Low False Negative Rate Research Articles

Optimization techniques for IDS‐Generated traffic congestion control in VANET

AbstractVehicular Ad‐hoc Network (VANET) is an emerging field of wireless networks that enables a variety of vehicle safety and convenience applications. It employs Intrusion Detection System (IDS) frameworks in its different tiers to ensure reliable and secure communication among nodes. However, IDS requires a significant amount of data to process for monitoring intrusive activities in the network. As a result, the volume of traffic increases, resulting in the network congestion. Motivated by this fact, this study provides an overview of the optimization techniques for VANET traffic congestion control. It discusses a state‐of‐the‐art analysis along with the requirements for IDS‐generated traffic congestion control. It highlights the congestion control approaches for the traffic generated by an IDS and identifies the challenges in this domain. This study also proposes a novel IDS framework for reducing IDS‐generated network traffic by combining the Local Outlier Factor and Random Forest classifier. The proposed study achieved a high precision while yielding low false positive and false negative rates. The study outperformed the existing studies with an increase in accuracy of 1.16% and a reduction in attack detection time of 1.1869 seconds. Additionally, it discusses the possible future research directions that can be applied to address the issues of IDS‐generated traffic congestion. Overall, this study serves as a comprehensive guide to the current status of IDS‐generated traffic congestion control and diverse approaches to lessen it that can be employed by academicians and researchers.

Graph Convolutional Network-Enhanced Model for Screening Persistent, Mobile, and Toxic and Very Persistent and Very Mobile Substances.

The global management for persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances has been further strengthened with the rapid increase of emerging contaminants. The development of a ready-to-use and publicly available tool for the high-throughput screening of PMT/vPvM substances is thus urgently needed. However, the current model building with the coupling of conventional algorithms, small-scale data set, and simplistic features hinders the development of a robust model for screening PMT/vPvM with wide application domains. Here, we construct a graph convolutional network (GCN)-enhanced model with feature fusion of a molecular graph and molecular descriptors to effectively utilize the significant correlation between critical descriptors and PMT/vPvM substances. The model is built with 213,084 substances following the latest PMT classification criteria. The application domains of the GCN-enhanced model assessed by kernel density estimation demonstrate the high suitability for high-throughput screening PMT/vPvM substances with both a high accuracy rate (86.6%) and a low false-negative rate (6.8%). An online server named PMT/vPvM profiler is further developed with a user-friendly web interface (http://www.pmt.zj.cn/). Our study facilitates a more efficient evaluation of PMT/vPvM substances with a globally accessible screening platform.

Modular neural network for Edge-based Detection of early-stage IoT Botnet

The Internet of Things (IoT) has led to rapid growth in smart cities. However, IoT botnet-based attacks against smart city systems are becoming more prevalent. Detection methods for IoT botnet-based attacks have been the subject of extensive research, but the identification of early-stage behaviour of the IoT botnet prior to any attack remains a largely unexplored area that could prevent any attack before it is launched. Few studies have addressed the early stages of IoT botnet detection using monolithic deep learning algorithms that could require more time for training and detection. We, however, propose an edge-based deep learning system for the detection of the early stages of IoT botnets in smart cities. The proposed system, which we call EDIT (Edge-based Detection of early-stage IoT Botnet), aims to detect abnormalities in network communication traffic caused by early-stage IoT botnets based on the modular neural network (MNN) method at multi-access edge computing (MEC) servers. MNN can improve detection accuracy and efficiency by leveraging parallel computing on MEC. According to the findings, EDIT has a lower false-negative rate compared to a monolithic approach and other studies. At the MEC server, EDIT takes as little as 16 ms for the detection of an IoT botnet.

