OBJECTIVES:To develop a nomogram model combining ultrasound radiomics and clinical features and to evaluate its predictive value for pathological invasiveness of papillary thyroid carcinoma (PTC). METHODS:This study included 224 patients diagnosed with PTC between January 2024 and May 2025 at Zhejiang Provincial People's Hospital. Baseline clinical data, laboratory findings, and raw ultrasound images were collected. The patients were randomly divided into a training cohort (n=179) and a testing cohort (n=45) at an 8∶2 ratio. Additionally, 42 patients diagnosed with PTC from June to November 2025 were enrolled as an independent validation cohort. Pathological invasiveness was defined as the presence of one or more of the following features: extrathyroidal extension, vascular invasion, perineural invasion, intraglandular dissemination, extra-glandular invasion, central or lateral cervical lymph node metastasis, or high-risk subtypes. Univariate analysis was performed on 11 candidate clinical features, followed by stepwise logistic regression to identify independent predictors and to construct a clinical feature-based model. Ultrasound radiomics features were screened using Mann-Whitney U test, Spearman's correlation analysis (threshold 0.9) with greedy recursive elimination, and least absolute shrinkage and selection operator (LASSO) regression. The selected features were then input into eight machine learning algorithms to build predictive models, and the optimal algorithm was selected based on the area under the receiver operating characteristic (ROC) curve (AUC). A nomogram was subsequently constructed by integrating the clinical model and the ultrasound radiomics model into a logistic regression framework. The discriminative ability, calibration, and clinical utility of the nomogram were assessed using ROC curves, calibration curves, and clinical decision curves. RESULTS:The clinical feature model based solely on nodule size achieved AUCs of 0.889 (95%CI: 0.843-0.935) and 0.934 (95%CI: 0.860-0.973) in the training and testing cohorts, respectively. Thirteen ultrasound radiomics features were selected. The radiomics model built with logistic regression yielded AUCs of 0.846 (95%CI: 0.791-0.902) and 0.939 (95%CI: 0.871-1.000) in the training and testing cohorts, respectively. The nomogram combining ultrasound radiomics and clinical features achieved AUCs of 0.902 (95%CI: 0.858-0.945) in the training cohort, 0.982 (95%CI: 0.953-1.000) in the testing cohort, and 0.803 (95%CI: 0.665-0.941) in the independent validation cohort. Calibration curves demonstrated good agreement between predicted and observed probabilities, and clinical decision curves indicated favorable net clinical benefit. CONCLUSIONS:A nomogram model combining ultrasound radiomics and clinical features was successfully developed, which exhibited good discrimination, calibration, and clinical utility.

Ultrasound is clinically established, portable, and repeatable, but its dominant contrast logic remains anatomical and perfusion-based. Gas vesicles (GVs), acoustic reporter genes (ARGs), targeted GV derivatives, and GV-containing release materials have introduced a protein-based route toward molecular ultrasound nanomedicine. Their translation, however, cannot be inferred from acoustic detectability alone. This Review examines GVs and related acoustic protein nanostructures as biomaterial and nanomedicine systems. We distinguish clinical microbubbles, purified GVs, exogenous GV-labeled cells, intracellular ARG systems, GV-based release materials, and prospective computationally designed acoustic protein nanostructures. These formats differ in biological setting, route of administration, pharmacologic burden, and evidentiary maturity. We define computational molecular sonography as a bounded organizing framework that links reporter design, acoustic signal representation, biological fate, exposure estimation, and clinician-governed safety. The strongest current evidence comes from natural and engineered GV systems, including pressure-dependent collapse, nonlinear detection, gas vesicle protein C (GvpC)-mediated surface engineering, blood-component interactions, and reporter-gene imaging. By contrast, fully de novo acoustic protein nanostructures remain prospective rather than in vivo-ready. The major translational barriers include shell mechanics and stability, surface corona and immune clearance, repeat-dose safety, toxicology, manufacturing consistency, critical quality attributes, and Sim-to-Real uncertainty in tissue and acoustic fields. Future clinical value will require standardized biomaterial characterization, direct acoustic phenotyping, reporter-sensitive acquisition, and regulatory-grade control of product and exposure. Ultrasound visibility is therefore necessary but insufficient; translation will depend on aligning material design, biological fate, signal interpretation, and safety as a coupled problem.

