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Extremity soft tissue sarcoma (ESTS) is a rare malignant nonepithelial disease, calling for combined modality treatments with surgery to further improve local control rates and long-term survival, especially in patients with multiple local recurrences with or without risk of amputation. In this double-arm, open-label, Phase II clinical trial, we will enroll 30 patients with pathologically confirmed ESTS without nodal involvement or distant metastases. Patients are randomly assigned to the combination treatment group or the radiation monotherapy group. Additionally, tumor and biological samples will be obtained directly before and after neoadjuvant therapy, allowing for studies of immune response and primary drug resistance mechanisms.Clinical Trial Registration: ChiCTR2200060659 (http://www.chictr.org.cn) (ClinicalTrials.gov).

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BACKGROUND: Crohn's disease (CD) is a chronic inflammatory disease of the digestive tract with unknown etiology. It follows a relapse-remission pattern, making disease activity assessment crucial for treatment. Our study aims to evaluate the diagnostic accuracy of various imaging modalities and to validate and compare the International Bowel Ultrasound Segmental Activity Score (IBUS-SAS), the multidetector computed tomography enterography score (MDCTEs), and the simplified endoscopic activity score for Crohn's disease (SES-CD). METHODS: We assessed diagnostic performance using the CD Activity Index (CDAI). We first categorized patients into remission and active groups. For those in the active stage, we further categorized them into mild/moderate and severe activity groups. We used Spearman rank correlation to evaluate the relationships among IBUS-SAS, bowel wall thickness (BWT), Color Doppler imaging signal (CDS), inflammatory fat (i-fat), bowel wall stratification (BWS), and clinical inflammatory indicators. RESULTS: A total of 103 CD patients were evaluated. The IBUS-SAS cut-off for remission and activity was 23.8, with an AUC of 0.923, sensitivity of 91.4%, and specificity of 84.8%. The SES-CD had an AUC of 0.801, sensitivity of 62.9%, and specificity of 84.4% at a cut-off of 4.5. The MDCTEs showed an AUC of 0.855, sensitivity of 77.1%, and specificity of 75.8% for a cut-off of 6.5. The Delong test revealed significant differences in diagnostic efficacy when comparing IBUS-SAS to SES-CD and IBUS-SAS to MDCTEs. In the group of mild or moderate-to-severe active, the IBUS-SAS had an AUC of 0.925, sensitivity of 83.7%, and specificity of 88.9% at a cut-off of 40. The SES-CD exhibited an AUC of 0.850, sensitivity of 90.7%, and specificity of 70.4% at a cut-off of 8.5. MDCTEs showed an AUC of 0.909, sensitivity of 83.7%, and specificity of 85.2% at a cut-off of 8.5. During Delong test, the IBUS-SAS, MDCTEs, and SES-CD showed no significant differences in assessing moderate-to-severe activity. Both IBUS-SAS and ultrasound parameters correlated with certain serum indicators (p < 0.05), although only weakly to moderately (all r < 0.5). CONCLUSION: The IBUS-SAS, MDCTEs and SES-CD can evaluate disease remission/active and mild/moderate-to-severe active in CD, and IBUS-SAS offers the potential to precisely define CD activity.