Hybrid semantics-based vulnerability detection incorporating a Temporal Convolutional Network and Self-attention Mechanism

Context:Desirable characteristics in vulnerability-detection (VD) systems (VDSs) include both good detection capability (high accuracy, low false positive rate, low false negative rate, etc.) and low time overheads. The widely used VDSs based on models such as Recurrent Neural Networks (RNNs) have some problems, such as low time efficiency, failing to learn the vulnerability features better, and insufficient amounts of vulnerability features. Therefore, it is very important to construct an automatic detection model with high detection accuracy. Objective:This paper reports on training based on the source code to analyze and learn from the code’s patterns and structures by deep-learning techniques to generate an efficient VD model that does not require manual feature design. Method:We propose a software VD model based on multi-feature fusion and deep neural networks called AIdetectorX-SP. It first uses a Temporal Convolutional Network (TCN) and adds a Self-attention Mechanism (SaM) to the TCN to build a model for extracting vulnerability logic features, then transforms the source code into an image input to a Convolutional Neural Network (CNN) to extract structural and semantic information. Finally, we use feature-fusion technology to design and implement an improved deep-learning-based VDS, called AIdetectorX Sequence with Picturization (AIdetectorX-SP). Results:We report on experiments conducted using publicly-available and widely-used datasets to evaluate the effectiveness of AIdetectorX-SP, with results indicating that AIdetectorX-SP is an effective VDS; that the combination of TCN and SaM can effectively extract vulnerability logic features; and that the pictorial code can extract code structure features, which can further improve the VD capability. Conclusion:In this paper, we propose a novel detection model for software vulnerability based on TCNs, SaM, and software picturization. The proposed model solves some shortcomings and limitations of existing VDSs, and obtains a high software-VD accuracy with a high degree of stability.

Identification and analysis of stochastic deception attacks on cyber–physical systems

Cyber–Physical Systems (CPSs) refer to control systems which are composed of sensors, actuators, computers and network components. These systems are vulnerable to unforeseen failures and external malicious attacks. In this paper, we analyze the stability of CPSs under stochastic deception attacks. To this end: (i) we propose a statistical Intrusion Detection System (IDS) for detection of deception attacks in CPSs; (ii) identify the place of such attacks by taking advantage of a novel cryptographic adversarial model; (iii) based on the real-time data and characterizing the deception attacks by IDS, we analyze the effect of both adaptive and blind/random types of deception attacks on the stability of CPSs. We do this through Markov chain modeling and subsequently extract the sufficient stability conditions. (iv) as an extra effort, introduce a new adaptive and intelligent deception attack. We validate our findings by illustrative examples at the end of the paper. Our results show that proposed IDS can detect deception attacks with low false positive and negative rates in real time. The results also confirm the validity of the theoretically-predicted stability conditions.

Data-set class-balancing and the Convolutional Vision Transformer: An analysis of chest radiographs for SARS-CoV-2 detection

Introduction:The COVID-19 pandemic has had impact on global public health and economies. Chest X-rays (CXRs) play a critical role in screening, especially in resource-constrained regions. This work focuses on the development of a deep learning model for detecting COVID-19 in CXRs images, aiming to address the challenges associated with manual interpretation. Methods:Our approach integrates Convolutional Vision Transformers with traditional Convolutional Neural Networks for CXRs analysis. Using a dataset comprising 5572 CXRs, including various COVID-19 severities and other pneumonia types, the model underwent training across multiple configurations and evaluation using various metrics. Each Dataset Configuration (DSC) involved 10 training iterations. We employed an enhanced attention visualization technique to identify key areas in X-rays. Results:This work presents the top five models based on training and validation outcomes across different radiographic classes. The model’s performance varied with dataset configurations, particularly in distinguishing COVID-19 from other pneumonia types. DSC 6 emerged as the most effective, demonstrating 97.1% sensitivity and a 2.9% false negative rate during training, and maintaining high performance during validation with 87.2% sensitivity and a 12.8% false negative rate. Conclusion:This fusion of engineering and medical expertise yields an efficient screening tool. The COVID-19 crisis underscores the importance of enhanced healthcare preparedness. Our proposed model achieves over 90% average recall, accuracy, and sensitivity, coupled with a low false negative rate. It reliably identifies COVID-19 across different severities and effectively distinguishes it from other pneumonia types, establishing its utility as a robust COVID-19 detection tool.