High workload and inconsistent quality of ultrasound report writing may lead to diagnostic errors. This study aims to ascertain whether fine-tuned open-source large language models (LLMs) can achieve promising performance for automated quality control of Chinese ultrasound reports, when compared to proprietary LLMs. This retrospective, multi-center study included a multi-subspecialty dataset of 1800 Chinese ultrasound reports, comprising 1500 quality-controlled reports injected artificially with six predefined error types and 300 reports with naturally occurring errors. Nine proprietary LLMs (under zero-shot and few-shot paradigms) and seven open-source LLMs (under fine-tuning) were evaluated, with performance compared against that of radiologists of varying seniority. Performance was measured by detection accuracy, Macro-F1 score, precision, recall, and mean absolute error across six categories. Fine-tuned open-source LLMs, notably Qwen3-14B, achieved a detection accuracy of 0.931 and a Macro-F1 of 0.739, approaching the performance of senior radiologists. Some fine-tuned open-source LLMs maintained performance despite smaller parameter sizes and outperformed most proprietary LLMs with vastly larger parameter counts. The fine-tuned Qwen3-14B demonstrated superior recognition capability for semantic errors such as redundancy, spelling, orientation, and unit or value errors. This study demonstrates that task-specific fine-tuning enables open-source LLMs to rival proprietary LLMs and expert radiologists in Chinese ultrasound report error detection, offering a locally deployable and privacy-compliant alternative for AI-assisted clinical quality control workflows.

Objective This study evaluated the effectiveness of dynamic ultrasound in diagnosing rotational vertebral artery syndrome (RVAS) and its potential as a screening tool. Methods From January 2022 to September 2024, 98 participants (49 suspected RVAS patients and 49 asymptomatic controls) underwent vertebral artery ultrasound in neutral and rotated head positions. Blood flow velocity and resistance index (RI) changes in the V2 and V3 artery segments were compared. Diagnostic performance and compression thresholds were assessed using independent t tests and receiver operating characteristic (ROC) analysis. An additional pilot study group of 10 participants (5 patients with suspected RVAS and 5 asymptomatic controls) was included to assess the diagnostic thresholds and the role of dynamic ultrasound in RVAS detection. Results Significant hemodynamic changes were observed in the RVAS group after neck rotation but not in controls. In the V2 segment, velocity decreased from 51.76 +/- 14.64 cm/s (neutral) to 44.61 +/- 21.01 cm/s (rotated, P = .014). In the V3 segment, velocity increased from 69.37 +/- 18.32 cm/s (neutral) to 161.18 +/- 51.32 cm/s (rotated, P < .001). RI rose from 0.69 +/- 0.06 to 0.76 +/- 0.15 (P = .001). ROC analysis identified thresholds of V3 velocity >74.68 cm/s and V3 RI >0.71 for RVAS diagnosis (area under the curve = 0.80, sensitivity = 67.3%, specificity = 85.7%). In the pilot study group, the sensitivity was 80%, and the specificity was 100%. Conclusion Dynamic ultrasound effectively detects positional hemodynamic changes and serves as a valuable tool for RVAS diagnosis. Identified thresholds support its clinical utility, advocating broader adoption to improve early detection and management.

