A cutting-edge deep learning-and-radiomics-based ultrasound nomogram for precise prediction of axillary lymph node metastasis in breast cancer patients ≥ 75 years.

Objective: The objective of this study was to develop a deep learning-and-radiomics-based ultrasound nomogram for the evaluation of axillary lymph node (ALN) metastasis risk in breast cancer patients ≥ 75 years. Methods: The study enrolled breast cancer patients ≥ 75 years who underwent either sentinel lymph node biopsy or ALN dissection at Fudan University Shanghai Cancer Center. DenseNet-201 was employed as the base model, and it was trained using the Adam optimizer and cross-entropy loss function to extract deep learning (DL) features from ultrasound images. Additionally, radiomics features were extracted from ultrasound images utilizing the Pyradiomics tool, and a Rad-Score (RS) was calculated employing the Lasso regression algorithm. A stepwise multivariable logistic regression analysis was conducted in the training set to establish a prediction model for lymph node metastasis, which was subsequently validated in the validation set. Evaluation metrics included area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. The calibration of the model's performance and its clinical prediction accuracy were assessed using calibration curves and decision curves respectively. Furthermore, integrated discrimination improvement and net reclassification improvement were utilized to quantify enhancements in RS. Results: Histological grade, axillary ultrasound, and RS were identified as independent risk factors for predicting lymph node metastasis. The integration of the RS into the clinical prediction model significantly improved its predictive performance, with an AUC of 0.937 in the training set, surpassing both the clinical model and the RS model alone. In the validation set, the integrated model also outperformed other models with AUCs of 0.906, 0.744, and 0.890 for the integrated model, clinical model, and RS model respectively. Experimental results demonstrated that this study's integrated prediction model could enhance both accuracy and generalizability. Conclusion: The DL and radiomics-based model exhibited remarkable accuracy and reliability in predicting ALN status among breast cancer patients ≥ 75 years, thereby contributing to the enhancement of personalized treatment strategies' efficacy and improvement of patients' quality of life.

Site-specific therapy guided by a 90-gene expression assay versus empirical chemotherapy in patients with cancer of unknown primary (Fudan CUP-001): a randomised controlled trial.

