Multifunctional Composite Nanosystems for Precise/Enhanced Sonodynamic Oxidative Tumor Treatment.

Ultrasound-activated therapies have been regarded as the efficient strategy for tumor treatment, among which sonosensitizer-enabled sonodynamic oxidative tumor therapy features intrinsic advantages as compared to other exogenous trigger-activated dynamic therapies. Nanomedicine-based nanosonosensitizer design has been extensively explored for improving the therapeutic efficacy of sonodynamic therapy (SDT) of tumor. This review focuses on solving two specific issues, i.e., precise and enhanced sonodynamic oxidative tumor treatment, by rationally designing and engineering multifunctional composite nanosonosensitizers. This multifunctional design can augment the therapeutic efficacy of SDT against tumor by either improving the production of reactive oxygen species or inducing the synergistic effect of SDT-based combinatorial therapies. Especially, this multifunctional design is also capable of endowing the nanosonosensitizer with bioimaging functionality, which can effectively guide and monitor the therapeutic procedure of the introduced sonodynamic oxidative tumor treatment. The design principles, underlying material chemistry for constructing multifunctional composite nanosonosensitizers, intrinsic synergistic mechanism, and bioimaging guided/monitored precise SDT are summarized and discussed in detail with the most representative paradigms. Finally, the existing critical issues, available challenges, and potential future developments of this research area are also discussed for promoting the further clinical translations of these multifunctional composite nanosonosensitizers in SDT-based tumor treatment.

Co-delivery of nanoparticle and molecular drug by hollow mesoporous organosilica for tumor-activated and photothermal-augmented chemotherapy of breast cancer.

BACKGROUND: In comparison with traditional therapeutics, it is highly preferable to develop a combinatorial therapeutic modality for nanomedicine and photothermal hyperthermia to achieve safe, efficient, and localized delivery of chemotherapeutic drugs into tumor tissues and exert tumor-activated nanotherapy. Biocompatible organic-inorganic hybrid hollow mesoporous organosilica nanoparticles (HMONs) have shown high performance in molecular imaging and drug delivery as compared to other inorganic nanosystems. Disulfiram (DSF), an alcohol-abuse drug, can act as a chemotherapeutic agent according to its recently reported effectiveness for cancer chemotherapy, whose activity strongly depends on copper ions. RESULTS: In this work, a therapeutic construction with high biosafety and efficiency was proposed and developed for synergistic tumor-activated and photothermal-augmented chemotherapy in breast tumor eradication both in vitro and in vivo. The proposed strategy is based on the employment of HMONs to integrate ultrasmall photothermal CuS particles onto the surface of the organosilica and the molecular drug DSF inside the mesopores and hollow interior. The ultrasmall CuS acted as both photothermal agent under near-infrared (NIR) irradiation for photonic tumor hyperthermia and Cu2+ self-supplier in an acidic tumor microenvironment to activate the nontoxic DSF drug into a highly toxic diethyldithiocarbamate (DTC)-copper complex for enhanced DSF chemotherapy, which effectively achieved a remarkable synergistic in-situ anticancer outcome with minimal side effects. CONCLUSION: This work provides a representative paradigm on the engineering of combinatorial therapeutic nanomedicine with both exogenous response for photonic tumor ablation and endogenous tumor microenvironment-responsive in-situ toxicity activation of a molecular drug (DSF) for augmented tumor chemotherapy.

Ameliorative effect of Albizia chinensis synthesized ZnO-NPs on Mycoplasma pneumoniae infected pneumonia mice model.