Objective The interplay between inflammation, immunity, and RNA modification (RM) in the tumor microenvironment (TME) of cervical cancer (CC) remains poorly understood.Materials and Methods We performed integrated analysis of single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), and bulk RNA-seq data. A comprehensive computational framework, encompassing 101 machine learning algorithms, was used to construct a consensus machine learning-derived RNA methylation signature (CMDRMS). This signature was evaluated for its prognostic value, association with immune cell infiltration, inflammatory profiles, and response to immunotherapy and pharmacotherapy. Key findings were validated through RT-qPCR, immunofluorescence, and structural modeling via AlphaFold3.Results scRNA-seq and ST revealed significant enrichment of RM regulators within tumor epithelial cells and specific immune subsets in the TME. The CMDRMS, comprising four hub genes (CBLLI, LARPI, NUDT3, and METTL16), effectively stratified patients into high- and low-risk groups according to the mean CMDRMS score. The high-CMDRMS group exhibited poorer overall survival, an inflamed TME with enhanced immune cell infiltration, yet a paradoxically diminished response to PD-1/CTLA-4 blockade, suggesting an immunosuppressive phenotype. Conversely, these patients showed potential sensitivity to PD-L1 therapy. Drug sensitivity analysis identified 48 agents with greater efficacy in the low-CMDRMS group. Pseudotime analysis indicated that core CMDRMS genes, particularly NUDT3, were upregulated in later differentiation stages. Experimental validation confirmed the overexpression of NUDT3 in CC tissues and cell lines, correlating with advanced clinical stages. Crucially, NUDT3 demonstrated significant coexpression and predicted structural interaction with PD-L1.Conclusion Our study underscores the critical role of RM in shaping the inflammation-immune interplay within the CC TME and provides a novel prognostic biomarker (CMDRMS) for CC and a potential therapeutic target (NUDT3) for reversing immunotherapy resistance.

Objective:To develop and validate a simple and practical scoring system based on O-RADS ultrasound features for distinguishing benign from malignant ovarian cystic lesions. Methods:This multi-institutional retrospective study included women with ovarian cystic lesions who underwent pelvic ultrasound and had either histopathological confirmation or at least 2 years of imaging follow-up. Participants were randomized into training (70%, n=1715) and internal validation (30%, n=711) sets, with an external validation set (n=278) from another center. Univariate and multivariate logistic regression identified independent O-RADS predictors of malignancy, which were assigned weights to construct a scoring system. Performance was validated internally and externally, and compared with O-RADS. Inter-observer agreement was assessed between two junior sonographers and two senior sonographers. Results:Seven independent predictors were identified: maximum diameter ≥10 cm (score 2), solid component (2), irregular internal wall (2), color score 3-4 (2), ascites (2), >3 papillary projections (1), and acoustic shadowing (-2), with an optimal cutoff value of 4 points. In the training set, the scoring system achieved an AUC of 0.973, sensitivity of 95.3%, and specificity of 92.5%, compared to O-RADS (AUC of 0.954, sensitivity of 97.3%, specificity of 86.7%). The scoring system maintained stable performance in internal validation (AUC of 0.968, sensitivity of 93.8%, specificity of 91.8%) and external validation (AUC of 0.964, sensitivity of 93.0%, specificity of 90.2%). Inter-observer agreement was excellent among all sonographers (κ=0.80, p<0.001), as well as between junior (κ=0.82) and senior (κ=0.79) sonographers. Conclusion:The O-RADS-based scoring system demonstrated excellent and stable diagnostic performance, potentially offering a simple and practical tool for clinical practice; however, prospective multicenter validation in diverse populations is warranted before widespread adoption.

Classification of benign, borderline, and malignant adnexal masses is critical to effective clinical management, but remains a challenge. We developed Clinical-Ovarian Multi-Task Attention (Clinical-OMTA), an artificial intelligence model based on a dual-backbone architecture (benign vs. non-benign, and borderline vs. malignant) that integrates ultrasound, age, and Carbohydrate Antigen 125 (CA125) for multi-class classification. The model's performance, generalisability, and clinical utility were evaluated. Retrospective data were collected from 23 hospitals (1882 patients for training, validation, and internal testing from 21 hospitals; 340 and 159 patients for external testing from two hospitals). In the external image dataset, Clinical-OMTA demonstrated comparable diagnostic performance to ADNEX (area under the receiver operating characteristic curve [AUC]: 0.950 vs. 0.953, 0.870 vs. 0.853, 0.930 vs. 0.938) and subjective assessment by an expert examiner (accuracy: 85.6% vs. 87.4%). While Clinical-OMTA supported multimodal integration, it did not outperform Ovarian Multi-Task Attention (OMTA) that trained only with images, indicating that including age and CA125 did not improve performance. Clinical-OMTA performed similarly across acquisition modes, equipment types, scanning methods, and different centres (accuracy: 79.9%-87.7%). With Clinical-OMTA as a decision support tool, radiologists showed significantly improved inter-reader agreement (kappa: 0.17-0.78 vs. 0.86-0.98) and diagnostic accuracy (72.3% vs. 88.0%). Clinical-OMTA appears generalisable and could be especially useful in low-resource or remote settings where expert ultrasound examiners are scarce.