BACKGROUND: Empirical chemotherapy remains the standard of care in patients with unfavourable cancer of unknown primary (CUP). Gene-expression profiling assays have been developed to identify the tissue of origin in patients with CUP; however, their clinical benefit has not yet been demonstrated. We aimed to evaluate the efficacy and safety of site-specific therapy directed by a 90-gene expression assay compared with empirical chemotherapy in patients with CUP. METHODS: This randomised controlled trial was conducted at Fudan University Shanghai Cancer Center (Shanghai, China). We enrolled patients aged 18-75 years, with previously untreated CUP (histologically confirmed metastatic adenocarcinoma, squamous cell carcinoma, poorly differentiated carcinoma, or poorly differentiated neoplasms) and an Eastern Cooperative Oncology Group (ECOG) performance status of 0-2, who were not amenable to local radical treatment. Patients were randomly assigned (1:1) by the Pocock and Simon minimisation method to receive either site-specific therapy or empirical chemotherapy (taxane [175 mg/m2 by intravenous infusion on day 1] plus platinum [cisplatin 75 mg/m2 or carboplatin area under the curve 5 by intravenous infusion on day 1], or gemcitabine [1000 mg/m2 by intravenous infusion on days 1 and 8] plus platinum [same as above]). The minimisation factors were ECOG performance status and the extent of the disease. Clinicians and patients were not masked to interventions. The tumour origin in the site-specific therapy group was predicted by the 90-gene expression assay and treatments were administered accordingly. The primary endpoint was progression-free survival in the intention-to-treat population. The trial has been completed and the analysis is final. This study is registered with ClinicalTrials.gov (NCT03278600). FINDINGS: Between Sept 18, 2017, and March 18, 2021, 182 patients (105 [58%] male, 77 [42%] female) were randomly assigned to receive site-specific therapy (n=91) or empirical chemotherapy (n=91). The five most commonly predicted tissues of origin in the site-specific therapy group were gastro-oesophagus (14 [15%]), lung (12 [13%]), ovary (11 [12%]), cervix (11 [12%]), and breast (nine [10%]). At the data cutoff date (April 30, 2023), median follow-up was 33·3 months (IQR 30·4-51·0) for the site-specific therapy group and 30·9 months (27·6-35·5) for the empirical chemotherapy group. Median progression-free survival was significantly longer with site-specific therapy than with empirical chemotherapy (9·6 months [95% CI 8·4-11·9] vs 6·6 months [5·5-7·9]; unadjusted hazard ratio 0·68 [95% CI 0·49-0·93]; p=0·017). Among the 167 patients who started planned treatment, 46 (56%) of 82 patients in the site-specific therapy group and 52 (61%) of 85 patients in the empirical chemotherapy group had grade 3 or worse treatment-related adverse events; the most frequent of these in the site-specific therapy and empirical chemotherapy groups were decreased neutrophil count (36 [44%] vs 42 [49%]), decreased white blood cell count (17 [21%] vs 26 [31%]), and anaemia (ten [12%] vs nine [11%]). Treatment-related serious adverse events were reported in five (6%) patients in the site-specific therapy group and two (2%) in the empirical chemotherapy group. No treatment-related deaths were observed. INTERPRETATION: This single-centre randomised trial showed that site-specific therapy guided by the 90-gene expression assay could improve progression-free survival compared with empirical chemotherapy among patients with previously untreated CUP. Site-specific prediction by the 90-gene expression assay might provide more disease information and expand the therapeutic armamentarium in these patients. FUNDING: Clinical Research Plan of Shanghai Hospital Development Center, Program for Shanghai Outstanding Academic Leader, and Shanghai Anticancer Association SOAR PROJECT. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Sonodynamic and sonomechanical effect on cellular stemness and extracellular physicochemical environment to potentiate chemotherapy.

BACKGROUND: Hypoxia-activated prodrug (HAP) is a promising candidate for highly tumor-specific chemotherapy. However, the oxygenation heterogeneity and dense extracellular matrix (ECM) of tumor, as well as the potential resistance to chemotherapy, have severely impeded the resulting overall efficacy of HAP. RESULTS: A HAP potentiating strategy is proposed based on ultrasound responsive nanodroplets (PTP@PLGA), which is composed of protoporphyrin (PpIX), perfluoropropane (PFP) and a typical HAP, tirapazamine (TPZ). The intense vaporization of PFP upon ultrasound irradiation can magnify the sonomechanical effect, which loosens the ECM to promote the penetration of TPZ into the deep hypoxic region. Meanwhile, the PpIX enabled sonodynamic effect can further reduce the oxygen level, thus activating the TPZ in the relatively normoxic region as well. Surprisingly, abovementioned ultrasound effect also results in the downregulation of the stemness of cancer cells, which is highly associated with drug-refractoriness. CONCLUSIONS: This work manifests an ideal example of ultrasound-based nanotechnology for potentiating HAP and also reveals the potential acoustic effect of intervening cancer stem-like cells.

Standardization of ultrasound images across various centers: M2O-DiffGAN bridging the gaps among unpaired multi-domain ultrasound images.

Domain shift problem is commonplace for ultrasound image analysis due to difference imaging setting and diverse medical centers, which lead to poor generalizability of deep learning-based methods. Multi-Source Domain Transformation (MSDT) provides a promising way to tackle the performance degeneration caused by the domain shift, which is more practical and challenging compared to conventional single-source transformation tasks. An effective unsupervised domain combination strategy is highly required to handle multiple domains without annotations. Fidelity and quality of generated images are also important to ensure the accuracy of computer-aided diagnosis. However, existing MSDT approaches underperform in above two areas. In this paper, an efficient domain transformation model named M2O-DiffGAN is introduced to achieve a unified mapping from multiple unlabeled source domains to the target domain. A cycle-consistent "many-to-one" adversarial learning architecture is introduced to model various unlabeled domains jointly. A condition adversarial diffusion process is employed to generate images with high-fidelity, combining an adversarial projector to capture reverse transition probabilities over large step sizes for accelerating sampling. Considering the limited perceptual information of ultrasound images, an ultrasound-specific content loss helps to capture more perceptual features for synthesizing high-quality ultrasound images. Massive comparisons on six clinical datasets covering thyroid, carotid and breast demonstrate the superiority of the M2O-DiffGAN in the performance of bridging the domain gaps and enlarging the generalization of downstream analysis methods compared to state-of-the-art algorithms. It improves the mean MI, Bhattacharyya Coefficient, dice and IoU assessments by 0.390, 0.120, 0.245 and 0.250, presenting promising clinical applications.