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OBJECTIVES: To compare the diagnostic accuracy of 3D contrast-enhanced ultrasound (CEUS)/MRI-CEUS fusion imaging with 2D-CEUS in assessing the response of hepatocellular carcinoma (HCC) to locoregional therapies in a multicenter prospective study. MATERIALS AND METHODS: A consecutive series of patients with HCC scheduled for locoregional treatment were enrolled between April 2021 and March 2023. Patients were randomly divided into 3D-CEUS/MRI-CEUS fusion imaging group (3D/fusion group) or 2D-CEUS group (2D group). CEUS was performed 1 week before and 4-6 weeks after locoregional treatment. Contrast-enhanced MRI (CE-MRI) 4-6 weeks after treatment was set as the reference standard. CEUS images were evaluated for the presence or absence of viable tumors. Diagnostic performance criteria, including sensitivity, specificity, accuracy, and area under the curve (AUC), were determined for each modality. RESULTS: A total of 140 patients were included, 70 patients in the 2D group (mean age, 60.2 ± 10.4 years) and 70 patients in the 3D/fusion group (mean age, 59.8 ± 10.6 years). The sensitivity of the 3D/fusion group was 100.0% (95% CI: 75.9, 100.0), higher than that of the 2D group (55.6%, 95% CI: 22.7, 84.7; p = 0.019). The specificity of the 3D/fusion group was 96.3% (95% CI: 86.2, 99.4), which was comparable to that of the 2D group (98.4%, 95% CI: 90.0, 99.9; p = 0.915). The AUC of the 3D/fusion group was 0.98 (95% CI: 0.95, 1.00), higher than that of the 2D group (0.77, 95% CI: 0.56, 0.98; p = 0.020). CONCLUSION: 3D-CEUS/MRI-CEUS fusion imaging exhibits superior diagnostic accuracy in evaluating the treatment response to locoregional therapies for HCC. CLINICAL RELEVANCE STATEMENT: 3D-CEUS/MRI-CEUS fusion imaging can be applied for post-treatment assessment of residual tumors in HCC undergoing locoregional treatment, offering potential benefits in terms of accurate diagnosis and clinical management. KEY POINTS: Evaluating for HCC recurrence following locoregional therapy is important. 3D-CEUS/MRI-CEUS fusion imaging achieved a higher sensitivity than 2D-CEUS in assessing residual tumors after locoregional therapies. 3D-CEUS/MRI-CEUS fusion imaging can help clinicians intervene early in residual HCC lesions after locoregional treatment.

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OBJECTIVES: Ultrasound imaging (USI) is the gold standard in the clinical diagnosis of thyroid diseases. Compared with two-dimensional (2D) USI, three-dimensional (3D) USI could provide more structural information. However, the unstable pressure generated by the hand-hold ultrasound probe scanning can cause tissue deformation, especially in soft tissues such as the thyroid. The deformation is manifested as tissue structure being compressed in 2D USI, which results in structural discontinuity in 3D USI. Furthermore, multiple scans apply pressure in different directions to the tissue, which will cause relative displacement between the 3D images obtained from multiple thyroid scans. METHODS: In this work, we proposed a framework to minimize the influence of the variation of pressure in thyroid 3D USI. To correct pressure artifacts in a single scanning sequence, an adaptive method to smooth the position of the 2D ultrasound (US) image sequence is adopted before performing volumetric reconstruction. To build a whole 3D US image including both sides of the thyroid gland, an iterative closest point (ICP) based registration pipeline is adopted to eliminate the relative displacement caused by different pressure directions. RESULTS: Our proposed method was validated by in vivo experiments, including healthy volunteers and volunteers with thyroid nodules at different grading levels. CONCLUSIONS: The thyroid gland and nodule are rendered intelligently in the whole scanning region to facilitate the observation of 3D USI results by the doctor. This work might make a positive contribution to the clinical diagnosis of diseases of the thyroid or other soft tissues.

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Dynamic contrast-enhanced ultrasound (CEUS) imaging can reflect the microvascular distribution and blood flow perfusion, thereby holding clinical significance in distinguishing between malignant and benign thyroid nodules. Notably, CEUS offers a meticulous visualization of the microvascular distribution surrounding the nodule, leading to an apparent increase in tumor size compared to gray-scale ultrasound (US). In the dual-image obtained, the lesion size enlarged from gray-scale US to CEUS, as the microvascular appeared to be continuously infiltrating the surrounding tissue. Although the infiltrative dilatation of microvasculature remains ambiguous, sonographers believe it may promote the diagnosis of thyroid nodules. We propose a deep learning model designed to emulate the diagnostic reasoning process employed by sonographers. This model integrates the observation of microvascular infiltration on dynamic CEUS, leveraging the additional insights provided by gray-scale US for enhanced diagnostic support. Specifically, temporal projection attention is implemented on time dimension of dynamic CEUS to represent the microvascular perfusion. Additionally, we employ a group of confidence maps with flexible Sigmoid Alpha Functions to aware and describe the infiltrative dilatation process. Moreover, a self-adaptive integration mechanism is introduced to dynamically integrate the assisted gray-scale US and the confidence maps of CEUS for individual patients, ensuring a trustworthy diagnosis of thyroid nodules. In this retrospective study, we collected a thyroid nodule dataset of 282 CEUS videos. The method achieves a superior diagnostic accuracy and sensitivity of 89.52% and 93.75%, respectively. These results suggest that imitating the diagnostic thinking of sonographers, encompassing dynamic microvascular perfusion and infiltrative expansion, proves beneficial for CEUS-based thyroid nodule diagnosis.

