The normal value and influencing factors of shear wave elastography in healthy tibial nerves: A cross-sectional study.

Background and Aims: Shear wave elastography is a potential method for evaluating peripheral neuropathy, but lacking reference values. The aim of this study was to measure tibial nerve stiffness in healthy individuals using shear wave elastography and to investigate the influencing factors of tibial nerve stiffness. Methods: Shear wave elastography of bilateral tibial nerves was performed in 50 healthy individuals 4 cm proximal to the medial malleolus. Mean shear modulus data of tibial nerves were obtained and recorded. Intra- and interobserver agreement were assessed using intraclass correlation coefficients. Differences among groups (grouped by laterality, sex, age, and body mass index) were analyzed with independent-samples t-tests and paired t-tests. Effect size (Cohen's d) was also calculated. Results: The intra-and interobserver agreement were moderate (intraclass correlation coefficient, 0.700-0.747) for all participants, and was poor (intraclass correlation coefficient, 0.265-0.088) in very thin people (body mass index <18.5 kg/m2). The shear wave elastography measurements of the tibial nerve did not show a significant difference between legs, sexes, or different age groups. Higher values of tibial nerve stiffness were found in thinner participants. Conclusions: Shear wave elastography is a method to evaluate the stiffness of peripheral nerves. The measurement results were likely influenced by body mass index of the participants.

HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images.

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. Then, a precise diagnose using breast ultrasound (BUS) image would be significant useful. Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define region of interest (ROI) and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and three vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance (GDPH&SYSUCC AUC: 0.924, ACC: 0.893, Spec: 0.836, Sens: 0.926) with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given (GDPH&SYSUCC-AUC ours: 0.924 vs. reader1: 0.825 vs. reader2: 0.820).

A new discriminant strategy combined with four TIRADS screening procedures increases ultrasound diagnostic accuracy-focusing on "wrong diagnostic" thyroid nodules.

OBJECTIVE: To utilize the discrepancies of different TIRADS, including ACR-TIRADS, Kwak-TIRADS, C-TIRADS, and EU-TIRADS, to explore methods for improving ultrasound diagnostic accuracy. METHODS: In total, 795 nodules with cytological or surgical pathology were included. All nodules were screened by the four TIRADS according to their diagnostic concordance (Screening procedures, SP). Discriminant strategy (DS) derived from predictor variables was combined with SP to construct the evaluation method (SP+DS). The diagnostic performance of the SP+DS method alone and its derivational methods and two-TIRADS combined tests was evaluated. RESULTS: A total of 86.8% (269/310) malignant nodules and 93.6% (365/390) benign cases diagnosed by the four TIRADS simultaneously were pathologically confirmed, while 12.0% (95/795) nodules could not be consistently diagnosed by them. The criteria of DS were that iso- or hyper-echogenicity nodules should be considered benign, while hypo- or marked hypo-echogenicity nodules malignant. For 95 inconsistently diagnosed nodules screened by at least two TIRADS, DS performed best with an accuracy of 79.0%, followed by Kwak-TIRADS (72.6%). In the overall sample, the sensitivity and AUC were highest for the SP+DS method compared to the four TIRADS (91.3%, 0.895). Combining ACR-TIRADS and Kwak-TIRADS via parallel test resulted in significant improvements in the sensitivity and AUC compared to ACR-TIRADS (89.2% vs. 81.4%, 0.889 vs. 0.863). Combining C-TIRADS and DS in serial resulted in the highest AUC (0.887), followed by Kwak-TIRADS (0.884), while EU-TIRADS was the lowest (0.879). CONCLUSIONS: For undetermined or suspected thyroid nodules, two-TIRADS combined tests can be used to improve diagnostic accuracy. Otherwise, considering the inconsistent diagnosis of two TIRADS may require attention to the echo characteristics to differentiate between benign and malignant nodules. KEY POINTS: • The discrepancies in the diagnostic performance of different TIRADS arise from their performance on inconsistently diagnosed nodules. • ACR-TIRADS improves sensitivity via combining with Kwak-TIRADS in parallel (from 81.4 to 89.2%), while C-TIRADS increases specificity via combining with EU-TIRADS in serial (from 80.9 to 85.7%). • If the diagnostic findings of two TIRADS are inconsistent, echo characteristics will be helpful for the differentiation of benign and malignant nodules with an accuracy of 79.0%.

