Yang Miaomiao, Zhang Xiuming, Jin Jiyang
Department of Radiology, Southeast University Zhongda Hospital, No. 87 Dingjiaqiao Road, Gulou District, Nanjing, Jiangsu Province, China (M.Y., J.J.).
Department of Radiology, Jiangsu Cancer Hospital, Nanjing, Jiangsu Province, China (X.Z.).
Acad Radiol. 2025 May;32(5):2838-2846. doi: 10.1016/j.acra.2024.12.026. Epub 2025 Jan 2.
To develop radiomics and deep learning models for differentiating malignant and benign soft tissue tumors (STTs) preoperatively based on fat saturation T2-weighted imaging (FS-T2WI) of patients.
Data of 115 patients with STTs of extremities and trunk were collected from our hospital as the training set, and data of other 70 patients were collected from another center as the external validation set. Outlined Regions of interest included the intratumor and the peritumor region extending outward by 5 mm, then the corresponding radiomics features were extracted respectively. Deep learning was performed using pretrained 3D ResNet algorithms, and deep learning features were extracted from the entire FS-T2WI of patients. Recursive feature elimination and least absolute shrinkage and selection operator were used to select the radiomics and deep learning features with predictive value. Five machine learning algorithms were applied to build radiomics models, the area under the ROC curve (AUC) in the validation set were used to evaluate the diagnostic performance, and decision curve analysis (DCA) was used to evaluate the clinical benefit of models.
Based on 20 selected deep learning and radiomics features, the deep learning radiomics (DLR) model had the best predictive performance in the validation set, with an AUC of 0.9410. DCA and calibration curves showed that the DLR model had better clinical net benefit and goodness of fit.
By extracting more features from FS-T2WI, the DLR model is a noninvasive, low-cost, and highly accurate preoperative differential diagnosis of benign and malignant STTs.
基于患者的脂肪饱和T2加权成像（FS-T2WI），开发用于术前鉴别恶性和良性软组织肿瘤（STT）的放射组学和深度学习模型。
收集我院115例四肢和躯干STT患者的数据作为训练集，从另一个中心收集另外70例患者的数据作为外部验证集。勾勒出的感兴趣区域包括肿瘤内以及向外延伸5毫米的肿瘤周围区域，然后分别提取相应的放射组学特征。使用预训练的3D ResNet算法进行深度学习，并从患者的整个FS-T2WI中提取深度学习特征。采用递归特征消除和最小绝对收缩与选择算子来选择具有预测价值的放射组学和深度学习特征。应用五种机器学习算法构建放射组学模型，使用验证集中的ROC曲线下面积（AUC）评估诊断性能，并使用决策曲线分析（DCA）评估模型的临床获益。
基于20个选定的深度学习和放射组学特征，深度学习放射组学（DLR）模型在验证集中具有最佳预测性能，AUC为0.9410。DCA和校准曲线表明DLR模型具有更好的临床净获益和拟合优度。
通过从FS-T2WI中提取更多特征，DLR模型是一种无创、低成本且高度准确的术前鉴别诊断良性和恶性STT的方法。
