USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis

Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundational models encounters intrinsic challenges in US analysis, i.e., insufficient databases, low quality, and ineffective features. In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis. First, a large-scale Multi-organ, Multi-center, and Multi-device US database was built, comprehensively containing over two million US images. Organ-balanced sampling was employed for unbiased learning. Then, USFM is self-supervised pre-trained on the sufficient US database. To extract the effective features from low-quality US images, we proposed a spatial-frequency dual masked image modeling method. A productive spatial noise addition-recovery approach was designed to learn meaningful US information robustly, while a novel frequency band-stop masking learning approach was also employed to extract complex, implicit grayscale distribution and textural variations. Extensive experiments were conducted on the various tasks of segmentation, classification, and image enhancement from diverse organs and diseases. Comparisons with representative US image analysis models illustrate the universality and effectiveness of USFM. The label efficiency experiments suggest the USFM obtains robust performance with only 20% annotation, laying the groundwork for the rapid development of US models in clinical practices.Comment: Submit to MedIA, 17 pages, 11 figure

Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese women

Background and purpose: Artificial intelligence (AI) technology is increasingly being used in the medical field. This study aimed to assess the effectiveness of artificial intelligence ultrasound system for identifying breast lesions in Chinese women and its role in breast cancer early detection. Methods: A prospective study was conducted on healthy women aged 35-74 years who came to Fudan University Shanghai Cancer Center from August 2020 to December 2020 for breast ultrasonography. All the women were examined by AI-assisted ultrasound first, and then by conventional ultrasonography. We compared the differences between AI-assisted ultrasound and conventional ultrasonography in identifying breast lesions in Chinese women. One year later, we looked up the hospital medical history and Shanghai Cancer Registration Management System for the final diagnosis of breast cancer. Results: A total of 360 women were included in the study and received breast examinations using both AI-assisted ultrasound and conventional ultrasound. A total of 2 504 breast lesions were detected, of which, 2 217 were detected by AI-assisted ultrasound, with a lesion recognition rate of 88.5%. Conventional ultrasound identified 1 090 lesions, with a lesion recognition rate of 43.5%. Using conventional ultrasound as the standard, the sensitivity and specificity of AI-assisted ultrasound for Breast Imaging Reporting and Data System (BI-RADS) level 4 and above lesions were 93.3% (95% CI: 80.7-98.3) and 100.0% (95% CI: 99.5-100.0), respectively. During one-year follow-up, 10 patients were diagnosed with breast cancer, and 8 of whom were identified by both AI-assisted ultrasound and conventional B ultrasound. The sensitivity of AI-assisted ultrasound and conventional ultrasound for breast cancer was 80.0% (95% CI: 44.2-96.4), and the specificity was 88.6% (95% CI: 84.6-91.6). Conclusion: AI-assisted ultrasound has good identification ability for breast lesions in Chinese women. The recognition ability for high-risk breast lesions (BI-RADS 4A and above) and early breast cancer is equivalent to that of conventional ultrasound, which is suitable for breast cancer screening in large-scale community of women with general risk

Comparison of Intra-Arterial Chemotherapy Efficacy Delivered Through the Ophthalmic Artery or External Carotid Artery in a Cohort of Retinoblastoma Patients

Purpose: To evaluate the efficacy of an external carotid artery (ECA) alternative route in intra-arterial chemotherapy (IAC) for treatment of retinoblastoma.Methods: In this retrospective, single-centre, case-control study, 98 retinoblastoma patients who received successful IAC were included. The drug delivery routes were the primary ophthalmic artery (OA) route and the ECA route when OA catheterization was not feasible.Results: A total of 337 successful IAC procedures were performed in our study, of which 32 (9.5%) procedures were performed through the ECA route. Eighteen eyes (18.4%) accepted at least one IAC through branches of the ECA. Statistical analysis showed that there was no significant difference in ocular clinical results (enucleation, death, recurrence and event-free) between the ECA and OA routes. No significant association was found between the route of drug delivery and the ocular survival time (p = 0.69). The use of ECA catheterization in at least one IAC cycle was not a predictor of enucleation (HR: 1.58; 95% CI: 0.56–4.46, p = 0.39). The increasing number of procedures through the ECA route did not increase the risk of enucleation (HR: 1.64; 95% CI: 0.42–6.39, p = 0.48).Conclusion: The ECA alternative route did not affect the efficacy of IAC in retinoblastoma. When the standard OA approach is not feasible, ECA system catheterization should be considered

Radiogenomic Analysis of Papillary Thyroid Carcinoma for Prediction of Cervical Lymph Node Metastasis: A Preliminary Study

