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Peer-reviewedCC BYFrontiers in medicine

Artificial intelligence for ultrasound-based differentiation of soft tissue liposarcoma from benign masses.

  • YuKun Deng1
  • Chengjia Qu2
  • Yi Cao1
  • Guoyang Yu1
  • Jingmiao Yu1
  • Jie Yang1
  • Wen Guo1
  • Dandan Wang1
  • Xiaoting Qin1
  • Yibing Zhao1
  • Tao Chen1
  1. 1Department of Medical Ultrasonics, Beijing Jishuitan Hospital Affiliated to Capital Medical University, National Center for Orthopedics, Beijing, China.
  2. 2Department of Vascular Surgery, Beijing Jishuitan Hospital Affiliated to Capital Medical University, National Center for Orthopedics, Beijing, China.
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Abstract

Background

Accurate preoperative differentiation of liposarcoma from benign masses on ultrasound remains challenging and operator-dependent. We aimed to develop and validate a deep learning-based model to assist in distinguishing liposarcoma from benign masses.Methods

In this retrospective single-center study, we collected B-mode ultrasound examinations from patients with soft tissue masses between March 2015 and March 2022 at Beijing Jishuitan Hospital affiliated to Capital Medical University. A two-stage deep convolutional neural network was developed: a U-Net model segmented tumor regions, followed by a ResNet50 classifier for tumor type prediction. The cohort (n = 317) was split at the patient level into a development set (n = 222) and an internal test set (n = 95). Model performance was compared with 4 radiologists in the internal test set.Results

Among 317 cases, 191 were benign masses and 126 were liposarcomas. In the internal test set, the AI model achieved an AUC of 0.97 (95% CI 0.92-1.00), an accuracy of 0.97 (95% CI 0.93-1.00), a sensitivity of 0.96 (95% CI 0.86-1.00), and a specificity of 0.97 (95% CI 0.93-1.00). AI achieved significantly higher accuracy than both residents after Holm adjustment (adjusted P = 0.0156 and 0.0352, respectively). Differences between AI and either specialist were not statistically significant (both adjusted P = 1.0000).Conclusions

A two-stage deep learning model using ultrasound images showed preliminary potential for differentiating liposarcoma from benign masses in this single-center cohort. External validation is required before clinical use.

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Cite

BibTeXRIS
BibTeX
@article{deng2026artificial,
  title = {{Artificial intelligence for ultrasound-based differentiation of soft tissue liposarcoma from benign masses.}},
  author = {YuKun Deng and Chengjia Qu and Yi Cao and Guoyang Yu and Jingmiao Yu and Jie Yang and Wen Guo and Dandan Wang and Xiaoting Qin and Yibing Zhao and Tao Chen},
  journal = {Frontiers in medicine},
  year = {2026},
  month = oct,
  doi = {10.3389/fmed.2026.1960900},
  url = {https://europepmc.org/article/MED/42909320},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Artificial intelligence for ultrasound-based differentiation of soft tissue liposarcoma from benign masses.
AU  - YuKun Deng
AU  - Chengjia Qu
AU  - Yi Cao
AU  - Guoyang Yu
AU  - Jingmiao Yu
AU  - Jie Yang
AU  - Wen Guo
AU  - Dandan Wang
AU  - Xiaoting Qin
AU  - Yibing Zhao
AU  - Tao Chen
PY  - 2026
DA  - 2026/10/07
JO  - Frontiers in medicine
DO  - 10.3389/fmed.2026.1960900
UR  - https://europepmc.org/article/MED/42909320
AB  - Background  Accurate preoperative differentiation of liposarcoma from benign masses on ultrasound remains challenging and operator-dependent. We aimed to develop and validate a deep learning-based model to assist in distinguishing liposarcoma from benign masses.Methods  In this retrospective single-center study, we collected B-mode ultrasound examinations from patients with soft tissue masses between March 2015 and March 2022 at Beijing Jishuitan Hospital affiliated to Capital Medical University. A two-stage deep convolutional neural network was developed: a U-Net model segmented tumor regions, followed by a ResNet50 classifier for tumor type prediction. The cohort (n = 317) was split at the patient level into a development set (n = 222) and an internal test set (n = 95). Model performance was compared with 4 radiologists in the internal test set.Results  Among 317 cases, 191 were benign masses and 126 were liposarcomas. In the internal test set, the AI model achieved an AUC of 0.97 (95% CI 0.92-1.00), an accuracy of 0.97 (95% CI 0.93-1.00), a sensitivity of 0.96 (95% CI 0.86-1.00), and a specificity of 0.97 (95% CI 0.93-1.00). AI achieved significantly higher accuracy than both residents after Holm adjustment (adjusted P = 0.0156 and 0.0352, respectively). Differences between AI and either specialist were not statistically significant (both adjusted P = 1.0000).Conclusions  A two-stage deep learning model using ultrasound images showed preliminary potential for differentiating liposarcoma from benign masses in this single-center cohort. External validation is required before clinical use.
SN  - 2296-858X
ER  -