Compelling evidences have manifested that breast cancer cells prefer to metastasize to certain distant organs, including brain, lung, bone and liver. According to the canonical “seed and soil” theory, this prominent biological behavior, termed as metastatic organotropism, involves intricate interactions between breast cancer cells (the “seeds”) and specific residents in the tumor microenvironment (the “soil”), initiating from pre-metastatic niche formation to metastatic outgrowth. Recently, multifaceted heterogeneity of tissue-specific macrophages (TSMs) and their roles played in organotropic metastases of breast cancer are incrementally unveiled. Herein, we decipher multiple diversities of TSMs, including evolvement, profiles, functions and metabolic characteristics under different polarization states. Further, we elaborate on bidirectional effects of TSMs on metastatic organotropism of breast cancer (both to the “seeds” and “soil”), and unearth underlying signaling pathways based on updated mechanistic researches. Lastly, we compile a series of clinical trials, hoping to illuminate promising TSM-targeting therapies against breast cancer organotropic metastases.

This study aimed to analyze and subsequently recapitulate the contrast-enhanced ultrasound (CEUS) characteristics in orbital cavernous venous malformation (CVM), exploring the key aspects of differential diagnosis. Several orbital CVM cases (n=56) confirmed by surgery and pathology at Sichuan Provincial People's Hospital (Chengdu, China) were analyzed retrospectively. The conventional ultrasound showed mostly hypoechoic orbital CVM lesions (64.2%), with clear boundaries (98.2%) and quasi-circular (100%) characteristics. However, the blood flow signals were not rich enough, overlapping with other orbital space-occupying lesions to a considerable extent. The CEUS imaging showed a characteristic progressive enhancement pattern of focal nodules in CVM lesions. In conclusion, CEUS may offer certain specificity and positive significance for the preoperative qualitative diagnosis of orbital CVM.

At present, most leuco-dye-doped dosimeters exhibit detection limits for γ-rays in the range of tens to hundreds of Gy, which remains insufficient for numerous industrial and medical applications requiring lower radiation doses. Herein, a novel highly sensitive photonic hydrogel radiochromic film is reported. This is the first fabrication of methacrylic anhydride modified Gelatin (GelMA) based carbon-coated Fe3O4 nanoparticles (Fe3O4@C NPs) film with a magnetic field-induced one-dimension (1-D) periodic structure for γ-irradiation detection. Upon exposure to γ-irradiation, the active vinyl-containing GelMA matrix undergoes cross-linking-induced volume contraction, leading to alterations in the periodic structure of Fe3O4@C NPs and consequent structural color changes. This photonic film exhibits a high sensitivity of radiochromic behavior, displaying clear green change even at a low dosage of 5 Gy γ-ray. This magnetic field-induced construction of the photonic periodic structure is a much simpler method than that of typical opal or inverse opal. In addition, optimized radiochromic behaviors are achieved by choosing appropriate nanoparticles (e.g., sizes or additive amounts).

An adequate amount of theoretical knowledge is a prerequisite for developing hands-on competency in contrast-enhanced ultrasound (CEUS), particularly for novices. Despite the increasing adoption CEUS for liver examinations, there is currently no standardized tool to assess the competency of trainees in terms of both theoretical knowledge and practical cognitive skills, which hinders the objective evaluation of training effectiveness.The purpose of this study is to develop and validate a CEUS competency assessment for the liver (CCAL) and address the critical gap in current training paradigms. To develop and validate a CEUS competency assessment for the liver (CCAL) and address the critical gap in current training paradigms. A four-stage process was conducted as follows: ① Item Generation: The initial items were derived from a literature review and clinical guidelines. ② Qualitative Refinement: The items underwent three rounds of Delphi expert consultation (n=21 experts) and cognitive testing (n=10 trainees) for content validity and clarity. ③ Internal Validation: Psychometric properties (reliability: Cronbach’s αand Guttman split-half; validity: exploratory factor analysis and item difficulty/discrimination) were analyzed using responses from trainees (n=100). ④ External Validation: Generalizability was assessed using data from trainees (n=579) across 25 multicenter sites. The final CCAL was comprised of 66 items across three domains (demographics, self-evaluations, and objective questions), demonstrating high reliability (Cronbach’s α=0.916) and discriminant validity and revealing significant competency gaps in scanning procedures (65.6% pass rate) and image-guided intervention (38.3% pass rate), with objective questions showing balanced difficulty (63.2% easy and 3.5% difficult) and strong discrimination (61.4% good). The CCAL represents the first validated instrument designed for the objective assessment of operator competency in liver CEUS and thereby addresses an essential unmet need in current training paradigms. Furthermore, its implementation holds significant potential to standardize competency-based assessments and improve quality assurance protocols within hepatic imaging programs. Chinese Clinical Trial Registry; ChiCTR2000035750.

