，以确定用于识别表现为较大实性结节和肿块的良性和肺腺癌的最佳模型。
在训练数据集中，CIM、PRM、ERM 和 CM 的曲线下面积（AUCs）分别为 0.718、0.806、0.819 和 0.917。ERM 的鉴别诊断能力优于 PRM 和 CIM。CM 是最优的。中级和初级放射科医生和呼吸内科医生使用放射组学模型后，诊断结果明显改善。使用放射组学模型后，高级放射科医生的诊断结果略有改善。
放射组学可能有潜力成为用于鉴别表现为较大实性结节和肿块的良性和肺腺癌病变的一种非侵入性工具。