BACKGROUND: Mycoplasma pneumoniae (MP) is a common cause of community-acquired pneumonia (CAP) among the children and adults that results upper and lower respiratory tract infections. OBJECTIVE: This study was aimed to inspect the ameliorative action of A. chinensis synthesized ZnONPs against M. pneumoniae infected pneumonia mice model. MATERIALS AND METHODS: ZnO NPs was synthesized from Albizia chinensis bark extract and characterized by UV-Vis spectroscopy, Fourier Transform Infrared (FTIR), Transmission Electron Microscopy (TEM), energy dispersive X-ray (EDX) and atomic force microscope (AFM) analyses. The antibacterial effectual of synthesized ZnONPs were examined against clinical pathogens. The pneumonia was induced to BALB/c mice via injecting the M. pneumoniae and treated with synthesized ZnONPs, followed by the total protein content, total cell counts and inflammatory mediators level was assessed in the BALF of experimental animals. The Histopathological investigation was done in the lung tissues of test animals. RESULTS: The outcomes of this work revealed that the formulated ZnONPs was quasi-spherical, radial and cylindrical; the size was identified as 116.5 ± 27.45 nm in diameter. The in vitro antimicrobial potential of formulated ZnO-NPs displayed noticeable inhibitory capacity against the tested fungal and bacterial strains. The administration of synthesized ZnO-NPs in MP infected mice model has significantly reduced the levels of total protein, inflammatory cells, inflammatory cytokines such as IL-1, IL-6, IL-8, tumour necrosis factor-alpha (TNF-a) and transforming growth factor (TGF). Besides, the histopathological examination of MP infected mice lung tissue showed the cellular arrangements were effectively retained after administration of synthesized ZnO-NPs. CONCLUSION: In conclusion, synthesized ZnO-NPs alleviate pneumonia progression via reducing the level of inflammatory cytokines and inflammatory cells in MP infected mice model.

Construction of Single-Iron-Atom Nanocatalysts for Highly Efficient Catalytic Antibiotics.

Bacterial infection caused by pathogenic bacteria has long been an intractable issue that threatens human health. Herein, the fact that nanocatalysts with single iron atoms anchored in nitrogen-doped amorphous carbon (SAF NCs) can effectively induce peroxidase-like activities in the presence of H2 O2 , generating abundant hydroxyl radicals for highly effective bacterial elimination (e.g., Escherichia coli and Staphylococcus aureus), is reported. In combination with the intrinsic photothermal performance of the nanocatalysts, noticeable bacterial-killing effects are extensively investigated. Especially, the antibacterial mechanism of critical cell membrane destruction induced by SAF NCs is unveiled. Based on the bactericidal properties of SAF NCs, in vivo bacterial infections propagated at wounds by E. coli and S. aureus pathogens can be effectively eradicated, resulting in better wound healing. Collectively, the present study highlights the highly efficient in vitro antibacterial and in vivo anti-infection performances by the single-iron-atom-containing nanocatalysts.

Pharmacokinetic comparison of berberine in rat plasma after oral administration of berberine hydrochloride in normal and post inflammation irritable bowel syndrome rats.

In the present study, post inflammation irritable bowel syndrome (PI-IBS) rats were firstly established by intracolonic instillation of acetic acid with restraint stress. Then the pharmacokinetics of berberine in the rat plasma were compared after oral administration of berberine hydrochloride (25 mg/kg) to normal rats and PI-IBS rats. Quantification of berberine in the rat plasma was achieved by using a sensitive and rapid UPLC-MS/MS method. Plasma samples were collected at 15 different points in time and the pharmacokinetic parameters were analyzed by WinNonlin software. Compared with the normal group, area under the plasma concentration vs. time curve from zero to last sampling time (AUC0-t) and total body clearance (CL/F) in the model group significantly increased or decreased, (2039.49 ± 492.24 vs. 2763.43 ± 203.14; 4999.34 ± 1198.79 vs. 3270.57 ± 58.32) respectively. The results indicated that the pharmacokinetic process of berberine could be altered in PI-IBS pathological conditions.

[Research on bioactive ingredients in rat liver after oral administration of different combinations of Wuji pill].