Development of a Tic Screener: The Motor or Vocal Inventory of Tics (MOVeIT-14)

ABSTRACT This study describes the development and preliminary testing of the Motor or Vocal Inventory of Tics (MOVeIT), a tic symptom screener. Scale items were reviewed by 15 subject matter experts and tested with 2 pilot subject parent-child dyads. Subsequently, 80 children aged 4–17 years and their parents were recruited from a pediatric behavioral health clinic (N = 49) and a general pediatric clinic (N = 31). Parents and youth aged 8 years and older completed the screener. An expert clinician diagnostic interview and the Yale Global Tic Severity Scale served as the gold standard for comparison. Using a MOVeIT-14 Parent Report cutoff of 10, 83.3% of youth were correctly classified (90% sensitivity, 81% specificity). For the youth report, the optimal cutoff was 12 (74% sensitivity, 83% specificity, 61.5% correctly classified). Parent-youth agreement was strong (r = .74), and reliability was 0.98 and 0.95 for parent and youth reports respectively. Results suggest that a 14-item version (the MOVeIT-14) may accurately identify tic disorders with low rates of false negatives, especially using parent report.

Evaluation of traditional targeted axillary dissection eligibility criteria for node-positive breast cancer after neoadjuvant chemotherapy in a prospective multicenter registry

IntroductionTargeted axillary dissection (TAD) is performed after neoadjuvant systemic therapy (NST) to decrease the rate of non-therapeutic axillary dissection (ALND) for patients with node-positive breast cancer. In order to ensure the oncologic safety of TAD, eligibility criteria resulting in a low false negative rate (FNR) have been proposed. The purpose of this study was to evaluate the utility of the traditional criteria. MethodsData was collected from a prospective multicenter registry. In order to ascertain FNRs, pathologic findings in the sentinel lymph nodes (LN)s, malignant clipped LN, and axillary contents were determined. The FNRs within TAD eligibility criterion groups were compared. ResultsA total of 110 patients underwent TAD and ALND, and were therefore eligible for analysis. TAD retained a low FNR in advanced clinical T-N stage compared with earlier disease (T stage: 95% CI 0.00–11.93, p = 0.42; N stage: 95% CI 0.00–8.76, p = 0.31). Presentation with ≥4 abnormal LNs on axillary ultrasound did not predict a high TAD FNR (95% CI 0.00–5.37, p = 0.16). No significant differences were noted in TAD FNR when single was compared with dual tracer (blue dye vs dual tracer 95% CI 0.72–52.49, p = 0.13; radiotracer vs dual tracer 0.04–20.11, p = 0.51). Excision of the clipped LN and only one SLN was as accurate as excision of the clipped LN and ≥2 SLNs (95% CI 0.00–10.61, p = 0.38). ConclusionsTAD retained a low FNR among patients traditionally considered ineligible for this technique. However, excision of the clipped LN and at least one SLN remained essential to a low FNR.

Artificial Intelligence to Differentiate Pediatric Pseudopapilledema and True Papilledema on Fundus Photographs

PurposeTo develop and test an artificial intelligence (AI) model to aid in differentiating pediatric pseudopapilledema from true papilledema on fundus photographs. DesignMulti-center retrospective study Subjects851 fundus photographs from 235 children (age <18 years) with pseudopapilledema and true papilledema MethodsFour pediatric neuro-ophthalmologists at four different institutions contributed fundus photographs of children with confirmed diagnoses of papilledema or pseudopapilledema. An AI model to classify fundus photographs as papilledema or pseudopapilledema was developed using a DenseNet backbone and a tri-branch convolutional neural network. We performed 10-fold cross-validation and separately analyzed an external test set. The AI model’s performance was compared to two masked human expert pediatric neuro-ophthalmologists, who performed the same classification task. Main Outcome MeasuresAccuracy, sensitivity, and specificity of the AI model compared to human experts. ResultsThe area under receiver operating curve (AUC) of the AI model was 0.77 for the cross-validation set and 0.81 for the external test set. The accuracy of the AI model was 70.0% for the cross-validation set and 73.9% for the external test set. The sensitivity of the AI model was 73.4% for the cross-validation set and 90.4% for the external test set. The AI model’s accuracy was significantly higher than human experts on the cross validation set (p<0.002), and the model’s sensitivity was significantly higher on the external test set (p<0.0002). The specificity of the AI model and human experts was similar (56.4-67.3%). Moreover, the AI model was significantly more sensitive at detecting mild papilledema than human experts, whereas AI and humans performed similarly on photographs of moderate-to-severe papilledema. On review of the external test set, only one child (with nearly resolved pseudotumor cerebri) had both eyes with papilledema incorrectly classified as pseudopapilledema. ConclusionsWhen classifying fundus photographs of pediatric papilledema and pseudopapilledema, our AI model achieved over 90% sensitivity at detecting papilledema, superior to human experts. Due to the high sensitivity and low false negative rate, AI may be useful to triage children with suspected papilledema requiring work-up to evaluate for serious underlying neurologic conditions.