Accurate segmentation of thyroid nodules is essential for early screening and diagnosis, but it can be challenging due to the nodules' varying sizes and positions. To address this issue, we propose a multi-attention guided UNet (MAUNet) for thyroid nodule segmentation. We use a multi-scale cross attention (MSCA) module for initial image feature extraction. By integrating interactions between features at different scales, the impact of thyroid nodule shape and size on the segmentation results has been reduced. Additionally, we incorporate a dual attention (DA) module into the skip-connection step of the UNet network, which promotes information exchange and fusion between the encoder and decoder. To test the model's robustness and effectiveness, we conduct the extensive experiments on multi-center ultrasound images provided by 17 local hospitals. The model is trained using the federal learning mechanism to ensure privacy protection. The experimental results show that the Dice scores of the model on the data sets from the three centers are 0.908, 0.912 and 0.887, respectively. Compared to existing methods, our method demonstrates higher generalization ability on multi-center datasets and achieves better segmentation results.

OBJECTIVE:The main aim of this study was to determine whether the use of contrast-enhanced ultrasound (CEUS) could improve the categorization of suspicious breast lesions based on the Breast Imaging Reporting and Data System (BI-RADS), thereby reducing the number of benign breast lesions referred for biopsy. METHODS:This prospective study, conducted between January 2017 and December 2018, enrolled consenting patients from eight teaching hospitals in China, who had been diagnosed with solid breast lesions classified as BI-RADS 4 using conventional ultrasound. CEUS was performed within 1 wk of diagnosis for reclassification of breast lesions. Histopathological results obtained from core needle biopsies or surgical excision samples served as the reference standard. The simulated biopsy rate and cancer-to-biopsy yield were used to compare the accuracy of CEUS and conventional ultrasound (US). RESULTS:Among the 1490 lesions diagnosed as BI-RADS 4 with conventional ultrasound, 486 malignant and 1004 benign lesions were confirmed based on histology. Following CEUS, 2, 395, and 211 lesions were reclassified as CEUS-based BI-RADS 2, 3, and 5, respectively, while 882 (59%) remained as BI-RADS 4. The actual cancer-to-biopsy yield based on US was 32.6%, which increased to 43.4% when CEUS-based BI-RADS 4A was used as the cut-off point to recommend biopsy. The simulated biopsy rate decreased to 73.4%. Overall, in this preselected BI-RADS 4 population, only 2.5% (12/486) of malignant lesions would have been miscategorized as BI-RADS 3 using CEUS-based reclassification. The diagnostic accuracy, sensitivity, and specificity of contrast-enhanced ultrasound reclassification were 57.65%, 97.53%, and 38.35%, respectively. CONCLUSION:Our collective findings indicate that CEUS is a valuable tool in further triage of BI-RADS category 4 lesions and facilitates a reduction in the number of biopsies while increasing the cancer-to-biopsy yield.

