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ORIGINAL RESEARCH article
Front. Oncol.
Sec. Radiation Oncology
Volume 14 - 2024 |
doi: 10.3389/fonc.2024.1443029
Development of ultrasound-based clinical, radiomics and deep learning fusion models for the diagnosis of benign and malignant soft tissue tumors
Provisionally accepted- 1 Third Hospital of Hebei Medical University, Shijiazhuang, China
- 2 First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei Province, China
Objectives: 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).Methods: 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.Results: 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. Conclusion: This study demonstrates the high clinical applicability of the fusion model in the differential diagnosis of STTs.
Keywords: deep learning 1, fusion model 2, Radiomics 3, Soft Tissue Tumors 4, Ultrasound 5
Received: 03 Jun 2024; Accepted: 16 Oct 2024.
Copyright: © 2024 Dai, Lu, Wang, Zhao, Liu, Sun, Yu and Xin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Sui Xin, Third Hospital of Hebei Medical University, Shijiazhuang, China
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