QRS Detection Based on Discrete Wavelet Transform for ECG Signal with Motion Artifacts

Motion artifacts in ECG signals recorded during physical exercises activities can affect the diagnosis of arrhythmia. To minimize the faults in arrhythmia detection, it was important to choose accurate algorithm for detecting QRS in ECG signal with noises produced during physical movements of the patients. Therefore, choosing the QRS detection algorithm with good competency for the signal affected by noises and motion artifacts is needed for arrhythmia detection analysis. The QRS detection based on Discrete Wavelet Transform was implemented and presented in this paper. The performance of the algorithm was assessed using the MIT-BIH Arrhythmia Database and MIT-BIH Noise Stress Database. For the MIT-BIH Arrhythmia database, the average Sensitivity (Se) and positive Predictivity (+P) of the algorithm were 98.24% and 98.61%, respectively. The algorithms had a lower average false negative rate (FNR) than the pan Tompkins algorithm when applied to the MIT-BIH noise stress test database, which was 0.033% for record 118 and 0.032% for record 119, respectively. The results demonstrated that the algorithms perform well when dealing with arrhythmia data and motion artifact at various levels of signal to noise ratio.

Early Detection of Windows Cryptographic Ransomware Based on Pre-Attack API Calls Features and Machine Learning

Ransomware attacks are currently one of cybersecurity's greatest and most alluring threats. Antivirus software is frequently ineffective against zero-day malware and ransomware attacks; consequently, significant network infections could result in substantial data loss. Such attacks are also becoming more dynamic and capable of altering their signatures, resulting in a race to the bottom regarding weaponry. Cryptographic ransomware exploits crypto-viral extortion techniques. The malware encrypts the victim's data and demands payment in exchange. The attacker would release the data decryption key after accepting payment. After data encryption, the user has two options: pay the ransom or lose the data. Cryptographic ransomware causes damage that is nearly impossible to undo. Detection at an early stage of a ransomware attack's lifecycle is vital for preventing unintended consequences for the victim. Most ransomware detection technologies concentrate on detection during encryption and post-attack stages. Due to the absence of early behaviour signs, it is challenging to detect ransomware before it begins the unwanted process of mass file encryption. This study examines the relationship between API calls pattern and their nature to determine whether it is ransomware early behaviour. The purpose of this paper is to determine whether this technique can be used to early detect the presence of ransomware activity on a Windows endpoint. 582 ransomware samples that consist of ten ransomware families and 942 benign software samples were analysed. This study proposed RENTAKA, a novel framework for the early detection of cryptographic ransomware. It makes use of characteristics acquired from ransomware behaviour and machine learning. This study presented an algorithm to generate a ransomware pre-encryption dataset. This study, which includes six machine-learning models, gives satisfactory results in detecting cryptographic ransomware. The features used in this research were among the 232 features identified in Windows API calls. Five standard machine learning classifiers were employed in this experiment: Naive Bayes, k-nearest neighbours (kNN), Support Vector Machines (SVM), Random Forest, and J48. In our tests, SVM fared the best, with an accuracy rate of 93.8% and an area under the curve (AUC) of 0.979, respectively. The results indicate that we can distinguish ransomware from benign applications with low false-positive and false-negative rates.