Abstract Aims To develop and validate a clinical prediction model to aid radiologist optimize the diagnostic classification of Chinese thyroid imaging reporting and data system (C-TIRADS) . Materials and methods A total of 1,659 patients from two hospitals participated in the study. The derivation set comprised 909 patients for model development and internal validation, while the external validation set included 750 patients. Multinomial logistics regression was used to establish the prediction model. In the derivation set, the ROC curve assessed model performance, and a line chart provided an intuitive visualization. For external validation, ROC and calibration curves were plotted to evaluate discrimination and calibration. Results The data revealed that C-TIRADS classification, abnormal sonogram of cervical lymph nodes, and changes in thyroid nodule size were the most significant predictors of C-TIRADS optimization. The predictive performance of the C-TIRADS optimized nomogram was 0.730 (95%CI 0.697-0.762), with a sensitivity of 63.2%, specificity of 74.9%, and accuracy of 67.7%. A risk threshold of 60% or higher effectively defines C-TIRADS optimization, while a threshold below 30% indicates inefficiency. The calibration curve indicates good consistency, and the clinical decision-making and impact curve demonstrate a favorable net benefit. Moreover, external validation confirms excellent discrimination in predicting C-TIRADS optimization (C index: 0.865, 95%CI 0.839-0.891). Conclusions We established an optimized C-TIRADS model that combines the imaging features of thyroid nodules with clinical risk factors. It may help radiologists to improve the diagnostic efficiency of TIRADS classification.

Objectives This study aims to develop a non-invasive assessment in breast cancer sentinel lymph node (SLN) using deep learning. Materials and methods Continuously retrospective patients with breast cancer who have undergone both contrast-enhanced ultrasound (CEUS) and two-dimensional ultrasound (TDUS) for sentinel lymph node examination. Those patients were randomly divided into training set, validation set, and internal test set in a ratio of 8:1:1. A Re-parameterization Visual Geometry Group-Convolutional Block Attention Module (RepVGG-CBAM) model was constructed based on the RepVGG network, embedding the CBAM attention mechanism. The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic performance. Results In the test set, the AUC were experts in TDUS, CEUS, and combination ultrasound (CBUS), model in TDUS, CEUS and CBUS were 0.794, 0.806, 0.774, 0.861, 0.851, 0.842 respectively. The difference in AUC between Experts in TDUS (0.794) and Model in TDUS (0.861) was statistically significant (p = 0.043). The difference in AUC between Experts in TDUS (0.794) and Model in CEUS (0.851) was statistically significant (p<0.01). The difference in AUC between Experts in CBUS (0.774) and Model in TDUS (0.861) was statistically significant (p = 0.007). The difference in AUC between Experts in CBUS (0.774) and Model in CEUS (0.851) was statistically significant (p<0.001). Conclusions An algorithm model was developed to determine the SLN metastasis status of breast cancer patients.

Background LncRNA HOXB-AS3 are associated with tumor progression in several types of carcinomas, yet, its possibly biological role in gallbladder carcinoma(GBC) remains unclear. Therefore, this study aimed to investigate the biological function of HOXB-AS3 in GBC. Methods To know the potential function of HOXB-AS3 in gallbladder carcinoma, real-time polymerase chain reaction was used to detected the expression of HOXB-AS3 in gallbladder carcinoma cells. The colony formation assay and cell counting kit-8 assay was performed to measured cell viability. Flow cytometry was to analyse cell apoptosis and cell cycle. Cell invasion and migration were determined by the transwell invasion assay and wound-healing assay. A nude mice xenograft tumor model was performed to investigate the biological function of HOXB-AS3 in vivo. Results The results indicated that HOXB-AS3 was significantly elevated in gallbladder carcinoma tissues and cell lines. We used siHOXB-AS3 to knockdown the expression levels of HOXB-AS3. And knockdown HOXB-AS3 expression depressed gallbladder cancer cell viability and induced cell apoptosis. In addition, the gallbladder carcinoma cell cycle was obviously arrested at the G1 phase. Cell invasion and migration were markedly suppressed following knockdown HOXB-AS3 expression. Furthermore, the features of siHOXB-AS3 in gallbladder cancer cells could be reversed by the ERK1/2 phosphorylation agonist Ro 67–7476. Finally, we confirmed that HOXB-AS3 promoted the growth of transplanted tumors in vivo. Conclusion HOXB-AS3 promoted gallbladder carcinoma cell proliferation, invasion and migration by activating the MEK/ERK signaling pathway. HOXB-AS3 contributed to gallbladder cancer tumorigenesis and metastasis, making it a viable therapeutic target for gallbladder cancer treatment.