Development and Validation of a Novel Mitochondrion and Ferroptosis-Related Long Non-Coding RNA Prognostic Signature in Hepatocellular Carcinoma.

Mitochondrion and ferroptosis are related to tumorigenesis and tumor progression of hepatocellular carcinoma (HCC). Therefore, this study focused on exploring the participation of lncRNAs in mitochondrial dysfunction and ferroptosis using public datasets from The Cancer Genome Atlas (TCGA) database. We identified the mitochondrion- and ferroptosis-related lncRNAs by Pearson's analysis and lasso-Cox regression. Moreover, real-time quantitative reverse transcription PCR (RT-qPCR) was utilized to further confirm the abnormal expression of these lncRNAs. Based on eight lncRNAs, the MF-related lncRNA prognostic signature (LPS) with outstanding stratification ability and prognostic prediction capability was constructed. In addition, functional enrichment analysis and immune cell infiltration analysis were performed to explore the possible functions of lncRNAs and their impact on the tumor microenvironment. The pathways related to G2M checkpoint and MYC were activated, and the infiltration ratio of regulatory T cells and M0 and M2 macrophages was higher in the high-risk group. In conclusion, these lncRNAs may affect mitochondria functions, ferroptosis, and immune cell infiltration in HCC through specific pathways, which may provide valuable insight into the progression and therapies of HCC.

Performance of a computer aided diagnosis system for SARS-CoV-2 pneumonia based on ultrasound images.

PURPOSE: In this study we aimed to leverage deep learning to develop a computer aided diagnosis (CAD) system toward helping radiologists in the diagnosis of SARS-CoV-2 virus syndrome on Lung ultrasonography (LUS). METHOD: A CAD system is developed based on a transfer learning of a residual network (ResNet) to extract features on LUS and help radiologists to distinguish SARS-CoV-2 virus syndrome from healthy and non-SARS-CoV-2 pneumonia. A publicly available LUS dataset for SARS-CoV-2 virus syndrome consisting of 3909 images has been employed. Six radiologists with different experiences participated in the experiment. A comprehensive LUS data set was constructed and employed to train and verify the proposed method. Several metrics such as accuracy, recall, precision, and F1-score, are used to evaluate the performance of the proposed CAD approach. The performances of the radiologists with and without the help of CAD are also evaluated quantitively. The p-values of the t-test shows that with the help of the CAD system, both junior and senior radiologists significantly improve their diagnosis performance on both balanced and unbalanced datasets. RESULTS: Experimental results indicate the proposed CAD approach and the machine features from it can significantly improve the radiologists' performance in the SARS-CoV-2 virus syndrome diagnosis. With the help of the proposed CAD system, the junior and senior radiologists achieved F1-score values of 91.33% and 95.79% on balanced dataset and 94.20% and 96.43% on unbalanced dataset. The proposed approach is verified on an independent test dataset and reports promising performance. CONCLUSIONS: The proposed CAD system reports promising performance in facilitating radiologists' diagnosis SARS-CoV-2 virus syndrome and might assist the development of a fast, accessible screening method for pulmonary diseases.

Using Ultrasound-Based Multilayer Perceptron to Differentiate Early Breast Mucinous Cancer and its Subtypes From Fibroadenoma.

OBJECTIVES: Mucinous breast cancer (MBC), particularly pure MBC (pMBC), often tend to be confused with fibroadenoma (FA) due to their similar images and firm masses, so some MBC cases are misdiagnosed to be FA, which may cause poor prognosis. We analyzed the ultrasonic features and aimed to identify the ability of multilayer perceptron (MLP) to classify early MBC and its subtypes and FA. MATERIALS AND METHODS: The study consisted of 193 patients diagnosed with pMBC, mMBC, or FA. The area under curve (AUC) was calculated to assess the effectiveness of age and 10 ultrasound features in differentiating MBC from FA. We used the pairwise comparison to examine the differences among MBC subtypes (pure and mixed types) and FA. We utilized the MLP to differentiate MBC and its subtypes from FA. RESULTS: The nine features with AUCs over 0.5 were as follows: age, echo pattern, shape, orientation, margin, echo rim, vascularity distribution, vascularity grade, and tumor size. In subtype analysis, the significant differences were obtained in 10 variables (p-value range, 0.000-0.037) among pMBC, mMBC, and FA, except posterior feature. Through MLP, the AUCs of predicting MBC and FA were both 0.919; the AUCs of predicting pMBC, mMBC, and FA were 0.875, 0.767, and 0.927, respectively. CONCLUSION: Our study found that the MLP models based on ultrasonic characteristics and age can well distinguish MBC and its subtypes from FA. It may provide a critical insight into MBC preoperative clinical management.