Advancing spinal cord injury care through non-invasive autonomic dysreflexia detection with AI

This paper presents an AI-powered solution for detecting and monitoring Autonomic Dysreflexia (AD) in individuals with spinal cord injuries. Current AD detection methods are limited, lacking non-invasive monitoring systems. We propose a model that combines skin nerve activity (SKNA) signals with a deep neural network (DNN) architecture to overcome this limitation. The DNN is trained on a meticulously curated dataset obtained through controlled colorectal distension, inducing AD events in rats with spinal cord surgery above the T6 level. The proposed system achieves an impressive average classification accuracy of 93.9% ± 2.5%, ensuring accurate AD identification with high precision (95.2% ± 2.1%). It demonstrates a balanced performance with an average F1 score of 94.4% ± 1.8%, indicating a harmonious balance between precision and recall. Additionally, the system exhibits a low average false-negative rate of 4.8% ± 1.6%, minimizing the misclassification of non-AD cases. The robustness and generalizability of the system are validated on unseen data, maintaining high accuracy, F1 score, and a low false-negative rate. This AI-powered solution represents a significant advancement in non-invasive, real-time AD monitoring, with the potential to improve patient outcomes and enhance AD management in individuals with spinal cord injuries. This research contributes a promising solution to the critical healthcare challenge of AD detection and monitoring.

Towards a global impact-based forecasting model for tropical cyclones

Abstract. Tropical cyclones (TCs) produce strong winds and heavy rains accompanied by consecutive events such as landslides and storm surges, resulting in losses of lives and livelihoods, particularly in regions with high socioeconomic vulnerability. To proactively mitigate the impacts of TCs, humanitarian actors implement anticipatory action. In this work, we build upon such an existing anticipatory action for the Philippines, which uses an impact-based forecasting model for housing damage based on eXtreme Gradient Boosting (XGBoost) to release funding and trigger early action. We improve it in three ways. First, we perform a correlation and selection analysis to understand if Philippines-specific features can be left out or replaced with features from open global data sources. Secondly, we transform the target variable (percentage of completely damaged houses) and not yet grid-based global features to a 0.1∘ grid resolution by de-aggregation using Google Open Buildings data. Thirdly, we evaluate XGBoost regression models using different combinations of global and local features at grid and municipality spatial levels. We first introduce a two-stage model to predict if the damage is above 10 % and then use a regression model trained on all or only high-damage data. All experiments use data from 39 typhoons that impacted the Philippines between 2006–2020. Due to the scarcity and skewness of the training data, specific attention is paid to data stratification, sampling, and validation techniques. We demonstrate that employing only the global features does not significantly influence model performance. Despite excluding local data on physical vulnerability and storm surge susceptibility, the two-stage model improves upon the municipality-based model with local features. When applied to anticipatory action, our two-stage model would show a higher true-positive rate, a lower false-negative rate, and an improved false-positive rate, implying that fewer resources would be wasted in anticipatory action. We conclude that relying on globally available data sources and working at the grid level holds the potential to render a machine-learning-based impact model generalizable and transferable to locations outside of the Philippines impacted by TCs. Also, a grid-based model increases the resolution of the predictions, which may allow for a more targeted implementation of anticipatory action. However, it should be noted that an impact-based forecasting model can only be as good as the forecast skill of the TC forecast that goes into it. Future research will focus on replicating and testing the approach in other TC-prone countries. Ultimately, a transferable model will facilitate the scaling up of anticipatory action for TCs.

Machine learning-based classifiers to predict metastasis in colorectal cancer patients.