BACKGROUND:Umbilical cord ulceration (UCU) is a rare but potentially fatal obstetric condition characterized by ulcerative disruption of the umbilical cord, often leading to fetal hemorrhage and intrauterine fetal demise. Placental abruption, defined as the premature separation of the placenta from the uterine wall, is another major cause of perinatal mortality. When UCU is complicated by occult placental abruption, the risk of adverse perinatal outcomes increases substantially. These associations highlight the importance of vigilant prenatal surveillance and comprehensive imaging to facilitate early detection and timely intervention. CASE PRESENTATION:We report a case involving a 33-year-old woman at 37 weeks and 5 days of gestation who presented with abdominal tightness and subjectively reduced fetal movements. Prenatal ultrasound and Color Doppler imaging (CDI) revealed umbilical cord abnormalities, including loss of Wharton's jelly and absent blood flow signals. An emergency cesarean section was promptly performed, resulting in favorable maternal and neonatal outcome. Postoperative pathological examination confirmed the diagnosis of UCU with hemorrhage, complicated by placental abruption. CONCLUSIONS:To our knowledge, this is the first reported case of UCU complicated by placental abruption. This case underscores the importance of considering UCU in the differential diagnosis of unexplained fetal distress in late pregnancy. Early recognition and timely intervention guided by prenatal imaging are essential for improving perinatal outcomes.

Purpose:C-X-C chemokine receptor 4 (CXCR4) mediates the inflammatory response of atherosclerotic vulnerable plaques (ASVP) and is a potential biomarker of atherosclerotic vulnerable plaques. The purpose of this study was to use the imaging ability of a new type of ultrasound contrast agent, nanoscale biosynthetic gas vesicles (GVs), on the vascular wall and to combine the specific ligand of CXCR4 to construct a targeted molecular probe to achieve early identification of atherosclerotic vulnerable plaques and guide clinical treatment decisions. Materials and Methods:Compared three contrast agents: GVs, the micro-contrast agent SonoVue, and polyethylene glycol (PEG)-modified GVs in the carotid artery. The expression of CXCR4 in atherosclerotic plaques was demonstrated using flow cytometry and immunofluorescence experiments. Cell adhesion and in vivo ultrasound imaging experiments demonstrated their ability to target the nanoscale biosynthetic gas vesicles. The safety of GVs, PEG-GVs, and CXCR4-GVs was tested the CCk8 test, H&E staining, and serum detection. Results:Strong CXCR4 expression was observed in plaques, whereas little expression was observed in normal vessels. GVs can produce stable contrast signals on the carotid artery walls of rats, whereas PEG-GVs can produce more lasting contrast signals on the carotid artery wall of rats. CXCR4-GVs exhibited excellent binding capability to ox-LDL-induced RAW264.7 cells. Animal experiments showed that compared with Con-GVs, CXCR4-GVs injected plaque imaging signal was stronger and more durable. In vitro scanning of vulnerable plaques in rats injected with fluorescent vesicles demonstrated that CXCR4-GVs oozed through the neovasculars within vulnerable plaques and aggregated in vulnerable plaques. Through the CCK8 test, H&E staining, and serum detection, the safety of CXCR4-GVs was confirmed. Conclusion:CXCR4-GVs were constructed as targeted molecular probes, which can be proven to have good targeting properties to vulnerable atherosclerotic plaques.

Photothermal therapy (PTT) has emerged as a promising new approach in tumor treatment, with the great advantages including non-invasiveness and temporal controllability. However, the effective delivery of photothermal agents into tumor remains a significant challenge, limiting its clinical translational application. In this study, we developed a kind of photothermal agents modified with gas vesicles (GVs), greatly facilitating ultrasound/fluorescence imaging-guided delivery of photothermal agents and enhancing the efficacy of photothermal therapy. The GVs were synthesized and extracted from Halobacterium NRC-1, followed with modification with IR808. The resulting GVs-IR808 were able to be visually tracked by ultrasound and fluorescence imaging. Upon their arrival at the tumor area after systemic administration, ultrasound irradiation was applied to induce the cavitation of GVs-IR808, greatly promoting IR808 delivery into the tumor. The subsequent laser irradiation was applied and resulted in a significant inhibition of tumor growth. In conclusion, our study provides a novel approach for ultrasound/fluorescence dual-modal imaging-guided photothermal treatment of breast tumors.

