Dai Xinpeng, Lu Haiyong, Wang Xinying, Zhao Bingxin, Liu Zongjie, Sun Tao, Gao Feng, Xie Peng, Yu Hong, Sui Xin
Third Hospital of Hebei Medical University, Shijiazhuang, China.
First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China.
Front Oncol. 2024 Nov 12;14:1443029. doi: 10.3389/fonc.2024.1443029. eCollection 2024.
The aim of this study is to develop an ultrasound-based fusion model of clinical, radiomics and deep learning (CRDL) for accurate diagnosis of benign and malignant soft tissue tumors (STTs).
In this retrospective study, ultrasound images and clinical data of patients with STTs from two hospitals were collected between January 2021 and December 2023. Radiomics features and deep learning features were extracted from the ultrasound images, and the optimal features were selected to construct fusion models using support vector machines. The predictive performance of the model was evaluated based on three aspects: discrimination, calibration and clinical usefulness. The DeLong test was used to compare whether there was a significant difference in AUC between the models. Finally, two radiologists who were unaware of the clinical information performed an independent diagnosis and a model-assisted diagnosis of the tumor to compare the performance of the two diagnoses.
A training cohort of 516 patients from Hospital-1 and an external validation cohort of 78 patients from Hospital-2 were included in the study. The Pre-FM CRDL showed the best performance in predicting STTs, with area under the curve (AUC) of 0.911 (95%CI: 0.894-0.928) and 0.948 (95%CI: 0.906-0.990) for training cohort and external validation cohort, respectively. The DeLong test showed that the Pre-FM CRDL significantly outperformed the clinical models (P< 0.05). In addition, the Pre-FM CRDL can improve the diagnostic accuracy of radiologists.
This study demonstrates the high clinical applicability of the fusion model in the differential diagnosis of STTs.
本研究旨在开发一种基于超声的临床、影像组学和深度学习（CRDL）融合模型，用于准确诊断良性和恶性软组织肿瘤（STT）。
在这项回顾性研究中，收集了2021年1月至2023年12月期间来自两家医院的STT患者的超声图像和临床数据。从超声图像中提取影像组学特征和深度学习特征，并选择最佳特征使用支持向量机构建融合模型。基于三个方面评估模型的预测性能：辨别力、校准和临床实用性。使用DeLong检验比较模型之间的AUC是否存在显著差异。最后，两名不了解临床信息的放射科医生对肿瘤进行独立诊断和模型辅助诊断，以比较两种诊断的性能。
本研究纳入了来自医院1的516例患者的训练队列和来自医院2的78例患者的外部验证队列。预融合模型CRDL在预测STT方面表现最佳，训练队列和外部验证队列的曲线下面积（AUC）分别为0.911（95%CI：0.894-0.928）和0.948（95%CI：0.906-0.990）。DeLong检验表明，预融合模型CRDL显著优于临床模型（P<0.05）。此外，预融合模型CRDL可以提高放射科医生的诊断准确性。
本研究证明了融合模型在STT鉴别诊断中的高临床适用性。