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Background: The authors' preclinical study has confirmed that RO adjuvant (composed of TLR 7 agonists [imiquimod/R837] and OX40 agonists) injected into local lesions induces the regression of both primary tumor and distant metastasis. The authors propose to realize local control and exert abscopal effect through an 'R-ISV-RO' in situ strategy plus anti-PD-1 monoclonal antibody in advanced tumors. Methods: This study is a single-center, exploratory, phase II trial to evaluate the efficacy and safety of R-ISV-RO plus anti-PD-1 monoclonal antibody in advanced tumors. 30 patients with one or more measurable extracerebral lesions that are accessible for radiation or injection will be enrolled. The primary endpoint is the objective response rate of target lesions. Discussion/Conclusion: The efficacy and safety of the novel strategy will be further validated through this clinical trial.Clinical trial registration: ChiCTR2100053870 (www.chictr.org.cn/).

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This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) for the preoperative evaluation of axillary lymph node (ALN) metastasis status in patients with a newly diagnosed unifocal breast cancer. A total of 883 eligible patients with breast cancer who underwent preoperative breast and axillary ultrasound were retrospectively enrolled between April 1, 2016, and June 30, 2022. The training cohort comprised 621 patients from Hospital I; the external validation cohorts comprised 112, 87, and 63 patients from Hospitals II, III, and IV, respectively. A DLR signature was created based on the deep learning and handcrafted features, and the DLRN was then developed based on the signature and four independent clinical parameters. The DLRN exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) of 0.914, 0.929, and 0.952 in the three external validation cohorts, respectively. Decision curve and calibration curve analyses demonstrated the favorable clinical value and calibration of the nomogram. In addition, the DLRN outperformed five experienced radiologists in all cohorts. This has the potential to guide appropriate management of the axilla in patients with breast cancer, including avoiding overtreatment.

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OBJECTIVE: To evaluate the alterations in retrobulbar color Doppler imaging (CDI) parameters and retinal/choroidal optical coherence tomography angiography (OCTA) parameters and their association with the clinical activity and severity in thyroid-associated orbitopathy (TAO) patients. METHODS: In this study, the retrobulbar flow parameters including resistance index (RI), Pulsatile Index(PI), peak systolic velocity (PSV) and end diastolic velocity (EDV) in posterior ciliary artery (PCA), central retinal artery (CRA) and ophthalmic artery (OA) were determined by CDI. Moreover, the retina and choroidal vascularity including the superficial vessel density (SVD), deep vessel density (DVD), choroidal thickness (ChT) and choroidal vascularity, including total choroidal area (TCA), luminal area (LA), stromal area (SA) and Choroidal Vascularity Index (CVI), were determined by OCTA. All patients grouped as active TAO and inactive TAO based on Clinical activity score (CAS). We picked the severe eye among the subjects and compared all parameters between two groups. We analyzed the correlations among those parameters. RESULTS: There was a significant difference in CAS score, proptosis value, ChT, LA, CVI between patients with active TAO and inactive TAO. In the active group, PSV and EDV of PCA were significantly higher than the inactive group. On logistic regression analysis, CAS was closely associated with PSV-PCA. On multiple linear regression, proptosis value was closely associated with ChT, LA, SA and CVI. CONCLUSION: Choroidal vascularization and retrobulbar blood flow were concurrently higher in active TAO patients and several variables in choroid circulation was closely related to TAO clinical features.