The increasing prevalence of colorectal cancer (CRC) in Iran over the past three decades has made it a key public health burden. This study aimed to predict metastasis in CRC patients using machine learning (ML) approaches in terms of demographic and clinical factors. This study focuses on 1,127 CRC patients who underwent appropriate treatments at Taleghani Hospital, a tertiary care facility. The patients were divided into training and test datasets in an 80:20 ratio. Various ML methods, including Naive Bayes (NB), random rorest (RF), support vector machine (SVM), neural network (NN), decision tree (DT), and logistic regression (LR), were used for predicting metastasis in CRC patients. Model performance was evaluated using 5-fold cross-validation, reporting sensitivity, specificity, the area under the curve (AUC), and other indexes. Among the 1,127 patients, 183 (16%) had experienced metastasis. In the predictionof metastasis, both the NN and RF algorithms had the highest AUC, while SVM ranked third in both the original and balanced datasets. The NN and RF algorithms achieved the highest AUC (100%), sensitivity (100% and 100%, respectively), and accuracy (99.2% and 99.3%, respectively) on the balanced dataset, followed by the SVM with an AUC of 98.8%, a sensitivity of 97.5%, and an accuracy of 97%. Moreover, lower false negative rate (FNR), false positive rate (FPR), and higher negative predictive value (NPV) can be confirmed by these two methods. The results also showed that all methods exhibited good performance in the test datasets, and the balanced dataset improved the performance of most ML methods. The most important variables for predicting metastasis were the tumor stage, the number of involved lymph nodes, and the treatment type. In a separate analysis of patients with tumor stages I-III, it was identified that tumor grade, tumor size, and tumor stage are the most important features. This study indicated that NN and RF were the best among ML-based approaches for predicting metastasis in CRC patients. Both the tumor stage and the number of involved lymph nodes were considered the most important features.

P458 Diagnostic accuracy of patency capsule and cross-sectional imaging for capsule endoscopy retention: A systematic review and meta-analysis

Abstract Background Although small bowel capsule endoscopy (SBCE) has been widely used to evaluate small bowel lesions including Crohn’s disease, there is a risk of capsule retention that can cause obstructive symptoms potentially necessitating surgical treatment. We conducted a systematic review and meta-analysis to evaluate the accuracy of patency capsule (PC) and cross-sectional imaging (CSI) for predicting capsule retention. Methods MEDLINE/PubMed, EMBASE, Web of science, and the Cochrane library databases were searched for articles that invested the diagnostic accuracy of PC or CSI for predicting capsule retention up to August 15, 2023. Pooled sensitivity and specificity were calculated using a bivariate random-effects model. Considering that SBCE was generally not performed when capsule retention was suspected on PC, the false-negative rates of PC and CSI were separately analyzed and compared using a single-proportion meta-analysis based on random-effects modeling. Quality assessment of the selected studies was performed using the revised Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results A total of 22 peer-reviewed articles, including 17 articles for PC (covering 2,234 patients) and 7 articles for CSI (covering 302 patients) were included in this systematic review. The pooled sensitivity and specificity for predicting capsule retention with CSI were 64% [95% confidence interval (CI), 31%–87%] and 85% [95% CI, 62%–95%], respectively. The pooled sensitivity and specificity for predicting capsule retention with PC were 75% [95% CI, 43%–92%] and 94% [95% CI, 90%–96%], respectively (Figure 1, Table 1). Although there was no statistically significant difference in prediction accuracy between PC and CSI on the bivariate random-effects model (P=0.14), the pooled false-negative rate was significantly lower in the PC compared to CSI (2.5% [95% CI, 1.4%–4.7%] vs. 11.0% [95% CI, 5.2%–21.8%]; P=0.003) (Table 1). Conclusion Cross-sectional imaging showed good specificity, but showed moderate sensitivity in predicting capsule retention. In contrast, patency capsule showed good sensitivity and excellent specificity, with a significantly lower false-negative rate compared to cross-sectional imaging. Therefore, in patients with clinically suspicious small bowel stenosis, the patency capsule is a more reliable modality for predicting capsule retention.

Advancing Tailored Treatments: A Predictive Nomogram, Based on Ultrasound and Laboratory Data, for Assessing Nodal Involvement in Endometrial Cancer Patients.