The incidence of breast cancer has been increasing in recent years, and ultrasound imaging has shown to be effective in early detection and treatment. Diagnosing breast cancer from ultrasound images with common deep learning methods usually requires a large-scale number of training data with accurate labels, which is challenging in reality. Many clinical images are labeled based on the physicians’ preliminary assessment without the biopsy verification, which leads to coarsely labeled data. Directly utilizing such a coarsely labeled clinical dataset to train a fully supervised model would not bring satisfactory accuracy. In this paper, we propose a joint self-supervised learning and coarse-label supervision learning network to overcome the limitations of the small dataset and coarse labels. Furthermore, we also propose to refine the model with a limited number of data with biopsy verified labels. We conducted extensive experiments on the collected dataset. The results show our method outperforms the state-of-the-art methods.

为实现更准确地鉴别诊断涎腺肿瘤良恶性,避免不必要的穿刺或活检,本研究提出一种基于卷积神经网络(CNN)的涎腺肿瘤常规超声图像分类方法,并结合其超声图像特征,提高了鉴别涎腺肿瘤良恶性的准确率.将984张涎腺肿瘤超声图像分为训练集689张、验证集197张、测试集98张,结果显示训练集中该分类方法的最高准确率为92.43%,测试集中该分类方法鉴别诊断涎腺肿瘤良恶性的曲线下面积、准确率、灵敏度、特异度、阳性预测值、阴性预测值分别为0.863、85.44%、86.67%、86.27%、0.701、0.915,显示出对恶性样本具有较好的学习效果,证实了该模型鉴别诊断涎腺肿瘤良恶性的可行性,以及通过深度学习与人工提取特征图像结合的方法可以获得更高的识别准确率.本研究提出的方法可较高效、准确地对涎腺肿瘤良恶性进行分类,使目标病灶的检出及鉴别诊断更加直接、清晰且客观,辅助提高使用者诊断效率.

Background:Significant differences exist in the classification outcomes for radiologists using ultrasonography-based Breast Imaging Reporting and Data Systems for diagnosing category 3-5 (BI-RADS 3-5) breast nodules, due to a lack of clear and distinguishing image features. Consequently, this retrospective study investigated the improvement of BI-RADS 3-5 classification consistency using a transformer-based computer-aided diagnosis (CAD) model. Methods:Independently, 5 radiologists performed BI-RADS annotations on 21,332 breast ultrasonographic images collected from 3,978 female patients from 20 clinical centers in China. All images were divided into training, validation, testing, and sampling sets. The trained transformer-based CAD model was then used to classify test images, for which sensitivity (SEN), specificity (SPE), accuracy (ACC), area under the curve (AUC), and calibration curve were evaluated. Variations in these metrics among the 5 radiologists were analyzed by referencing BI-RADS classification results for the sampling test set provided by CAD to determine whether classification consistency (the k value), SEN, SPE, and ACC could be improved. Results:After the training set (11,238 images) and validation set (2,996 images) were learned by the CAD model, the classification ACC of the CAD model applied to the test set (7,098 images) was 94.89% in category 3, 96.90% in category 4A, 95.49% in category 4B, 92.28% in category 4C, and 95.45% in category 5 nodules. Based on pathological results, the AUC of the CAD model was 0.924 and the predicted probability of CAD was a little higher than the actual probability in the calibration curve. After referencing BI-RADS classification results, the adjustments were made to 1,583 nodules, of which 905 were classified to a lower category and 678 to a higher category in the sampling test set. As a result, the ACC (72.41-82.65%), SEN (32.73-56.98%), and SPE (82.46-89.26%) of the classification by each radiologist were significantly improved on average, with the consistency (k values) in almost all of them increasing to >0.6. Conclusions:The radiologist's classification consistency was markedly improved with almost all the k values increasing by a value greater than 0.6, and the diagnostic efficiency was also improved by approximately 24% (32.73% to 56.98%) and 7% (82.46% to 89.26%) for SEN and SPE, respectively, of the total classification on average. The transformer-based CAD model can help to improve the radiologist's diagnostic efficacy and consistency with others in the classification of BI-RADS 3-5 nodules.