Axillary lymph node metastasis in pure mucinous carcinoma of breast: clinicopathologic and ultrasonographic features.

BACKGROUND: The purpose of this research is to study the sonographic and clinicopathologic characteristics that associate with axillary lymph node metastasis (ALNM) for pure mucinous carcinoma of breast (PMBC). METHODS: A total of 176 patients diagnosed as PMBC after surgery were included. According to the status of axillary lymph nodes, all patients were classified into ALNM group (n = 15) and non-ALNM group (n = 161). The clinical factors (patient age, tumor size, location), molecular biomarkers (ER, PR, HER2 and Ki-67) and sonographic features (shape, orientation, margin, echo pattern, posterior acoustic pattern and vascularity) between two groups were analyzed to unclose the clinicopathologic and ultrasonographic characteristics in PMBC with ALNM. RESULTS: The incidence of axillary lymph node metastasis was 8.5% in this study. Tumors located in the outer side of the breast (upper outer quadrant and lower outer quadrant) were more likely to have lymphatic metastasis, and the difference between the two group was significantly (86.7% vs. 60.3%, P = 0.043). ALNM not associated with age (P = 0.437). Although tumor size not associated with ALNM(P = 0.418), the tumor size in ALNM group (32.3 ± 32.7 mm) was bigger than non-ALNM group (25.2 ± 12.8 mm). All the tumors expressed progesterone receptor (PR) positively, and 90% of all expressed estrogen receptor (ER) positively, human epidermal growth factor receptor 2 (HER2) were positive in two cases of non-ALNM group. Ki-67 high expression was observed in 36 tumors in our study (20.5%), and it was higher in ALNM group than non-ALNM group (33.3% vs. 19.3%), but the difference wasn't significantly (P = 0.338). CONCLUSIONS: Tumor location is a significant factor for ALNM in PMBC. Outer side location is more easily for ALNM. With the bigger size and/or Ki-67 higher expression status, the lymphatic metastasis seems more likely to present.

Revealing nitrogenous VX metabolites and the whole-molecule VX metabolism in the urine of guinea pigs.

VX, a well-known organophosphorus nerve agent (OPNA), poses a significant threat to public safety if employed by terrorists. Obtaining complete metabolites is critical to unequivocally confirm its alleged use/exposure and elucidate its whole-molecular metabolism. However, the nitrogenous VX metabolites containing 2-diisopropylaminoethyl moiety from urinary excretion remain unknown. Therefore, this study applied a newly developed untargeted workflow platform to discover and identify them using VX-exposed guinea pigs as animal models. 2-(N,N-diisopropylamino)ethanesulfonic acid (DiPSA) was revealed as a novel nitrogenous VX metabolite in urine, and 2-(Diisopropylaminoethyl) methyl sulfide (DAEMS) was confirmed as another in plasma, indicating that VX metabolism differed between urine and plasma. It is the first report of a nitrogenous VX metabolite in urine and a complete elucidation of the VX metabolic pathway. DiPSA was evaluated as an excellent VX exposure biomarker. The whole-molecule VX metabolism in urine was characterized entirely for the first time via the simultaneous quantification of DiPSA and two known P-based biomarkers. About 52.1% and 32.4% of VX were excreted in urine as P-based and nitrogenous biomarkers within 24 h. These findings provide valuable insights into the unambiguous detection of OPNA exposure/intoxication and human and environmental exposure risk assessment.