Web-Based Ultrasonic Nomogram Predicts Preoperative Central Lymph Node Metastasis of cN0 Papillary Thyroid Microcarcinoma.

Background: Many clinicians are facing the dilemma about whether they should apply the active surveillance (AS) strategy for managing Clinically Node-negative (cN0) PTMC patients in daily clinical practice. This research plans to construct a dynamic nomogram based on network, connected with ultrasound characteristics and clinical data, to predict the risk of central lymph node metastasis (CLNM) in cN0 PTMC patients before surgery. Methods: A retrospective analysis of 659 patients with cN0 PTMC who had underwent thyroid surgery and central compartment neck dissection. Patients were randomly (2:1) divided into the development cohort (439 patients) and validation cohort (220 patients). The group least absolute shrinkage and selection operator (Group Lasso) regression method was used to select the ultrasonic features for CLNM prediction in the development cohort. These features and clinical data were screened by the multivariable regression analysis, and the CLNM prediction model and web-based calculator were established. Receiver operating characteristic, calibration curve, Clinical impact curve and decision curve analysis (DCA) were used to weigh the performance of the prediction model in the validation set. Results: Multivariable regression analysis showed that age, tumor size, multifocality, the number of contact surface, and real-time elastography were risk factors that could predict CLNM. The area under the curve of the prediction model in the development and validation sets were 0.78 and 0.77, respectively, with good discrimination and calibration. A web-based dynamic calculator was built. DCA proved that the prediction model had excellent net benefits and clinical practicability. Conclusions: The web-based dynamic nomogram incorporating US and clinical features was able to forecast the risk of preoperative CLNM in cN0 PTMC patients, and has good predictive performance. As a new observational indicator, NCS can provide additional predictive information.

Selecting the optimal parameters for sonoporation of pancreatic cancer in a pre-clinical model.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers in the modern world, in part due to poor delivery of chemotherapeutics. Sonoporation can be used to enhance the efficacy of standard of care therapies for PDAC. Using xenograft models of PDAC we investigate sonoporation using four ifferent ultrasound contrast agents (UCAs) and two ultrasound regimens to identify the ideal parameters to increase therapeutic efficacy. MIA-PaCa2 xenografts in over 175 immunodeficient mice were treated with gemcitabine and paclitaxel and subjected to low or high power ultrasound (60 and 200 mW/cm2 respectively) in conjunction with one of four different UCAs. The UCAs investigated were Definity®, SonoVue®, Optison™ or Sonazoid™. Tumor volumes, vascularity, hemoglobin, and oxygenation were measured and compared to controls. High power treatment in conjunction with Sonazoid sonoporation led to significantly smaller tumors when started early (tumors ~50mm3; p = .0105), while no UCAs significantly increased efficacy in the low power cohort. This trend was also found in larger tumors (~250mm3) where all four UCA agents significantly increased therapeutic efficacy in the high power group (p < .01), while only Definity and SonoVue increased efficacy in the low power cohort (p < .03). Overall, the higher power ultrasound treatment modality was more consistently effective at decreasing tumor volume and increasing vascularity characteristics. In conclusion, Sonazoid was the most consistently effective UCA at decreasing tumor volume and increasing vascularity. Thus, we are pursuing a larger phase II clinical trial to validate the increased efficacy of sonoporation in conjunction with chemotherapy in PDAC patients.

Ultrasound-Based Nomogram for Distinguishing Malignant Tumors from Nodular Sclerosing Adenoses in Solid Breast Lesions.