OBJECTIVE:This study evaluated the effectiveness of dynamic ultrasound in diagnosing rotational vertebral artery syndrome (RVAS) and its potential as a screening tool. METHODS:From January 2022 to September 2024, 98 participants (49 suspected RVAS patients and 49 asymptomatic controls) underwent vertebral artery ultrasound in neutral and rotated head positions. Blood flow velocity and resistance index (RI) changes in the V2 and V3 artery segments were compared. Diagnostic performance and compression thresholds were assessed using independent t tests and receiver operating characteristic (ROC) analysis. An additional pilot study group of 10 participants (5 patients with suspected RVAS and 5 asymptomatic controls) was included to assess the diagnostic thresholds and the role of dynamic ultrasound in RVAS detection. RESULTS:Significant hemodynamic changes were observed in the RVAS group after neck rotation but not in controls. In the V2 segment, velocity decreased from 51.76 ± 14.64 cm/s (neutral) to 44.61 ± 21.01 cm/s (rotated, P = .014). In the V3 segment, velocity increased from 69.37 ± 18.32 cm/s (neutral) to 161.18 ± 51.32 cm/s (rotated, P < .001). RI rose from 0.69 ± 0.06 to 0.76 ± 0.15 (P = .001). ROC analysis identified thresholds of V3 velocity >74.68 cm/s and V3 RI >0.71 for RVAS diagnosis (area under the curve = 0.80, sensitivity = 67.3%, specificity = 85.7%). In the pilot study group, the sensitivity was 80%, and the specificity was 100%. CONCLUSION:Dynamic ultrasound effectively detects positional hemodynamic changes and serves as a valuable tool for RVAS diagnosis. Identified thresholds support its clinical utility, advocating broader adoption to improve early detection and management.

Endometriosis is a common gynecological disorder that is associated with chronic pelvic pain, infertility, and metabolic complications. Sarcopenia, characterized by progressive skeletal muscle loss, predominantly affects older adults. This study explored the incidence and risk factors for sarcopenia in endometriosis patients using the NHANES dataset, which included 373 participants. Endometriosis was confirmed through self-report questionnaire, and sarcopenia was diagnosed via dual-energy X-ray absorptiometry. Covariates encompassed age, race, marital status, education attainment, poverty income ratio, smoking habits, and comorbidities. Statistical analyses were conducted using SPSS version 26.0, incorporating four multivariate regression models. The average age was 40.3 and 40.0 years in endometriotic participants with and without sarcopenia, respectively. Minority ethnicity had higher odds for sarcopenia (OR 6.00, 95% CI 1.24-29.07). A disease duration of endometriosis less than five years was associated with higher sarcopenia risk (OR 4.83, 95% CI 2.57-9.09). Conversely, lower educational levels were linked to a reduced chance of developing sarcopenia (OR 0.42, 95% CI 0.21-0.86). These findings were consistent across all regression models, indicating that ethnic minority status, higher educational attainment, and shorter disease duration are significant risk factors for concurrent sarcopenia in endometriosis patients.

The assessment of Human Epidermal Growth Factor Receptor 2 (HER2) expression status is crucial for determining the eligibility of breast cancer (BC) patients for HER2-targeted therapies. This study aims to develop a nomogram model that incorporates multimodal ultrasound imaging features alongside clinicopathological characteristics to evaluate HER2 status. A retrospective analysis was conducted on 456 breast cancer patients who underwent breast ultrasound between January 2019 and December 2021. The dataset was randomly divided into a training cohort (n = 319) and a validation cohort (n = 137) in a 7:3 ratio. Independent factors predicting HER2 status in the training cohort were evaluated using univariate and multivariate logistic regression. Subsequently, a combined model was developed and validated in the validation cohort. Model performance was assessed through receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and calibration curves to evaluate discrimination, net clinical benefit, and calibration, respectively. Of the 456 patients enrolled, 120 (26.32