A L9 (3(4)) orthogonal design table to be used to get nine combinations of extraction of three herbs of Wuji pill: Coptis chinensis, Tetradium ruticarpum and Paeonia lactiflora Pall., and nine extraction of single herbs correspondingly, altogether eighteen combinations. Quantification of five representative bioactive ingredients: berberine, palmatine, evodiamine, rutaecarpine, paeoniflorin in rat liver by ultra high liquid chromatography-tandem mass spectrometry after oral administration at 2 h time point of eighteen combinations. The result shows the bioactive ingredients have different concentrations betweem different combinations and the single herb with the same dosage significantly as well as the same dose combinations. C. chinensis with evodiamine concentration of low and high dose T. ruticarpum was positively correlated. T. ruticarpum with berberine concentration of low dose C. chinensis was negatively correlated and of meddle dose C. chinensis was correlated positively. T. ruticarpum with paeoniflorin concentration of middle dose P. lactiflora was correlated positively. P. lactiflora with palmatine concentration of middle dose C. chinensis was negatively correlated and with evodiamine and rutaecarpine concentration of middle dose T. ruticarpum was negatively correlated. These shows the three single herbs interactions resulted in the differences of each ingredients concentration in rat liver. The orthogonal analysis indicates the combination 12: 6: 6 make the maximum concentration in rat liver.

A Forward Learning Algorithm for Neural Memory Ordinary Differential Equations.

The deep neural network, based on the backpropagation learning algorithm, has achieved tremendous success. However, the backpropagation algorithm is consistently considered biologically implausible. Many efforts have recently been made to address these biological implausibility issues, nevertheless, these methods are tailored to discrete neural network structures. Continuous neural networks are crucial for investigating novel neural network models with more biologically dynamic characteristics and for interpretability of large language models. The neural memory ordinary differential equation (nmODE) is a recently proposed continuous neural network model that exhibits several intriguing properties. In this study, we present a forward-learning algorithm, called nmForwardLA, for nmODE. This algorithm boasts lower computational dimensions and greater efficiency. Compared with the other learning algorithms, experimental results on MNIST, CIFAR10, and CIFAR100 demonstrate its potency.

Spectral Embedding Fusion for Incomplete Multiview Clustering.

Incomplete multiview clustering (IMVC) aims to reveal the underlying structure of incomplete multiview data by partitioning data samples into clusters. Several graph-based methods exhibit a strong ability to explore high-order information among multiple views using low-rank tensor learning. However, spectral embedding fusion of multiple views is ignored in low-rank tensor learning. In addition, addressing missing instances or features is still an intractable problem for most existing IMVC methods. In this paper, we present a unified spectral embedding tensor learning (USETL) framework that integrates the spectral embedding fusion of multiple similarity graphs and spectral embedding tensor learning for IMVC. To remove redundant information from the original incomplete multiview data, spectral embedding fusion is performed by introducing spectral rotations at two different data levels, i.e., the spectral embedding feature level and the clustering indicator level. The aim of introducing spectral embedding tensor learning is to capture consistent and complementary information by seeking high-order correlations among multiple views. The strategy of removing missing instances is adopted to construct multiple similarity graphs for incomplete multiple views. Consequently, this strategy provides an intuitive and feasible way to construct multiple similarity graphs. Extensive experimental results on multiview datasets demonstrate the effectiveness of the two spectral embedding fusion methods within the USETL framework.

FAOT-Net: A 1.5-Stage Framework for 3D Pelvic Lymph Node Detection With Online Candidate Tuning.

Accurate and automatic detection of pelvic lymph nodes in computed tomography (CT) scans is critical for diagnosing lymph node metastasis in colorectal cancer, which in turn plays a crucial role in its staging, treatment planning, surgical guidance, and postoperative follow-up of colorectal cancer. However, achieving high detection sensitivity and specificity poses a challenge due to the small and variable sizes of these nodes, as well as the presence of numerous similar signals within the complex pelvic CT image. To tackle these issues, we propose a 3D feature-aware online-tuning network (FAOT-Net) that introduces a novel 1.5-stage structure to seamlessly integrate detection and refinement via our online candidate tuning process and takes advantage of multi-level information through the tailored feature flow. Furthermore, we redesign the anchor fitting and anchor matching strategies to further improve detection performance in a nearly hyperparameter-free manner. Our framework achieves the FROC score of 52.8 and the sensitivity of 91.7% with 16 false positives per scan on the PLNDataset. Code will be available at: github.com/SCUsomebody/FAOT-Net/.

An artificial intelligence platform for the screening and managing of strabismus.