OBJECTIVES: Nodular sclerosing adenoses (NSAs) and malignant tumors (MTs) may coexist and are often classified into the same Breast Imaging Reporting and Data System (BI-RADS) category. We aimed to build and validate an ultrasound-based nomogram to distinguish MT from NSA for building a precise sequence of biopsies. MATERIALS AND METHODS: The training cohort included 156 patients (156 masses) with NSA or MT at one study institution. We used best subset regression to determine the predictors for building a nomogram from ultrasonic characteristics and patients' age. Model performance and clinical utility were evaluated using Brier score, concordance (C)-index, calibration curve, and decision curve analysis. The independent validation cohort consisted of 162 patients (162 masses) from a separate institution. RESULTS: Through best subset regression, we selected 6 predictors to develop nomogram: age, calcification, echogenic rim, vascularity distribution, tumor size, and thickness of breast parenchyma. Brier score and C-index of the nomogram in the training cohort were 0.068 and 0.967 (95% confidence interval [CI]: 0.941-0.993), respectively. In addition, calibration curve demonstrated good agreement between prediction and pathological result. In the validation cohort, the nomogram still obtained a favorable C-index score of 0.951 (95% CI: 0.919-0.983) and fine calibration. Decision curve analysis showed that the model was clinically useful. CONCLUSIONS: If multiple NSA and MT masses are present in the same patient and are classified into the same BI-RADS category, our nomogram can be used as a supplement to the BI-RADS category for accurate biopsy of the mass most likely to be MT.

Real-Time Elastography: A Web-Based Nomogram Improves the Preoperative Prediction of Central Lymph Node Metastasis in cN0 PTC.

BACKGROUND: Given the difficulty of accurately determining the central lymph node metastasis (CLNM) status of patients with clinically node-negative (cN0) papillary thyroid carcinoma (PTC) before surgery, this study aims to combine real-time elastography (RTE) and conventional ultrasound (US) features with clinical features. The information is combined to construct and verify the nomogram to foresee the risk of CLNM in patients with cN0 PTC and to develop a network-based nomogram. METHODS: From January 2018 to February 2020, 1,157 consecutive cases of cN0 PTC after thyroidectomy and central compartment neck dissection were retrospectively analyzed. The patients were indiscriminately allocated (2:1) to a training cohort (771 patients) and validation cohort (386 patients). Multivariate logistic regression analysis of US characteristics and clinical information in the training cohort was performed to screen for CLNM risk predictors. RTE data were included to construct prediction model 1 but were excluded when constructing model 2. DeLong's test was used to select a forecast model with better receiver operator characteristic curve performance to establish a web-based nomogram. The clinical applicability, discrimination, and calibration of the preferable prediction model were assessed. RESULTS: Multivariate regression analysis showed that age, sex, tumor size, bilateral tumors, the number of tumor contacting surfaces, chronic lymphocytic thyroiditis, and RTE were risk predictors of CLNM in cN0 PTC patients, which constituted prediction model 1. Model 2 included the first six risk predictors. Comparison of the areas under the curves of the two models showed that model 1 had better prediction performance (training set 0.798 vs. 0.733, validation set 0.792 vs. 0.715, p < 0.001) and good discrimination and calibration. RTE contributed significantly to the performance of the prediction model. Decision curve analysis showed that patients could obtain good net benefits with the application of model 1. CONCLUSION: A noninvasive web-based nomogram combining US characteristics and clinical risk factors was developed in the research. RTE could improve the prediction accuracy of the model. The dynamic nomogram has good performance in predicting the probability of CLNM in cN0 PTC patients.

Development and validation of an ultrasound-based nomogram for preoperative prediction of cervical central lymph node metastasis in papillary thyroid carcinoma.

BACKGROUND: Preoperative prediction of central lymph node metastasis (CLNM) holds significant value in determining a patient's suitability for surgical resection and the need for adjuvant treatment, thereby contributing to better therapeutic strategies. This study aimed to build and confirm a nomogram that integrates ultrasound (US) characteristics with clinical features to predict CLNM in patients with papillary thyroid carcinoma (PTC) preoperatively. METHODS: The prediction model was set up with a training dataset that included 512 patients with histopathologically confirmed PTC. The least absolute shrinkage and selection operator (LASSO) regression method was applied to select US features in the development cohort. The patients' US characteristics and clinical features were incorporated into a multivariate logistic regression analysis to develop the nomogram. The clinical feasibility, calibration, and discriminatory ability of the nomogram were evaluated in an independent validation cohort of 306 patients. RESULTS: Age, sex, tumor size, multiple tumors, and US-based CLNM status were included as independent predictors in the personalized nomogram. The nomogram showed good calibration and discrimination in the training and validation datasets. The addition of the BRAF V600E mutation status did not improve the performance of the nomogram. The decision curve analysis showed the nomogram to have clinical feasibility. CONCLUSIONS: A nomogram that integrates US characteristics with patients' clinical features was built. This US-based nomogram can be expediently applied to promote the personalized preoperative prediction of CLNM and to develop surgical strategies, such as tailored central compartment neck dissection, in patients with PTC.