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Vascular diseases may occur in the upper extremities, and the lesions can span the entire length of the blood vessel. One of the most popular methods to identify vascular disorders is ultrasound Doppler imaging. However, traditional two-dimensional (2D) ultrasound Doppler imaging cannot capture the entire length of a long vessel in one image. Medical professionals often have to painstakingly reconstruct three-dimensional (3D) data using 2D ultrasound images to locate the lesions, especially for large blood vessels. 3D ultrasound Doppler imaging can display the morphological structure of blood vessels and the distribution of lesions more directly, providing a more comprehensive view compared to 2D imaging. In this work, we propose a wide-range 3D volumetric ultrasound Doppler imaging system with dual modality, in which a high-definition camera is adopted to automatically track the movement of the ultrasound transducer, simultaneously capturing a corresponding sequence of 2D ultrasound Doppler images. We conducted experiments on human arms using our proposed system and separately with X-ray computerized tomography (X-CT). The comparison results prove the potential value of our proposed system in the diagnosis of arm vascular diseases.

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Accurate ultrasound (US) image segmentation is crucial for the screening and diagnosis of diseases. However, it faces two significant challenges: 1) pixel-level annotation is a time-consuming and laborious process; 2) the presence of shadow artifacts leads to missing anatomy and ambiguous boundaries, which negatively impact reliable segmentation results. To address these challenges, we propose a novel semi-supervised shadow aware network with boundary refinement (SABR-Net). Specifically, we add shadow imitation regions to the original US, and design shadow-masked transformer blocks to perceive missing anatomy of shadow regions. Shadow-masked transformer block contains an adaptive shadow attention mechanism that introduces an adaptive mask, which is updated automatically to promote the network training. Additionally, we utilize unlabeled US images to train a missing structure inpainting path with shadow-masked transformer, which further facilitates semi-supervised segmentation. Experiments on two public US datasets demonstrate the superior performance of the SABR-Net over other state-of-the-art semi-supervised segmentation methods. In addition, experiments on a private breast US dataset prove that our method has a good generalization to clinical small-scale US datasets.

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In ultrasound-guided liver surgery, the lack of large-scale intraoperative ultrasound images with important anatomical structures remains an obstacle hindering the successful application of AI to ultrasound guidance. In this case, intraoperative ultrasound (iUS) simulation should be conducted from preoperative magnetic resonance (pMR), which not only helps doctors understand the characteristics of iUS in advance, but also expands the iUS dataset from various imaging positions, thereby promoting the automatic iUS analysis in ultrasound guidance. Herein, a novel anatomy preserving generative adversarial network (ApGAN) framework was proposed to generate simulated intraoperative ultrasound (Sim-iUS) of liver with precise structure information from pMR. Specifically, the low-rank factors based bimodal fusion was first established focusing on the effective information of hepatic parenchyma. Then, a deformation field based correction module was introduced to learn and correct the slight structural distortion from surgical operations. Meanwhile, the multiple loss functions were designed to constrain the simulation of the content, structures, and style. Empirical results of clinical data showed that the proposed ApGAN obtained higher Structural Similarity (SSIM) of 0.74 and Fr´echet Inception Distance (FID) of 35.54 compared to existing methods. Furthermore, the average Hausdorff Distance (HD) error of the liver capsule structure was less than 0.25 mm, and the average relative (Euclidean Distance) ED error for polyps was 0.12 mm, indicating the high-level precision of this ApGAN in simulating the anatomical structures and focal areas.

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The security of AI systems has gained significant attention in recent years, particularly in the medical diagnosis field. To develop a secure medical image classification system based on deep neural networks, it is crucial to design effective adversarial attacks that can embed hidden, malicious behaviors into the system. However, designing a unified attack method that can generate imperceptible attack samples with high content similarity and be applied to diverse medical image classification systems is challenging due to the diversity of medical imaging modalities and dimensionalities. Most existing attack methods are designed to attack natural image classification models, which inevitably corrupt the semantics of pixels by applying spatial perturbations. To address this issue, we propose a novel frequency constraint-based adversarial attack method capable of delivering attacks in various medical image classification tasks. Specially, our method introduces a frequency constraint to inject perturbation into high-frequency information while preserving low-frequency information to ensure content similarity. Our experiments include four public medical image datasets, including a 3D CT dataset, a 2D chest X-Ray image dataset, a 2D breast ultrasound dataset, and a 2D thyroid ultrasound dataset, which contain different imaging modalities and dimensionalities. The results demonstrate the superior performance of our model over other state-of-the-art adversarial attack methods for attacking medical image classification tasks on different imaging modalities and dimensionalities.