The up-regulation of Extradomain B fibronectin (EDB-FN) levels in the extracellular matrix is a potential biomarker of advanced atherosclerotic lesions. Currently, no ultrasound molecular imaging probe targeting EDB-FN is available for EDB-FN the identification and treatment of atherosclerotic vulnerable plaques. Herein, we developed a biosynthetic gas vesicles (GVs)-based acoustic probe targeting EDB-FN, which delivered small interfering RNA (siRNA) molecules to inhibit the progression of vulnerable plaques, enabling integrated diagnosis and therapy via ultrasound. The cyclic nonapeptide CTVRTSADC (ZD2)-GVs showed highly enhanced vascular walls, while lumens were nearly not enhanced, which is different from that of traditional microbubble ultrasonic imaging in large and medium vessels. This special imaging feature facilitated the ultrasound-based detection of vulnerable plaques. The pathological analysis results showed that the synthesised integrated diagnostic and therapeutic probe EDBsiRNA@ZD2-GVs provided significant therapeutic benefits in the treatment of vulnerable plaques such as reduced lipid contents and neovascularisation, and increased collagen deposition and smooth muscle cell proliferation accompanied by downregulating expression of EDB-FN when combined with acoustic irradiation. This study demonstrated that EDBsiRNA@ZD2-GVs achieved effective ultrasound targeting visualization and delivery of siRNA to vulnerable plaques. Thus, EDBsiRNA@ZD2-GVs can be used as an ultrasound probe for the detection and therapy of vulnerable plaques.

ObjectiveTo evaluate whether ultrasound-radiomics (US-radiomics) features extracted from ultrasound, integrated with genomic data of single nucleotide polymorphisms (SNPs) associated with cervical cancer (CC) susceptibility and clinical features, could improve the prediction of lymph node metastasis (LNM) in patients with CC.MethodsThe model was established using ultrasound image features, SNPs data, and clinical data from patients. All subjects were randomly divided into a training set and a validation set in a 7:3 ratio. Feature selection and prediction modeling were performed using the max-relevance and min-redundancy (mRMR) algorithm, the least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) methods.ResultsD-dimer, SCC-Ag and rs2977530 were identified as independent predictors of LNM. The combined clinical-SNPs-US-radiomics model demonstrated higher classification efficiency for predicting LNM in CC, with an area under the receiver operating characteristic curve (AUC) of 0.826 [95% CI: 0.720-0.921] in the training cohort and 0.699 [95% CI: 0.537-0.857] in the validation cohort.ConclusionsThe model developed in this study, which integrates US-radiomics score with clinical features and SNPs data, has the potential to non-invasively predict LNM in CC and holds promise for clinical application.

Objective Cervical tuberculous lymphadenitis (CTBL) and cervical lymph node metastasis (CLNM) share similar imaging characteristics, making differentiation challenging. This study aims to evaluate the clinical utility of a multimodal radiomics model combining grayscale ultrasound (GUS), elastography ultrasound (EUS), and contrast-enhanced ultrasound (CEUS) for distinguishing CTBL from CLNM. Methods A high-quality dataset comprising 203 cases of CTBL was used to train and test the radiomics models. The performance of single-modal (GUS, EUS, CEUS) and combined models was compared using AUC, sensitivity, specificity, and accuracy metrics. An independent test set of 45 cases was included for validation. Results The combined GUS + EUS + CEUS model outperformed single-modal models, achieving AUCs of 0.894, 0.832, and 0.919 in the training, validation, and test sets, respectively. Its diagnostic performance was comparable to a clinical model in validation and test sets, demonstrating superior generalizability and robustness. Wavelet features accounted for all selected features, enhancing the model's discrimination ability. Conclusions The integration of three ultrasound modalities captures multidimensional imaging features, reducing reliance on subjective interpretation. This multimodal radiomics approach provides a standardized diagnostic tool with significant clinical potential, particularly for less experienced physicians. Further validation with diverse datasets is needed to confirm its utility.