Quantitative Analysis of Shear Wave Elastography and US-Guided Diffuse Optical Tomography for Evaluating Biological Characteristics of Breast Cancer.

RATIONALE AND OBJECTIVES: Shear Wave Elastography (SWE) and Ultrasound-guided Diffuse Optical Tomography (US-guided DOT) demonstrate promise in distinguishing between benign and malignant breast lesions. This study aims to assess the feasibility and correlation of SWE and US-guided DOT in evaluating the biological characteristics of breast cancer. MATERIALS AND METHODS: A cohort of 235 breast cancer patients with 238 lesions, scheduled for surgery within one to three days, underwent B-mode ultrasound (US), US-guided DOT, and SWE. Parameters such as Total Hemoglobin Concentration (THC), Maximal Elasticity (Emax), Mean Elasticity (Emean), Standard Deviation of Elasticity (Esd), and Area Ratio were measured. Correlation with post-surgical pathology reports was examined to explore associations between THC, SWE Parameters, and pathology characteristics. RESULTS: Lesions in patient groups with ER-, PR-, HER2 + , high Ki67, LVI+ , and ALN+ exhibited higher THC, Emax, and Esd compared to groups with ER+ , PR+ , HER2-, low Ki67, LVI-, and ALN-. The increase was seen in all grades of IDC-I to -III. THC significantly correlated with Smax (r = 0.340, P < 0.001), Emax (r = 0.339, P < 0.001), Emean (r = 0.201, P = 0.003), and Esd (r = 0.313, P < 0.001). CONCLUSION: US-guided DOT and SWE prove valuable for the quantitative assessment of breast cancer's biological characteristics, with THC positively correlated with Emax, Emean, and Esd.

Deep learning based ultrasound analysis facilitates precise distinction between parotid pleomorphic adenoma and Warthin tumor.

Background: Pleomorphic adenoma (PA), often with the benign-like imaging appearances similar to Warthin tumor (WT), however, is a potentially malignant tumor with a high recurrence rate. It is worse that pathological fine-needle aspiration cytology (FNAC) is difficult to distinguish PA and WT for inexperienced pathologists. This study employed deep learning (DL) technology, which effectively utilized ultrasound images, to provide a reliable approach for discriminating PA from WT. Methods: 488 surgically confirmed patients, including 266 with PA and 222 with WT, were enrolled in this study. Two experienced ultrasound physicians independently evaluated all images to differentiate between PA and WT. The diagnostic performance of preoperative FNAC was also evaluated. During the DL study, all ultrasound images were randomly divided into training (70%), validation (20%), and test (10%) sets. Furthermore, ultrasound images that could not be diagnosed by FNAC were also randomly allocated to training (60%), validation (20%), and test (20%) sets. Five DL models were developed to classify ultrasound images as PA or WT. The robustness of these models was assessed using five-fold cross-validation. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique was employed to visualize the region of interest in the DL models. Results: In Grad-CAM analysis, the DL models accurately identified the mass as the region of interest. The area under the receiver operating characteristic curve (AUROC) of the two ultrasound physicians were 0.351 and 0.598, and FNAC achieved an AUROC of only 0.721. Meanwhile, for DL models, the AUROC value for discriminating between PA and WT in the test set was from 0.828 to 0.908. ResNet50 demonstrated the optimal performance with an AUROC of 0.908, an accuracy of 0.833, a sensitivity of 0.736, and a specificity of 0.904. In the test set of cases that FNAC failed to provide a diagnosis, DenseNet121 demonstrated the optimal performance with an AUROC of 0.897, an accuracy of 0.806, a sensitivity of 0.789, and a specificity of 0.824. Conclusion: For the discrimination of PA and WT, DL models are superior to ultrasound and FNAC, thereby facilitating surgeons in making informed decisions regarding the most appropriate surgical approach.

Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers With Partially Annotated Ultrasound Images.

Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) of breast cancers. In an automatic CAD system, lesion detection is critical for the following diagnosis. However, existing DL-based methods generally require voluminous manually-annotated region of interest (ROI) labels and class labels to train both the lesion detection and diagnosis models. In clinical practice, the ROI labels, i.e. ground truths, may not always be optimal for the classification task due to individual experience of sonologists, resulting in the issue of coarse annotation to limit the diagnosis performance of a CAD model. To address this issue, a novel Two-Stage Detection and Diagnosis Network (TSDDNet) is proposed based on weakly supervised learning to improve diagnostic accuracy of the ultrasound-based CAD for breast cancers. In particular, all the initial ROI-level labels are considered as coarse annotations before model training. In the first training stage, a candidate selection mechanism is then designed to refine manual ROIs in the fully annotated images and generate accurate pseudo-ROIs for the partially annotated images under the guidance of class labels. The training set is updated with more accurate ROI labels for the second training stage. A fusion network is developed to integrate detection network and classification network into a unified end-to-end framework as the final CAD model in the second training stage. A self-distillation strategy is designed on this model for joint optimization to further improves its diagnosis performance. The proposed TSDDNet is evaluated on three B-mode ultrasound datasets, and the experimental results indicate that it achieves the best performance on both lesion detection and diagnosis tasks, suggesting promising application potential.

IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes.

Investigating correlations between radiomic and genomic profiling in breast cancer (BC) molecular subtypes is crucial for understanding disease mechanisms and providing personalized treatment. We present a well-designed radiogenomic framework image-gene-gene set (IMAGGS), which detects multi-way associations in BC subtypes by integrating radiomic and genomic features. Our dataset consists of 721 patients, each of whom has 12 ultrasound (US) images captured from different angles and gene mutation data. To better characterize tumor traits, 12 multi-angle US images are fused using two distinct strategies. Then, we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm, deviating from the prevalent one-to-one relationship pattern observed in previous studies. Key radiomic and genomic features are screened using these associations. In addition, gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC subtypes. We further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and diseases. Taken together, IMAGGS provides a comprehensive characterization for diseases by associating imaging, genes, and gene sets, paving the way for biological interpretation of radiomics and development of targeted therapy.

Ultrasound-Based Nomogram for Predicting the Aggressiveness of Papillary Thyroid Carcinoma in Adolescents and Young Adults.

RATIONALE AND OBJECTIVES: Assessing the aggressiveness of papillary thyroid carcinoma (PTC) preoperatively might play an important role in guiding therapeutic strategy. This study aimed to develop and validate a nomogram that integrated ultrasound (US) features with clinical characteristics to preoperatively predict aggressiveness in adolescents and young adults with PTC. MATERIALS AND METHODS: In this retrospective study, a total of 2373 patients were enrolled and randomly divided into two groups with 1000 bootstrap sampling. The multivariable logistic regression (LR) analysis or least absolute shrinkage and selection operator LASSO regression was applied to select predictive US and clinical characteristics in the training cohort. By incorporating most powerful predictors, two predictive models presented as nomograms were developed, and their performance was assessed with respect to discrimination, calibration, and clinical usefulness. RESULTS: The LR_model that incorporated gender, tumor size, multifocality, US-reported cervical lymph nodes (CLN) status, and calcification demonstrated good discrimination and calibration with an area under curve (AUC), sensitivity and specificity of 0.802 (0.781-0.821), 65.58% (62.61%-68.55%), and 82.31% (79.33%-85.46%), respectively, in the training cohort; and 0.768 (0.736-0.797), 60.04% (55.62%-64.46%), and 83.62% (78.84%-87.71%), respectively, in the validation cohort. Gender, tumor size, orientation, calcification, and US-reported CLN status were combined to build LASSO_model. Compared with LR_model, the LASSO_model yielded a comparable diagnostic performance in both cohorts, the AUC, sensitivity, and specificity were 0.800 (0.780-0.820), 65.29% (62.26%-68.21%), and 81.93% (78.77%-84.91%), respectively, in the training cohort; and 0.763 (0.731-0.792), 59.43% (55.12%-63.93%), and 84.98% (80.89%-89.08%), respectively, in the validation cohort. The decision curve analysis indicated that using the two nomograms to predict the aggressiveness of PTC provided a greater benefit than either the treat-all or treat-none strategy. CONCLUSION: Through these two easy-to-use nomograms, the possibility of the aggressiveness of PTC in adolescents and young adults can be objectively quantified preoperatively. The two nomograms may serve as a useful clinical tool to provide valuable information for clinical decision-making.

