探讨深度学习（DL）结合放射组学以及临床和影像学特征在预测软组织肿瘤（STT）Ki-67增殖指数中的价值。在这项回顾性研究中，自2021年1月起入院的STT患者共394例到 2023 年 12 月在两家不同的医院收集。 Hospital-1是训练队列（323例，其中分别有89例和低Ki-67），Hospital-2是外部验证队列（71例，其中23例和低Ki-67）。分别为 67）。评估临床和超声特征，包括年龄、性别、肿瘤大小、形态、边缘、内部回声和血流。通过单变量和多变量Logistic回归分析筛选具有显着相关性的危险因素。提取放射组学和深度学习特征后，通过支持向量机构建特征融合模型。将单独的临床特征、放射组学特征和DL特征获得的预测结果结合起来构建决策融合模型。最后使用DeLong检验比较模型之间的AUC是否存在显着差异。构建的三个特征融合模型和三个决策融合模型在预测STT中Ki-67表达水平方面表现出了优异的诊断性能。其中，基于临床、放射组学和深度学习的特征融合模型表现最好，训练队列中的 AUC 为 0.911（95% CI：0.886-0.935），验证中的 AUC 为 0.923（95% CI：0.873-0.972）队列，并被证明是经过良好校准且在临床上有用的。 DeLong测试表明，基于临床、放射组学和深度学习的决策融合模型在验证集上的表现明显差于三种特征融合模型。其他模型之间的诊断性能没有统计学差异。基于超声的临床、放射组学和 DL 特征的融合模型在预测 STT 中 Ki-67 表达水平方面表现出良好的性能。版权所有 © 2024 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

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.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.