ArticleFrontiers in medicine2026
Artificial intelligence for ultrasound-based differentiation of soft tissue liposarcoma from benign masses.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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 ( 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 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.