Clinical, endoscopic, and pathological analyses of 14 cases of gastric adenocarcinoma of the fundic gland type / 中华内科杂志

Objective:Gastric adenocarcinoma of the fundic gland type (GA-FG) is rare and often occurs in patients who are not infected with Helicobacter pylori. The current study analyzed and summarized the clinical, endoscopic, and pathological features of GA-FG, in an effort to improve its diagnosis. Methods:Patients who were diagnosed with GA-FG and treated with endoscopic submucosal dissection (ESD) resection at the Department of Gastroenterology, First Affiliated Hospital of Zhejiang Chinese Medical University from January 1st 2020 to October 1st 2022 were included in the study. Their clinical manifestations, endoscopic features, pathological immunohistochemistry, and other characteristics were analyzed.Results:A total of 14 patients with GA-FG were included in the study, 5 males and 9 females, with a mean age of 59 years. Most had no substantial clinical manifestations. Twelve patients were H. pylori-negative, all patients underwent ESD resection, and all patients survived during the follow-up period of 13±9 months. Eleven patients had postoperative endoscopic follow-up records, and no recurrence was detected. Fifteen lesions were detected (2 were present in 1 patient). Twelve were located in the upper 1/3 of the stomach, 10 were ≤ 1 cm in diameter, 12 had a morphology of type 0-Ⅱa, 8 had visible discoloration changes, and 12 had visible vasodilation on the surface. Magnified endoscopy and narrow-band imaging indicated that 12 of the lesions had enlarged marginal crypt epithelium, without any obvious microvascular pattern abnormalities and no obvious borderline. After resection the pathological specimens were all without vascular infiltration, and there was no atrophy of the mucosa at the edge of the lesion. In immunohistochemistry analyses MUC-2 was negative in all cases. MUC5AC was negative in 11 cases, MUC-6 was positive in all cases, and Ki-67 was ≤ 5% in 12 cases. Conclusions:GA-FG is a newly identified type of gastric cancer with low malignancy and a good prognosis. Characteristic discoloration and surface dilated vessels are often evident endoscopically. Enlarged marginal crypt epithelium and no visible boundary lines are often apparent in magnification endoscopy and narrow band imaging.

Lycium barbarian seed oil activates Nrf2/ARE pathway to reduce oxidative damage in testis of subacute aging rats / 中国药理学通报

Aim To explore the effects of Lycium berry seed oil on Nrf2/ARE pathway and oxidative damage in testis of subacute aging rats. Methods Fifty out of 60 male SD rats, aged 8 weeks, were subcutaneously injected with 125 mg • kg"D-galactosidase in the neck for 8 weeks to establish a subacute senescent rat model. The presence of senescent cells was observed using P-galactosidase ((3-gal), while testicular morphology was examined using HE staining. Serum levels of testosterone (testosterone, T), follicle-stimulating hormone ( follicle stimulating hormone, FSH ) , luteinizing hormone ( luteinizing hormone, LH ) , superoxide dis-mutase ( superoxide dismutase, SOD ) , glutathione ( glutathione, GSH) and malondialdehyde ( malondial-dehyde, MDA) were measured through ELISA, and the expressions of factors related to aging, oxidative damage, and the Nrf2/ARE pathway were assessed via immunohistochemical analysis and Western blotting. Results After successfully identifying the model, the morphology of the testis was improved and the intervention of Lycium seed oil led to a down-regulation in the expression of [3-gal and -yH2AX. The serum levels of SOD, GSH, T, and FSH increased while MDA and LH decreased (P 0. 05) . Additionally, there was an up-regulated expression of Nrf2, GCLC, NQOl, and SOD2 proteins in testicular tissue ( P 0. 05 ) and nuclear expression of Nrf2 in sertoli cells. Conclusion Lycium barbarum seed oil may reduce oxidative damage in testes of subacute senescent rats by activating the Nrf2/ARE signaling pathway.