OBJECTIVES: Considering the escalating incidence of strabismus and its consequential jeopardy to binocular vision, there is an imperative demand for expeditious and precise screening methods. This study was to develop an artificial intelligence (AI) platform in the form of an applet that facilitates the screening and management of strabismus on any mobile device. METHODS: The Visual Transformer (VIT_16_224) was developed using primary gaze photos from two datasets covering different ages. The AI model was evaluated by 5-fold cross-validation set and tested on an independent test set. The diagnostic performance of the AI model was assessed by calculating the Accuracy, Precision, Specificity, Sensitivity, F1-Score and Area Under the Curve (AUC). RESULTS: A total of 6194 photos with corneal light-reflection (with 2938 Exotropia, 1415 Esotropia, 739 Vertical Deviation and 1562 Orthotropy) were included. In the internal validation set, the AI model achieved an Accuracy of 0.980, Precision of 0.941, Specificity of 0.979, Sensitivity of 0.958, F1-Score of 0.951 and AUC of 0.994. In the independent test set, the AI model achieved an Accuracy of 0.967, Precision of 0.980, Specificity of 0.970, Sensitivity of 0.960, F1-Score of 0.975 and AUC of 0.993. CONCLUSIONS: Our study presents an advanced AI model for strabismus screening which integrates electronic archives for comprehensive patient histories. Additionally, it includes a patient-physician interaction module for streamlined communication. This innovative platform offers a complete solution for strabismus care, from screening to long-term follow-up, advancing ophthalmology through AI technology for improved patient outcomes and eye care quality.

Grayscale ultrasound feature typing of metastatic ovarian tumors, particularly signet-ring cell carcinoma.

Background: To describe grayscale ultrasound (US) features of metastatic ovarian tumors (MOTs) based on origin of the primary tumor in a large sample size study. Methods: This retrospective cross-sectional single-center study included 112 patients with 190 histopathologically confirmed MOTs. Among the patients, 102 collectively had 144 masses, which were detected via US. The clinical data and static US images of MOTs were collected. Results: The MOTs were mostly bilateral (78.9%) but had a lower rate of bilaterality when detected by US (55.6%). Breast cancer metastasis had the highest nondetection rate (69.6%), because its focal metastasis could only be recognized using histology or immunohistochemistry. The stomach was the most common origin of metastasis (45.3% and 50.7% detected via pathology and US, respectively). The US images were classified into three subtypes: multilocular solid (Type A), purely solid (Type B), and solid with several round or oval cysts (Type C). The MOTs that originated from the colon mostly belonged to Type A (65.1%) and closely mimicked primary epithelial ovarian tumor morphologically. The MOTs that originated from the stomach predominantly belonged to Types B (31.5%) and C (57.5%). Signet-ring cell carcinoma (SRCC) corresponded to Types B and C regardless of origin. Conclusions: The developed novel typing method provides more vivid images for classifying MOTs compared with existing typing methods. Given that no specific sonographic parameters have been established to distinguish MOTs from primary invasive ovarian tumors, these images may be helpful in diagnosing these masses.

Enhancing Robustness of Medical Image Segmentation Model with Neural Memory Ordinary Differential Equation.

Deep neural networks (DNNs) have emerged as a prominent model in medical image segmentation, achieving remarkable advancements in clinical practice. Despite the promising results reported in the literature, the effectiveness of DNNs necessitates substantial quantities of high-quality annotated training data. During experiments, we observe a significant decline in the performance of DNNs on the test set when there exists disruption in the labels of the training dataset, revealing inherent limitations in the robustness of DNNs. In this paper, we find that the neural memory ordinary differential equation (nmODE), a recently proposed model based on ordinary differential equations (ODEs), not only addresses the robustness limitation but also enhances performance when trained by the clean training dataset. However, it is acknowledged that the ODE-based model tends to be less computationally efficient compared to the conventional discrete models due to the multiple function evaluations required by the ODE solver. Recognizing the efficiency limitation of the ODE-based model, we propose a novel approach called the nmODE-based knowledge distillation (nmODE-KD). The proposed method aims to transfer knowledge from the continuous nmODE to a discrete layer, simultaneously enhancing the model's robustness and efficiency. The core concept of nmODE-KD revolves around enforcing the discrete layer to mimic the continuous nmODE by minimizing the KL divergence between them. Experimental results on 18 organs-at-risk segmentation tasks demonstrate that nmODE-KD exhibits improved robustness compared to ODE-based models while also mitigating the efficiency limitation.