Background Papillary thyroid carcinoma (PTC) is characterized by frequent metastases to cervical lymph nodes (CLNs), and the presence of lymph node metastasis at diagnosis has a significant impact on the surgical approach. Therefore, we established a radiomic signature to predict the CLN status of PTC patients using preoperative thyroid ultrasound, and investigated the association between the radiomic features and underlying molecular characteristics of PTC tumors. Methods In total, 270 patients were enrolled in this prospective study, and radiomic features were extracted according to multiple guidelines. A radiomic signature was built with selected features in the training cohort and validated in the validation cohort. The total protein extracted from tumor samples was analyzed with LC/MS and iTRAQ technology. Gene modules acquired by clustering were chosen for their diagnostic significance. A radiogenomic map linking radiomic features to gene modules was constructed with the Spearman correlation matrix. Genes in modules related to metastasis were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network was built to identify the hub genes in the modules. Finally, the screened hub genes were validated by immunohistochemistry analysis. Results The radiomic signature showed good performance for predicting CLN status in training and validation cohorts, with area under curve of 0.873 and 0.831 respectively. A radiogenomic map was created with nine significant correlations between radiomic features and gene modules, and two of them had higher correlation coefficient. Among these, MEmeganta representing the upregulation of telomere maintenance via telomerase and cell-cell adhesion was correlated with 'Rectlike' and 'deviation ratio of tumor tissue and normal thyroid gland' which reflect the margin and the internal echogenicity of the tumor, respectively. MEblue capturing cell-cell adhesion and glycolysis was associated with feature 'minimum calcification area' which measures the punctate calcification. The hub genes of the two modules were identified by protein-protein interaction network. Immunohistochemistry validated that LAMC1 and THBS1 were differently expressed in metastatic and non-metastatic tissues (p=0.003; p=0.002). And LAMC1 was associated with feature 'Rectlike' and 'deviation ratio of tumor and normal thyroid gland' (p<0.001; p<0.001); THBS1 was correlated with 'minimum calcification area' (p<0.001). Conclusions The radiomic signature proposed here has the potential to noninvasively predict the CLN status in PTC patients. Merging imaging phenotypes with genomic data could allow noninvasive identification of the molecular properties of PTC tumors, which might support clinical decision making and personalized management

Biomimetic nanoparticles drive the mechanism understanding of shear-wave elasticity stiffness in triple negative breast cancers to predict clinical treatment

In clinical practice, we noticed that triple negative breast cancer (TNBC) patients had higher shear-wave elasticity (SWE) stiffness than non-TNBC patients and a higher α-SMA expression was found in TNBC tissues than the non-TNBC tissues. Moreover, SWE stiffness also shows a clear correlation to neoadjuvant response efficiency. To elaborate this phenomenon, TNBC cell membrane-modified polylactide acid-glycolic acid (PLGA) nanoparticle was fabricated to specifically deliver artesunate to regulate SWE stiffness through inhibiting CAFs functional status. As tested in MDA-MB-231 and E0771 orthotopic tumor models, CAFs functional status inhibited by 231M-ARS@PLGA nanoparticles (231M-AP NPs) had reduced the SWE stiffness as well as attenuated hypoxia of tumor as tumor soil loosening agent which amplified the antitumor effects of paclitaxel and PD1 inhibitor. Single-cell sequencing indicated that the two main CAFs (extracellular matrix and wound healing CAFs) that produces extracellular matrix could influence the tumor SWE stiffness as well as the antitumor effect of drugs. Further, biomimetic nanoparticles inhibited CAFs function could attenuate tumor hypoxia by increasing proportion of inflammatory blood vessels and oxygen transport capacity. Therefore, our finding is fundamental for understanding the role of CAFs on affecting SWE stiffness and drugs antitumor effects, which can be further implied in the potential clinical theranostic predicting in neoadjuvant therapy efficacy through non-invasive analyzing of SWE imaging

The Performance and Mechanism of a Mg-Al Double-Layer Oxide in Chloride ion Removal from an Aqueous Solution

The increasing threat of chloride ions (Cl&minus;) has led researchers to explore efficient removal technologies. Sewage treatment with a double-layer hydroxide/oxide (LDH/LDO) is receiving increasing attention. In this work, Mg-Al LDO adsorbents were produced by the calcination of the Mg-Al LDH precursor, which was constituted by improved coprecipitation. The influence of calcination temperature, calcination time, adsorbent dosage, Cl&minus; initial concentration, contact time, and adsorption temperature on Cl&minus; elimination was investigated systematically. The experimental results showed that a better porous structure endowed the Mg-Al LDO with outstanding adsorption properties for Cl&minus;. The adsorption process was well matched to the pseudo-second-order kinetics model and the Freundlich model. Under optimal conditions, more than 97% of the Cl&minus; could be eliminated. Moreover, the removal efficiency was greater than 90% even after 11 adsorption&ndash;desorption cycles. It was found that the electrostatic interaction between Cl&minus; and the positively charged Mg-Al LDO laminate, coupled with the reconstruction of the layer structure, was what dominated the Cl&minus; removal process

An autocatalytic multicomponent DNAzyme nanomachine for tumor-specific photothermal therapy sensitization in pancreatic cancer

Abstract Multicomponent deoxyribozymes (MNAzymes) have great potential in gene therapy, but their ability to recognize disease tissue and further achieve synergistic gene regulation has rarely been studied. Herein, Arginylglycylaspartic acid (RGD)-modified Distearyl acylphosphatidyl ethanolamine (DSPE)-polyethylene glycol (PEG) (DSPE-PEG-RGD) micelle is prepared with a DSPE hydrophobic core to load the photothermal therapy (PTT) dye IR780 and the calcium efflux pump inhibitor curcumin. Then, the MNAzyme is distributed into the hydrophilic PEG layer and sealed with calcium phosphate through biomineralization. Moreover, RGD is attached to the outer tail of PEG for tumor targeting. The constructed nanomachine can release MNAzyme and the cofactor Ca2+ under acidic conditions and self-assemble into an active mode to cleave heat shock protein (HSP) mRNA by consuming the oncogene miRNA-21. Silencing miRNA-21 enhances the expression of the tumor suppressor gene PTEN, leading to PTT sensitization. Meanwhile, curcumin maintains high intracellular Ca2+ to further suppress HSP-chaperone ATP by disrupting mitochondrial Ca2+ homeostasis. Therefore, pancreatic cancer is triple-sensitized to IR780-mediated PTT. The in vitro and in vivo results show that the MNAzyme-based nanomachine can strongly regulate HSP and PTEN expression and lead to significant pancreatic tumor inhibition under laser irradiation