Metastasis is a primary cause of mortality and treatment failure in ovarian cancer, with limited effective therapeutic strategies. Low-intensity ultrasound (LIUS) and microbubbles (MBs) has been demonstrated as an adjunctive technique capable of enhancing drug delivery and suppressing tumor metastasis. However, the underlying mechanisms remain incompletely understood. In this study, we aimed to investigate whether LIUS + MBs alone could suppress tumor metastasis and to explore its mechanism of action through disruption of the cytoskeletal remodeling in filopodia, an essential structure in the early stages of cancer cell dissemination. Based on cell-based experiments to determine the optimal parameters, our results showed LIUS + MBs significantly inhibited the migration and invasion of ovarian cancer cells. In vivo, LIUS + MBs treatment markedly suppressed the overall metastasis in the orthotopic ovarian cancer model, and in both the intraperitoneal and hematogenous metastatic models established by injecting pretreated cells. Morphologically, such treatment led to a notable reduction in the length and number of filopodia, while the number of lamellipodia remained unaffected. At the molecular level, LIUS + MBs disturbed filopodia formation and the metastatic potential of ovarian cancer cells by suppressing the activation of Cdc42, a key regulator of cytoskeletal dynamics. The inhibitory effect was reversed by the overexpression of Cdc42CA. Further proteomic and bioinformatics analysis implied that LIUS + MBs may reduce Cdc42 activity by upregulating the expression of GTPase-activating proteins (GAPs). Our research provides novel insight into the mechanism by which LIUS + MBs can inhibit tumor metastasis, highlighting its role in disturbing the Cdc42-mediated cytoskeletal remodelling of filopodia.

ObjectivesOur previous studies have found that low‐frequency, low‐pressure, weakly focused ultrasound (FUS) can induce acoustic droplet vaporization (ADV) of perfluoropentane (PFP) droplets and result in localized liver and prostate tissue controllable cavitation resonance and mechanical damage. To further investigate the mechanical erosion induced by ultrasound and locally injected phase‐shift acoustic droplets in rabbit liver.MethodsThe liver of each rabbit was treated with perfluoromethylcyclopentane (PFMCP) alone, FUS combined with PFMCP (FUS + PFMCP), and FUS combined with PFP (FUS + PFP).ResultsTwo‐dimensional ultrasound images showed that immediately after the completion of FUS + PFP group treatments, a high echogenicity bubble cloud could be observed, while there were no significant differences in the PFMCP and FUS + PFMCP group before and after treatment. The liver necrotic area in the FUS + PFP group was 6.2 times that of the FUS + PFMCP group (P < .05), whereas no liver necrosis was observed in the PFMCP group. At the same time, the number of vacuoles in the liver in the FUS + PFP group was approximately 70 times that of the FUS + PFMCP group (P < .001), whereas no vacuoles were observed in the PFMCP group (P < .001).ConclusionsBoth FUS + PFMCP and PFMCP alone have poor mechanical erosion in liver tissue, and may even cause no damage. Only PFP droplets combined with FUS can cause significant mechanical destruction of liver tissue, leading to tissue necrosis in the droplet injection area.

We evaluated whether image-radiomics features extracted from ultrasound with integrated genomic data of single nucleotide polymorphisms (SNPs) associated with CIN susceptibility and clinical features could improve the differential diagnosis of high-grade intraepithelial disease (HSIL) and stage IA CC. Models were developed from ultrasound-derived radiomic features, SNPs data and clinical variables. After random 7:3 allocation into training and validation sets, clinical and SNPs datasets were each screened by univariable then multivariable logistic regression to build separate predictors. Ultrasound radiomics features were reduced with Max-relevance and min-redundancy (mRMR) and least absolute contraction and selection operator (LASSO) to generate an ultrasound radiomic score, which was subsequently used with clinical and SNPs data to establish the combined model. For the differentiation of HSIL and early CC models, only the ultrasound radiomics model showed higher classification efficiency, which the performance in the validation cohort (AUC: 0.885 [95%CI: 0.751-1.000]) than the method combining ultrasound radiomics score, clinical data and SNPs data with an AUC value of 0.850[95%CI: 0.713-0.987]. The model developed and constructed in this study, based on ultrasound radiomics, demonstrates potential for differentiating HSIL from Stage IA CC and exhibits significant clinical application value.