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PURPOSE: To assess the effect of ultrasound (US) fusion imaging on the clinical diagnostic and therapeutic strategies of focal liver lesions, which are difficult to detect or diagnose by conventional US. METHODS: From November 2019 to June 2022, 71 patients with invisible or undiagnosed focal liver lesions who underwent fusion imaging combining US with CT or MR were included in this retrospective study. The reasons for US fusion imaging were as follows: (1) lesions that were undetectable or inconspicuous on B-mode US; (2) post-ablation lesions that could not be assessed accurately by B-mode US; (3) to evaluate whether the lesions detected by B-mode US that were consistent with those presented on MRI/CT images. RESULTS: Of the 71 cases, 43 cases were single lesions, and 28 cases were multiple lesions. Among the 46 cases which were invisible on conventional US, the display rate of lesions using US-CT/MRI fusion imaging was 30.8%, and that combined with CEUS was 76.9%. US-guided biopsy was performed in 30 patients after the detection and localization determined by fusion imaging, with a positive rate of 73.3%. Six patients with recurrence after ablation therapy were all detected and located accurately after fusion imaging, and 4 of them successfully underwent ablation therapy again. CONCLUSION: Fusion imaging contributes to the understanding of the anatomical relationship between lesion location and blood vessels. Additionally, fusion imaging can improve the diagnostic confidence, be helpful to guide interventional operations, and hence be conducive to clinical therapeutic strategies.

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Dynamic contrast-enhanced ultrasound (CEUS) imaging has been widely applied in lesion detection and characterization, due to its offered real-time observation of microvascular perfusion. Accurate lesion segmentation is of great importance to the quantitative and qualitative perfusion analysis. In this paper, we propose a novel dynamic perfusion representation and aggregation network (DpRAN) for the automatic segmentation of lesions using dynamic CEUS imaging. The core challenge of this work lies in enhancement dynamics modeling of various perfusion areas. Specifically, we divide enhancement features into the two scales: short-range enhancement patterns and long-range evolution tendency. To effectively represent real-time enhancement characteristics and aggregate them in a global view, we introduce the perfusion excitation (PE) gate and cross-attention temporal aggregation (CTA) module, respectively. Different from the common temporal fusion methods, we also introduce an uncertainty estimation strategy to assist the model to locate the critical enhancement point first, in which a relatively distinguished enhancement pattern is displayed. The segmentation performance of our DpRAN method is validated on our collected CEUS datasets of thyroid nodules. We obtain the mean dice coefficient (DSC) and intersection of union (IoU) of 0.794 and 0.676, respectively. Superior performance demonstrates its efficacy to capture distinguished enhancement characteristics for lesion recognition.

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The immunohistochemical index is significant to help the selection of treatment strategy for breast cancer patients. Existing studies that focus on conventional ultrasound features and certain types of immunohistochemistry expressions are limited to correlation exploration, and only few studies have built predictive models. In this study, a Tri-Branch deep learning network is built for prediction of the immunohistochemical HER2 using the hybrid ultrasound data, instead of relying on the invasive and biopsy-based histopathological examination. Specifically, the deep learning model uses the cross-model attention and the interactive learning approaches to obtain the strong complementarity of hybrid data comprising B-mode US, contrast-enhanced ultrasound, and optical flow motion information to enhance accuracy of immunohistochemical HER2 prediction. The proposed prediction model was evaluated using hybrid ultrasound dataset from 335 breast cancer patients. The experimental results indicated that the Tri-Branch model had a high accuracy of 86.23% for HER2 status prediction, and the HER2 status prediction for patients with different pathology grades exhibited some meaningful clinical observations.