Discriminative detection of soman or VX exposure using europium chelated microparticle-based immunofluorescence microfluidic chip.

The retrospective detection of organophosphorus nerve agents (OPNAs) exposure has been achieved by the off-site analysis of OPNA-human serum albumin (HSA) adducts using mass spectrometry-based detection approaches. However, few specific methods are accessible for on-site detection. To address this, a novel immunofluorescence microfluidic chip (IFMC) testing system combining europium chelated microparticle (EuCM) with self-driven microfluidic chip assay has been established to unambiguously determine soman (GD) and VX exposure within 20 min, respectively. The detection system was based on the principle of indirect competitive enzyme-linked immunosorbent assay. The specific monoclonal antibodies that respectively recognized the phosphonylated tyrosine 411 of GD-HSA and VX-HSA adducts were labeled by EuCM to capture corresponding adducts in the exposed samples. The phosphonylated peptides in the test line and goat-anti-rabbit antibody in the control line were utilized to bind the EuCM-labeled antibodies for signal exhibition. The developed IFMC chip could discriminatively detect exposed HSA adducts with high specificity, demonstrating a low limit of detection at exposure concentrations of 0.5 × 10-6 mol/L VX and 1.0 × 10-6 mol/L GD. The exposed serum samples can be qualitatively detected following an additional pretreatment procedure. This is a novel rapid detection system capable of discriminating GD and VX exposure, providing an alternative method for rapidly identifying OPNA exposure.

Application of ultrasound-based radiomics models of breast masses to predict invasive components of encapsulated papillary carcinoma.

Background: Axillary lymph node (ALN) metastasis is seen in encapsulated papillary carcinoma (EPC), mostly with an invasive component (INV). Radiomics can offer more information beyond subjective grayscale and color Doppler ultrasound (US) image interpretation. This study aimed to develop radiomics models for predicting an INV of EPC in the breast based on US images. Methods: This study retrospectively enrolled 105 patients (107 masses) with a pathological diagnosis of EPC from January 2016 to April 2021, and all masses had preoperative US images. Of the 107 masses, 64 were randomized to a training set and 43 to a test set. US and clinical features were analyzed to identify features associated with INVs. Then, based on the manually segmented US images to obtain radiomics features, the models to predict INVs were built with 5 ensemble machine learning classifiers. We estimated the performance of the predictive models using accuracy, the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity. Results: The mean age was 63.71 years (range, 31 to 85 years); the mean size of tumors was 23.40 mm (range, 9 to 120 mm). Among all clinical and US features, only shape was statistically different between EPC with INVs and those without (P<0.05). In this study, the models based on Random Under Sampling (RUS) Boost, Random Forest, XGBoost, AdaBoost, and Easy Ensemble methods had good performance, among which RUS Boost had the best performance with an AUC of 0.875 [95% confidence interval (CI): 0.750-0.974] in the test set. Conclusions: Radiomics prediction models were effective in predicting the INV of EPC, whereas clinical and US features demonstrated relatively decreased predictive utility.

Artificial intelligence in breast imaging: potentials and challenges.

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

A protein standard absolute quantification strategy for enhanced absolute quantification of ricin in complex matrices using in vitro synthesized mutant holoprotein as internal standard by ultra-high-performance liquid chromatography-tandem mass spectrometry.

