Dai Xinpeng, Lu Haiyong, Wang Xinying, Liu Yujia, Zang Jiangnan, Liu Zongjie, Sun Tao, Gao Feng, Sui Xin
Department of Ultrasound, Hebei Medical University Third Hospital, Shijiazhuang, Hebei province, China (X.D., X.W., Y.L., Z.L., X.S.).
Department of Ultrasound, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China (H.L.).
Acad Radiol. 2025 Mar;32(3):1178-1188. doi: 10.1016/j.acra.2024.09.067. Epub 2024 Oct 15.
To investigate the value of deep learning (DL) combined with radiomics and clinical and imaging features in predicting the Ki-67 proliferation index of soft tissue tumors (STTs).
In this retrospective study, a total of 394 patients with STTs admitted from January 2021 to December 2023 in two separate hospitals were collected. Hospital-1 was the training cohort (323 cases, of which 89 and 234 were high and low Ki-67, respectively) and Hospital-2 was the external validation cohort (71 cases, of which 23 and 48 were high and low Ki-67, respectively). Clinical and ultrasound characteristics including age, sex, tumor size, morphology, margins, internal echoes and blood flow were assessed. Risk factors with significant correlations were screened by univariate and multivariate logistic regression analyses. After extracting the radiomics and DL features, the feature fusion model is constructed by Support Vector Machine. The prediction results obtained from separate clinical features, radiomics features and DL features were combined to construct decision fusion models. Finally, the DeLong test was used to compare whether the AUCs between the models were significantly different.
The three feature fusion models and three decision fusion models constructed demonstrated excellent diagnostic performance in predicting Ki-67 expression levels in STTs. Among them, the feature fusion model based on clinical, radiomics, and DL performed the best with an AUC of 0.911 (95% CI: 0.886-0.935) in the training cohort and 0.923 (95% CI: 0.873-0.972) in the validation cohort, and proved to be well-calibrated and clinically useful. The DeLong test showed that the decision fusion models based on clinical, radiomics and DL performed significantly worse than the three feature fusion models on the validation set. There was no statistical difference in diagnostic performance between the other models.
The ultrasound-based fusion model of clinical, radiomics, and DL features showed good performance in predicting Ki-67 expression levels in STTs.
探讨深度学习（DL）联合放射组学以及临床和影像特征在预测软组织肿瘤（STT）的Ki-67增殖指数中的价值。
在这项回顾性研究中，收集了2021年1月至2023年12月期间在两家不同医院收治的394例STT患者。医院1为训练队列（323例，其中高Ki-67和低Ki-67分别为89例和234例），医院2为外部验证队列（71例，其中高Ki-67和低Ki-67分别为23例和48例）。评估了包括年龄、性别、肿瘤大小、形态、边界、内部回声和血流等临床和超声特征。通过单因素和多因素逻辑回归分析筛选出具有显著相关性的危险因素。提取放射组学和DL特征后，采用支持向量机构建特征融合模型。将从单独的临床特征、放射组学特征和DL特征获得的预测结果进行组合，构建决策融合模型。最后，使用DeLong检验比较模型之间的AUC是否存在显著差异。
构建的三种特征融合模型和三种决策融合模型在预测STT的Ki-67表达水平方面表现出优异的诊断性能。其中，基于临床、放射组学和DL的特征融合模型表现最佳，在训练队列中的AUC为0.911（95%CI：0.886-0.935），在验证队列中的AUC为0.923（95%CI：0.873-0.972），并被证明具有良好的校准性和临床实用性。DeLong检验表明，基于临床、放射组学和DL的决策融合模型在验证集上的表现明显不如三种特征融合模型。其他模型之间的诊断性能无统计学差异。
基于超声的临床、放射组学和DL特征融合模型在预测STT的Ki-67表达水平方面表现良好。
