Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
Beijing Deepwise & League of PhD Technology Co. Ltd, Beijing, China.
Eur Radiol Exp. 2024 Jan 17;8(1):8. doi: 10.1186/s41747-023-00400-6.
We aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs).
A total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC).
Six CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918-0.966) in the training group and an AUC of 0.930 (0.902-0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value.
The combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs.
The model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work.
• We developed a pulmonary nodule diagnostic model including radiomics and CT features. • The model yielded the best performance in differentiating malignant from benign nodules. • The combined model had clinical application value and excellent discriminative ability. • The model can assist radiologists in diagnosing malignant from benign pulmonary nodules.
本研究旨在建立一种基于放射组学和计算机断层扫描（CT）成像特征的综合模型，用于辅助诊断亚厘米（≤10mm）实性肺结节（SSPN）的良恶性。
回顾性分析了 2016 年 5 月至 2022 年 6 月期间 324 例 SSPN 患者的临床资料。其中恶性结节（n=158）经病理证实，良性结节（n=166）经随访或病理证实。将 SSPN 分为训练集（n=226）和测试集（n=98）。从增强 CT 中提取了 2107 个放射组学特征。通过单因素和多因素逻辑回归分析保留的临床和 CT 特征，用于建立临床模型。使用逻辑回归将放射组学特征与 CT 成像特征相结合建立综合模型。采用受试者工作特征曲线（ROC）下面积（AUC）评估各模型的性能。
有 6 个 CT 成像特征是 SSPN 的独立预测因子，降维后选择了 4 个放射组学特征。通过逻辑回归方法构建的综合模型在区分良恶性 SSPN 方面具有最佳性能，在训练组的 AUC 为 0.942（95%置信区间 0.918-0.966），在测试组的 AUC 为 0.930（0.902-0.957）。决策曲线分析表明，该综合模型具有临床应用价值。
纳入放射组学和 CT 成像特征的综合模型具有良好的鉴别能力，有望帮助放射科医生诊断良恶性 SSPN。
该模型结合了放射组学和临床特征，在预测 SSPN 的良恶性方面具有较好的效率，有望辅助临床早期诊断肺癌，并改善临床工作中的随访策略。
我们建立了一个包含放射组学和 CT 特征的肺结节诊断模型。
该模型在区分良恶性结节方面表现最佳。
综合模型具有临床应用价值和良好的判别能力。
该模型可以帮助放射科医生诊断良恶性肺结节。
Zotero 插件