Ricin is a highly toxic protein toxin that poses a potential bioterrorism threat due to its potency and widespread availability. However, the accurate quantification of ricin through absolute mass spectrometry (MS) using a protein standard absolute quantification (PSAQ) strategy is not widely practiced. This limitation primarily arises from the presence of interchain disulfide bonds, which hinder the production of full-length isotope-labeled ricin as an internal standard (IS) in vitro. In this study, we have developed a novel approach for the absolute quantification of ricin in complex matrices using recombinant single-chain and full-length mutant ricin as the protein IS, instead of isotope-labeled ricin, in conjunction with ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). The amino acid sequence of the ricin mutant internal standard (RMIS) was designed by introducing site mutations in specific amino acids of trypsin/Glu-C enzymatic digestion marker peptides of ricin. To simplify protein expression, the A-chain and B-chain of RMIS were directly linked to replace the original interchain disulfide bonds. The RMISs were synthesized using an Escherichia coli expression system. An appropriate RMIS was selected as the protein IS based on consistent digestion efficiency, UHPLC-MS/MS behavior, antibody recognition function, lectin activity, and proper depurination activity with intact ricin. The RMIS was utilized to simultaneously quantify A- and B-chain marker peptides of ricin through UHPLC-MS/MS. This method was thoroughly validated using a milk matrix. By employing internal protein standards, this quantitative strategy overcomes the challenges posed by variations in extraction recoveries, matrix effects, and digestion efficiency encountered when working with different matrices. Consequently, calibration curves generated from milk matrix-spiked samples were utilized to accurately and precisely quantify ricin in river water and plasma samples. Moreover, the established method successfully detected intact ricin in samples obtained from the sixth Organization for the Prohibition of Chemical Weapons (OPCW) exercise on biotoxin analysis. This study presents a novel PSAQ strategy that enables the accurate quantification of ricin in complex matrices.

Unraveling the Pivotal Network of Ultrasound and Somatic Mutations in Triple-Negative and Non-Triple-Negative Breast Cancer.

Purpose: The emergence of genomic targeted therapy has improved the prospects of treatment for breast cancer (BC). However, genetic testing relies on invasive and sophisticated procedures. Patients and Methods: Here, we performed ultrasound (US) and target sequencing to unravel the possible association between US radiomics features and somatic mutations in TNBC (n=83) and non-TNBC (n=83) patients. Least absolute shrinkage and selection operator (Lasso) were utilized to perform radiomic feature selection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was utilized to identify the signaling pathways associated with radiomic features. Results: Thirteen differently represented radiomic features were identified in TNBC and non-TNBC, including tumor shape, textual, and intensity features. The US radiomic-gene pairs were differently exhibited between TNBC and non-TNBC. Further investigation with KEGG verified radiomic-pathway (ie, JAK-STAT, MAPK, Ras, Wnt, microRNAs in cancer, PI3K-Akt) associations in TNBC and non-TNBC. Conclusion: The pivotal network provided the connections of US radiogenomic signature and target sequencing for non-invasive genetic assessment of precise BC treatment.

Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.

The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy.

HAL-IA: A Hybrid Active Learning framework using Interactive Annotation for medical image segmentation.

High performance of deep learning models on medical image segmentation greatly relies on large amount of pixel-wise annotated data, yet annotations are costly to collect. How to obtain high accuracy segmentation labels of medical images with limited cost (e.g. time) becomes an urgent problem. Active learning can reduce the annotation cost of image segmentation, but it faces three challenges: the cold start problem, an effective sample selection strategy for segmentation task and the burden of manual annotation. In this work, we propose a Hybrid Active Learning framework using Interactive Annotation (HAL-IA) for medical image segmentation, which reduces the annotation cost both in decreasing the amount of the annotated images and simplifying the annotation process. Specifically, we propose a novel hybrid sample selection strategy to select the most valuable samples for segmentation model performance improvement. This strategy combines pixel entropy, regional consistency and image diversity to ensure that the selected samples have high uncertainty and diversity. In addition, we propose a warm-start initialization strategy to build the initial annotated dataset to avoid the cold-start problem. To simplify the manual annotation process, we propose an interactive annotation module with suggested superpixels to obtain pixel-wise label with several clicks. We validate our proposed framework with extensive segmentation experiments on four medical image datasets. Experimental results showed that the proposed framework achieves high accuracy pixel-wise annotations and models with less labeled data and fewer interactions, outperforming other state-of-the-art methods. Our method can help physicians efficiently obtain accurate medical image segmentation results for clinical analysis and diagnosis.