An attention-based neural network model for automatic partition of abdominal lymph nodes in CT imaging.

Background: Abdominal lymph node partition is highly relevant to colorectal cancer (CRC) metastasis, which may further affect patient prognosis and survival quality. In the traditional diagnostic process, medical radiologists must partition all lymph nodes from the computed tomography (CT) images for further diagnostics. The manual interpretation of abdominal nodes is experience-dependent and time-consuming, especially for node partition. Therefore, automated partition methods are desirable to make the diagnostic process more accessible. Automatic abdominal lymph node partition is a challenging task due to the subtle morphological features of the nodes and the complex relative position information of the abdominal structure. Methods: In this paper, a node-oriented dataset containing 6,880 nodes with partition labels was constructed by seasoned professionals through 2-round annotation due to there being no dataset with node-oriented labels to perform the partition task. In addition, specific masking strategies and attention mechanisms were proposed for the primary deep neural networks (DNNs). The specific masking strategy could utilize the positional and morphological information more substantially, which intensively exploits prior knowledge and hones the relative positional information in the lower abdomen. The comprehensive attention mechanism could introduce direction-aware information to enhance the inter-channel relationship of features and capture rich contextual relationships with multi-scale kernels. Results: The experiments were based on the node-oriented dataset. The proposed method achieved superior performance [accuracy (ACC): 89.74%; F1 score (F1): 85.95%; area under the curve (AUC): 88.23%], which is significantly higher than the baseline model with several masking strategies (ACC: 62.05-86.16%; F1: 51.77-80.86%; AUC: 60.44-83.94%). For exploration of attention, the proposed method also outperformed the state-of-the-art convolutional block attention module (CBAM; ACC: 88.90%; F1: 84.09%; AUC: 86.86%) with the same proposed input form. Conclusions: Experimental results indicate that the innovative method performs better in experimental metrics than other prevalent methods. The proposed method is expected to be introduced in future medical scenarios, which will help doctors to optimize the diagnosis workflow and improve partition sensitivity.

RECISTSup: Weakly-Supervised Lesion Volume Segmentation Using RECIST Measurement.

Lesion volume segmentation in medical imaging is an effective tool for assessing lesion/tumor sizes and monitoring changes in growth. Since manually segmentation of lesion volume is not only time-consuming but also requires radiological experience, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Although RECIST measurement is coarse compared with voxel-level annotation, it can reflect the lesion's location, length, and width, resulting in a possibility of segmenting lesion volume directly via RECIST measurement. In this study, a novel weakly-supervised method called RECISTSup is proposed to automatically segment lesion volume via RECIST measurement. Based on RECIST measurement, a new RECIST measurement propagation algorithm is proposed to generate pseudo masks, which are then used to train the segmentation networks. Due to the spatial prior knowledge provided by RECIST measurement, two new losses are also designed to make full use of it. In addition, the automatically segmented lesion results are used to supervise the model training iteratively for further improving segmentation performance. A series of experiments are carried out on three datasets to evaluate the proposed method, including ablation experiments, comparison of various methods, annotation cost analyses, visualization of results. Experimental results show that the proposed RECISTSup achieves the state-of-the-art result compared with other weakly-supervised methods. The results also demonstrate that RECIST measurement can produce similar performance to voxel-level annotation while significantly saving the annotation cost.

Tumor Location Causes Different Recurrence Patterns in Remnant Gastric Cancer.

