Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of clinical ultrasound : JCU
Deep Learning Radiomics Model Based on Ultrasound Images Predicts Myometrial Infiltration of Endometrial Cancer
OBJECTIVE: This study aims to develop and validate a deep learning radiomics (DLR) model based on ultrasound images for non-invasively distinguishing the myometrial infiltration (MI) of patients with endometrial cancer (EC). METHODS: We retrospectively collected 310 patients with EC who underwent surgical resection from center 1 from September 2017 to January 2025, forming a training group and a validation group. An external testing group was comprised of 80 patients from center 2. We extracted deep learning (DL) features and radiomics features from ultrasound images, establishing a DLR model after dimensionality reduction. The receiver operating characteristic analysis was used to evaluate the practicality of the proposed model. The utility of the proposed model was evaluated using receiver operating characteristic, calibration, and decision curve analysis. RESULTS: A total of 390 EC patients were included in the study. In the validation set, the AUC of the radiomics model was 0.874 (0.774-0.949), and in the testing group, the AUC of the DL model was 0.844 (0.699-0.957). The DLR model was superior to radiomics models and DL models in interpreting images, and also outperformed the diagnostic performance of advanced ultrasound physicians. CONCLUSION: The DLR model based on ultrasound images can accurately and non-invasively distinguish the MI depth of EC patients, assisting doctors in formulating more favorable treatment plans for patients.
5 May 2026
Read appraisal →Physics in medicine and biology
SSI-Net: a hybrid physics-constrained deep learning framework for quantitative ultrasound speed-of-sound reconstruction
Objective.Quantitative ultrasound tomography faces challenges in reconstructing speed-of-sound (SoS) distributions due to the ill-posed nature of the inverse problem and the computational complexity of full-waveform inversion. This study aims to develop an efficient and physically consistent deep learning framework for accurate SoS mapping from ultrasound signals.Approach. We propose speed of sound inversion network (SSI-Net), a dual data-physics-driven framework that integrates a bidirectional gated recurrent unit encoder for temporal feature extraction, a U-Net decoder for high-resolution spatial mapping, and a physics-constraint module based on an exact, differentiable finite-difference time-domain solver of the nonlinear Westervelt equation. The network model is trained through a joint optimization strategy that minimizes both data-fidelity loss and physics-informed residual loss, with a two-phase training schedule to ensure stable convergence.Main results.SSI-Net was validated across simulated, tissue-mimicking phantom, andin vivomouse datasets. It achieved superior reconstruction accuracy, with peak signal-to-noise ratio improvements of 7.4%-18.9% over state-of-the-art methods, while reducing the physics-based residual by 25.6%-57.5%. The framework demonstrated strong generalization to different transducer geometries (ring and linear arrays) and maintained stable performance across a ten-fold frequency range. Inference was completed within 109.4 ms per sample.Significance.By embedding an exact wave solver into a trainable architecture, SSI-Net combines the representational capacity of deep learning with strict physical consistency. It provides a robust, efficient, and clinically translatable solution for quantitative SoS imaging, offering a promising tool for tissue characterization in diagnostic ultrasound.
9 Apr 2026
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