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Genetically engineered (GE) bacteria were utilized for developing functional systems upon confinement within a restricted space. Use of natural soft hydrogel such as alginate, gelatin, and agarose, have been investigated as promising approaches to design functional architectures. Nevertheless, a challenge is to develop functional microenvironments that support biofilm-like confinement in a relevant three-dimensional (3D) format for long-term studies. We demonstrate a natural soft hydrogel bioactuator based on alginate core-shell structures (0.25-2 mm core and 50-300 µm shell thickness) that enables 3D microbial colonization upon confinement with high cell density. Specially, our study evaluates the efficiency of bacteria-functional system by recapitulating various GE bacteria which can produce common reporter proteins, to demonstrate their actuator functions as well as dynamic pair-wise interactions. The structural design of the hydrogel can endure continued growth of various bacteria colonies within the confined space for over 10 days. The total amount of cellular biomass upon hydrogel-shell confinement was increased 5-fold compared to conventional techniques without hydrogel-shell. Furthermore, the enzymatic activity increased 3.8-fold and bioluminescence signal by 8-fold compared to the responses from conventional hydrogel systems. The conceptualized platform and our workflow represent a reliable strategy with core-shell structures to develop artificial hydrogel habitats as bacteria-based functional systems for bioactuation.

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Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common malignancy arising from primary liver cancer (PLC). Liver ultrasound (US) has been the main approach for the early screening and differential diagnosis. Since ultrasonic findings of PLC are closely associated with tumor growth pattern, histological morphology, tumor staging, and other factors, ultrasonic imaging findings overlap partially. Besides, qualitative assessment is highly dependent on expertise. To improve the diagnostic objectiveness, we propose a novel transport-based anatomical-functional metric learning (T-AFML) method to quantify imaging similarity of both the gray-scale US and dynamic contrast-enhanced US view. Considering that the hemodynamic changes vary with individuals, we introduce a temporally regularized optimal transport to align the local enhancement patterns automatically. To sufficiently exploit ultrasonic findings similarity from different modalities, a selector-based metric integration mechanism is adopted to adaptively select a dominant modality accounting for the similarity measure. In this retrospective study, we collected a total of 174 liver cancer patients consists of 105 HCC and 69 ICC, and our method achieves the superior diagnostic accuracy and sensitivity of 88.41% and 86.16%, respectively, demonstrating its efficacy in quantifying multi-modal ultrasonic findings similarity for PLC diagnosis.

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Synthetic biology has shown remarkable potential to program living microorganisms for applications. However, a notable discrepancy exists between the current engineering practice-which focuses predominantly on planktonic cells-and the ubiquitous observation of microbes in nature that constantly alternate their lifestyles on environmental variations. Here we present the de novo construction of a synthetic genetic program that regulates bacterial life cycle and enables phase-specific gene expression. The program is orthogonal, harnessing an engineered protein from 45 candidates as the biofilm matrix building block. It is also highly controllable, allowing directed biofilm assembly and decomposition as well as responsive autonomous planktonic-biofilm phase transition. Coupling to synthesis modules, it is further programmable for various functional realizations that conjugate phase-specific biomolecular production with lifestyle alteration. This work establishes a versatile platform for microbial engineering across physiological regimes, thereby shedding light on a promising path for gene circuit applications in complex contexts.

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Objective: The aim of this study is to identify prognostic imaging biomarkers and create a radiogenomics nomogram to predict overall survival (OS) in gastric cancer (GC). Material: RNA sequencing data from 407 patients with GC and contrast-enhanced computed tomography (CECT) imaging data from 46 patients obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were utilized to identify radiogenomics biomarkers. A total of 392 patients with CECT images from the Nanfang Hospital database were obtained to create and validate a radiogenomics nomogram based on the biomarkers. Methods: The prognostic imaging features that correlated with the prognostic gene modules (selected by weighted gene coexpression network analysis) were identified as imaging biomarkers. A nomogram that integrated the radiomics score and clinicopathological factors was created and validated in the Nanfang Hospital database. Nomogram discrimination, calibration, and clinical usefulness were evaluated. Results: Three prognostic imaging biomarkers were identified and had a strong correlation with four prognostic gene modules (P < 0.05, FDR < 0.05). The radiogenomics nomogram (AUC = 0.838) resulted in better performance of the survival prediction than that of the TNM staging system (AUC = 0.765, P = 0.011; Delong et al.). In addition, the radiogenomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. Conclusions: The novel prognostic radiogenomics nomogram that was constructed achieved excellent correlation with prognosis in both the training and validation cohort of Nanfang Hospital patients with GC. It is anticipated that this work may assist in clinical preferential treatment decisions and promote the process of precision theranostics in the future.