PURPOSE: Tumor recurrence is the principal cause of poor outcomes in remnant gastric cancer (RGC) after resection. We sought to elucidate the recurrent patterns according to tumor locations in RGC. MATERIALS AND METHODS: Data were collected from the Shanghai Cancer Center between January 2006 and December 2020. A total of 129 patients with RGC were included in this study, of whom 62 had carcinomas at the anastomotic site (group A) and 67 at the non-anastomotic site (group N). The clinicopathological characteristics, surgical results, recurrent diseases, and survival were investigated according to tumor location. RESULTS: The time interval from the previous gastrectomy to the current diagnosis was 32.0±13.0 and 21.0±13.4 years in groups A and N, respectively. The previous disease was benign in 51/62 cases (82.3%) in group A and 37/67 cases (55.2%) in group N (P=0.002). Thirty-three patients had documented sites of tumor recurrence through imaging or pathological examinations. The median time to recurrence was 11.0 months (range, 1.0-35.1 months). Peritoneal recurrence occurred in 11.3% (7/62) of the patients in group A versus 1.5% (1/67) of the patients in group N (P=0.006). Hepatic recurrence occurred in 3.2% (2/62) of the patients in group A versus 13.4% (9/67) of the patients in group N (P=0.038). Patients in group A had significantly better overall survival than those in group N (P=0.046). CONCLUSIONS: The tumor location of RGC is an essential factor for predicting recurrence patterns and overall survival. When selecting an optimal postoperative follow-up program for RGC, physicians should consider recurrent features according to the tumor location.

Improving Exploration in Actor-Critic With Weakly Pessimistic Value Estimation and Optimistic Policy Optimization.

Deep off-policy actor-critic algorithms have been successfully applied to challenging tasks in continuous control. However, these methods typically suffer from the poor sample efficiency problem, limiting their widespread adoption in real-world domains. To mitigate this issue, we propose a novel actor-critic algorithm with weakly pessimistic value estimation and optimistic policy optimization (WPVOP) for continuous control. WPVOP integrates two key ingredients: 1) a weakly pessimistic value estimation, which compensates the pessimism of lower confidence bound in conventional value function (i.e., clipped double Q -learning) to trigger exploration in low-value state-action regions and 2) an optimistic policy optimization algorithm by sampling actions that could benefit the policy learning most toward optimal Q -values for efficient exploration. We theoretically analyze that the proposed weakly pessimistic value estimation method is lower and upper bounded, and empirically show that it could avoid extremely over-optimistic value estimates. We show that these two ideas are largely complementary, and can be fruitfully integrated to improve performance and promote sample efficiency of exploration. We evaluate WPVOP on the suite of continuous control tasks from MuJoCo, achieving state-of-the-art sample efficiency and performance.

TeachMe: a web-based teaching system for annotating abdominal lymph nodes.

The detection and characterization of lymph nodes through interpreting abdominal medical images are significant for diagnosing and treating colorectal cancer recurrence. However, interpreting abdominal medical images manually is labor-intensive and time-consuming. The related radiology education has many limitations as well. In this context, we seek to build an extensive collection of abdominal medical images with ground truth labels for lymph nodes recognition research and help junior doctors to train their interpretation skills. Therefore, we develop TeachMe, which is a web-based teaching system for annotating abdominal lymph nodes. The system has a three-level annotation-review workflow to construct an expert database of abdominal lymph nodes and a feedback mechanism helping junior doctors to learn the tricks of interpreting abdominal medical images. TeachMe's functionalities make itself stand out against other platforms. To validate these functionalities, we invite a medical team from Gastrointestinal Surgery Center, West China Hospital, to participate in the data collection workflow and experience the feedback mechanism. With the help of TeachMe, an expert dataset of abdominal lymph nodes has been created and an automated detection model for abdominal lymph nodes with incredible performances has been proposed. Moreover, through three rounds of practicing via TeachMe, our junior doctors' interpretation skills have been improved.

Differential diagnosis of B-mode ultrasound Breast Imaging Reporting and Data System category 3-4a lesions in conjunction with shear-wave elastography using conservative and aggressive approaches.