Abstract Backgrounds: A nomogram model based on clinical and ultrasound features was constructed to explore its clinical application value in predicting thyroid C-TI-RADS classification optimization. Methods: Clinical data and ultrasound imaging data of 1,234 patients with thyroid nodules collected from January 2021 to February 2022 of Sichuan Provincial People's Hospital were retrospectively analyzed.All patients underwent preoperative thyroid ultrasound examination and retained standard ultrasound images, evaluated the thyroid nodule C-TI-RADS classification, using the postoperative pathological results as the "gold standard". Independent predictors of C-TI-RADS classification optimization were selected by univariate and multivariate logstic regression analysis, and a nomogram prediction model(*C-TI-RADS) was constructed.The internal validation of the model was performed by Bootstrap resampling. ROC curve was drawn to evaluate the discrimination of the model, and calibration curve and decision curve were drawn to evaluate the consistency and clinical practicability of the prediction model. Results: C-TI-RADS classification, size and number of thyroid nodules, abnormal cervical lymph node ultrasonography, sex and age were independent factors for predicting C-TI-RADS classification optimization (all P < 0.05).The C index of the nomogram prediction model(*C-TI-RADS) constructed based on the above factors was 0.790 (95%CI: 0.765–0.815).Under the optimal cut-off value, the sensitivity was 70.8%, the specificity was 74.4%, and the accuracy was 72.2%.The calibration curve and decision curve showed good consistency and clinical practicability of the model. Conclusions: Nomogram model has good accuracy in the prediction of thyroid C-TI-RADS classification optimization, and can assist ultrasound physician to modify C-TI-RADS classification, which has potential clinical application value.

Long noncoding RNAs (lncRNAs) are involved in transcriptional regulation, and their deregulation is associated with the development of various human cancers, including prostate cancer (PCa). However, their underlying mechanisms remain unclear. In this study, lncRNAs that interact with DNA and regulate mRNA transcription in PCa were screened and identified to promote PCa development. First, 4195 protein-coding genes (PCGs, mRNAs) were obtained from the The Cancer Genome Atlas (TCGA) database, in which 1148 lncRNAs were differentially expressed in PCa. Then, 44,270 pairs of co-expression relationships were calculated between 612 lncRNAs and 2742 mRNAs, of which 42,596 (96%) were positively correlated. Among the 612 lncRNAs, 392 had the potential to interact with the promoter region to form DNA:DNA:RNA triplexes, from which lncRNA AD000684.2(AC002128.1) was selected for further validation. AC002128.1 was highly expressed in PCa. Furthermore, AD000684.2 positively regulated the expression of the correlated genes. In addition, AD000684.2 formed RNA–DNA triplexes with the promoter region of the regulated genes. Functional assays also demonstrated that lncRNA AD000684.2 promotes cell proliferation and motility, as well as inhibits apoptosis, in PCa cell lines. The results suggest that AD000684.2 could positively regulate the transcription of target genes via triplex structures and serve as a candidate prognostic biomarker and target for new therapies in human PCa.