Background: The high false-positive rates of US Breast Imaging Reporting and Data System (BI-RADS) category 3-4a breast lesions leads to excessive biopsies of many benign lesions, and our aim was to investigate the diagnostic performance achieved by adding a maximum elasticity (Emax) of shear-wave elastography (SWE) to ultrasound (US) to evaluate US BI-RADS category 3-4a breast lesions using conservative and aggressive approaches. We explored the capacity of using this method to avoid unnecessary biopsies without increasing the probability of missing breast cancers. Methods: A total of 123 breast lesions of 120 patients classified as BI-RADS category 3 or 4a were enrolled from January 2019 to December 2019. The US features were evaluated according to the US BI-RADS lexicon. The maximum diameter measured on the US was defined as the size of the lesion. The Emax was assessed by SWE, and the average Emax of breast lesions on two images were calculated and recorded as the final maximum Young's modulus. The diagnostic performance of the combined B-mode US and SWE approach for BI-RADS category 3-4a breast lesions was tested using a conservative approach and an aggressive approach. In the conservative approach, the lesions were downgraded with Emax of 30 kPa or less and upgraded with Emax of 160 kPa or more. In the aggressive approach, the lesions were downgraded with Emax of 80 kPa or less and upgraded with Emax of 160 kPa or more. Pathologic results were defined as the reference standard. Results: Among all 123 breast lesions, there were 60 lesions classified as BI-RADS category 3 and 63 lesions classified as BI-RADS category 4a. Compared to the B-mode US, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC) of the combined B-mode US and SWE with a conservative approach changed from 88.9% to 94.4%, 55.2% to 60.0%, 25.4% to 28.8%, 96.7% to 98.4%, 60.2% to 65.0%, and 0.721 to 0.772, respectively. The specificity, PPV, and accuracy of combined B-mode US and SWE with an aggressive approach increased from 55.2% to 72.4%, 25.4% to 29.3%, and 60.2% to 71.5%, respectively, but this was accompanied with decreases in the sensitivity from 88.9% to 66.7%, the NPV from 96.7% to 92.7%, and the AUC from 0.721 to 0.695. Conclusions: The addition of SWE improves the diagnostic performance of breast US. Adding the diagnostic criteria of SWE to the BI-RADS assessment of B-mode US, downgrading the lesions with Emax 30 kPa or less, and upgrading the lesions with Emax 160 kPa or more helped discriminate low suspicion lesions from benign lesions in order to decrease false-positive findings and avoid missing cancer diagnosis.

Combination of preoperative plasma fibrinogen and neutrophil-to-lymphocyte ratio to predict the prognosis for patients undergoing laparoscopic nephrectomy for renal cell carcinoma.

This study was conducted to investigate the prognostic significance of a combination of fibrinogen and neutrophil-to-lymphocyte ratio (NLR) named the F-NLR score as a novel indicator and further create nomograms for predicting the prognosis of patients with renal cell carcinoma (RCC) treated with laparoscopic nephrectomy. A total of 425 patients with RCC who underwent laparoscopic nephrectomy were included in this study. Then, we divided the patients based on the cut-off values of their F-NLR score into three categories: F-NLR 2 (both high fibrinogen and NLR), F-NLR 0 (both low fibrinogen and NLR), and F-NLR 1 (remaining patients). Cox regression analysis was performed to investigate the predictive performance of the F-NLR score on overall survival (OS) and cancer-specific survival (CSS). Predictive nomograms of F-NLR were established and internally validated. Time-dependent receiver operating characteristic (ROC) curve analysis was performed to assess the predictive accuracy of the nomogram, NLR, and fibrinogen as prognostic markers. The F-NLR 0, 1, and 2 groups included 226 (53.2%), 147 (34.6%), and 52 (12.2%) patients, respectively. Cox regression analysis showed that a high F-NLR score was significantly associated with poor prognosis and acted as an independent prognostic factor for OS and CSS (all P < 0.05). Predictive nomograms with F-NLR for OS (C-index: 0.773) and CSS (C-index: 0.838) were well developed. Time-dependent ROC results showed that nomograms containing F-NLR had better predictive performance than NLR and fibrinogen. F-NLR score was a novel effective prognostic biomarker for patients with RCC undergoing laparoscopic nephrectomy.