BACKGROUND:Intra-tumoral heterogeneity (ITH) is a distinguished hallmark of cancer, and cancer stem cells (CSCs) contribute to this malignant characteristic. Therefore, it is of great significance to investigate and even target the regulatory factors driving intra-tumoral stemness. c-Myc is a vital oncogene frequently overexpressed or amplified in various cancer types, including breast cancer. Our previous study indicated its potential association with breast cancer stem cell (BCSC) biomarkers. METHODS:In this research, we performed immunohistochemical (IHC) staining on sixty breast cancer surgical specimens for c-Myc, CD44, CD24, CD133 and ALDH1A1. Then, we analyzed transcriptomic atlas of 1533 patients with breast cancer from public database. RESULTS:IHC staining indicated the positive correlation between c-Myc and BCSC phenotype. Then, we used bioinformatic analysis to interrogate transcriptomics data of 1533 breast cancer specimens and identified an intriguing link among c-Myc, cancer stemness and copper-induced cell death (also known as "cuproptosis"). We screened out cuproptosis-related characteristics that predicts poor clinical outcomes and found that the pro-tumoral cuproptosis-based features were putatively enriched in MYC-targets and showed a significantly positive correlation with cancer stemness. CONCLUSION:In addition to previous reports on its oncogenic roles, c-Myc showed significant correlation to stemness phenotype and copper-induced cell toxicity in breast cancer tissues. Moreover, transcriptomics data demonstrated that pro-tumoral cuproptosis biomarkers had putative positive association with cancer stemness. This research combined clinical samples with large-scale bioinformatic analysis, covered description and deduction, bridged classic oncogenic mechanisms to innovative opportunities, and inspired the development of copper-based nanomaterials in targeting highly heterogeneous tumors.

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped.

PurposeTo develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound.Materials and MethodsA total of 619 participants pathologically diagnosed with invasive breast cancer underwent breast ultrasound examinations were recruited from a multicenter of 17 hospitals in China from August 2016 to August 2017. Conventional ultrasound and SWE features were compared between positive and negative LN metastasis groups. The regression equation, the weighting, and the counting methods were used to predict axillary LN metastasis. The sensitivity, specificity, and the areas under the receiver operating characteristic curve (AUC) were calculated.ResultsA significant difference was found in the Breast Imaging Reporting and Data System (BI-RADS) category, the “stiff rim” sign, minimum elastic modulusof the internal tumor and peritumor region of 3 mm between positive and negative LN groups (p < 0.05 for all). There was no significant difference in the diagnostic performance of the regression equation, the weighting, and the counting methods (p > 0.05 for all). Using the counting method, a 0–4 grade risk stratification system based on the four characteristics was established, which yielded an AUC of 0.656 (95% CI, 0.617–0.693, p < 0.001), a sensitivity of 54.60% (95% CI, 46.9%–62.1%), and a specificity of 68.99% (95% CI, 64.5%–73.3%) in predicting axillary LN metastasis.ConclusionA 0–4 grade risk stratification system was developed based on SWE characteristics and BI-RADS categories, and this system has the potential to predict axillary LN metastases in invasive breast cancer.

Background:Osteosarcoma (OS) is the most common primary bone malignancy in children and adolescents with a high incidence and poor prognosis. Activation of the RAS pathway promotes progression and metastasis of osteosarcoma. RAS has been studied in many different tumors; however, the prognostic value of RAS-associated genes in OS remains unclear. On this basis, we investigated the RAS-related gene signature and explored the intrinsic biological features of OS.Methods:We obtained RNA transcriptome sequencing data and clinical information of osteosarcoma patients from the TARGET database. RAS pathway-related genes were obtained from the KEGG pathway database. Molecular subgroups and risk models were developed using consensus clustering and least absolute shrinkage and selection operator (LASSO) regression, respectively. ESTIMATE algorithm and ssGSEA analysis were used to assess the tumor microenvironment and immune penetrance between the two groups. A comprehensive review of gene ontology (GO) and KEGG analyses revealed inherent biological functional differences between the two groups.Results:The consistent clustering showed stratification of osteosarcoma patients into two subtypes based on RAS-associated genes and provided a robust prediction of prognosis. A risk model further confirmed that RAS-related genes are the best prognostic indicators for OS patients. GO analysis showed that GDP/GTP binding, focal adhesion, cytoskeletal motor activity, and cell-matrix junctions were associated with the RAS-related model group. Furthermore, RAS signaling in osteosarcoma based on KEGG analysis was significantly associated with cancer progression, with immune function and tumor microenvironment particularly affected.Conclusion:We constructed a prognostic model founded on RAS-related gene and demonstrated its predictive ability. Then, furtherly exploration of the molecular mechanisms and immune characteristics proved the role of RAS-related gene in the dysregulation in OS.