Assessing lymph node metastasis is crucial in determining the optimal therapeutic approach for endometrial cancer (EC). Considering the impact of lymphadenectomy, there is an urgent need for a cost-effective and easily applicable method to evaluate the risk of lymph node metastasis in cases of sentinel lymph node (SLN) biopsy failure. This retrospective monocentric study enrolled EC patients, who underwent surgical staging with nodal assessment. Data concerning demographic, clinicopathological, ultrasound, and surgical characteristics were collected from medical records. Ultrasound examinations were conducted in accordance with the IETA statement. We identified 425 patients, and, after applying exclusion criteria, the analysis included 313 women. Parameters incorporated into the nomogram were selected via univariate and multivariable analyses, including platelet count, myometrial infiltration, minimal tumor-free margin, and CA 125. The nomogram exhibited good accuracy in predicting lymph node involvement, with an AUC of 0.88. Using a cutoff of 10% likelihood of nodal involvement, the nomogram displayed a low false-negative rate of 0.04 (95% CI 0.00-0.19) in the training set. The adaptability of this straightforward model renders it suitable for implementation across diverse clinical settings, aiding gynecological oncologists in preoperative patient evaluations and facilitating the design of personalized treatments. However, external validation is mandatory for confirming diagnostic accuracy.

Contrast-enhanced breast imaging: Current status and future challenges

BackgroundContrast-enhanced breast MRI and recently also contrast-enhanced mammography (CEM) are available for breast imaging. The aim of the current overview is to explore existing evidence and ongoing challenges of contrast-enhanced breast imaging. MethodsThis narrative provides an introduction to the contrast-enhanced breast imaging modalities breast MRI and CEM. Underlying principle, techniques and BI-RADS reporting of both techniques are described and compared, and the following indications and ongoing challenges are discussed: problem-solving, high-risk screening, supplemental screening in women with extremely dense breast tissue, breast implants, neoadjuvant systemic therapy (NST) response monitoring, MRI-guided and CEM- guided biopsy. ResultsTechnique and reporting for breast MRI are standardised, for the newer CEM standardisation is in progress. Similarly, compared to other modalities, breast MRI is well established as superior for problem-solving, screening women at high risk, screening women with extremely dense breast tissue or with implants; and for monitoring response to NST. Furthermore, MRI-guided biopsy is a reliable technique with low long-term false negative rates. For CEM, data is as yet either absent or limited, but existing results in these settings are promising. ConclusionContrast-enhanced breast imaging achieves highest diagnostic performance and should be considered essential. Of the two contrast-enhanced modalities, evidence of breast MRI superiority is ample, and preliminary results on CEM are promising, yet CEM warrants further study.

The Utility of ACR TI-RADS in Predicting False-Negative Fine Needle Aspiration for Thyroid Cancer.

Thyroid nodule fine needle aspiration (FNA) biopsies are associated with a low false-negative rate. There is limited data regarding the predictive value of American College of Radiology Thyroid Imaging Reporting and Data System for false-negative FNA. This single-center retrospective study evaluated 119 patients who underwent thyroidectomy. The association of TR category, along with other clinical variables, with false-negative FNA was evaluated. The overall false-negative rate of FNA was 10.8% (n = 9). False-negative FNAs were associated with younger age (mean 42years vs 50.6years, P = .04), larger nodule size (mean 4.4cm vs 3.2cm, P = .03), and a lower TR category (median 3 v 4, P = .01). Lower TR category, younger age, and larger nodule size were associated with false-negative FNA of thyroid nodules. These findings should be taken into context when counseling patients with thyroid nodules who have a benign FNA.

Predictors of Myelosuppression for Patients with Head and Neck Squamous Cell Carcinoma After Induction Chemotherapy.

Induction chemotherapy (ICT) has become an initial treatment for head and neck squamous cell carcinoma (HNSCC). However, myelosuppression, an unavoidable side effect of ICT, significantly impacts follow-up treatment and prognosis. The main objective of this study is to identify the risk factors and predictors of myelosuppression and its different severity after ICT for ICT. We retrospectively reviewed medical records of 102 patients with hypopharyngeal cancer or oropharyngeal cancer who received initial ICT from 2013 to 2022. Univariate and multivariate logistic regression analyses were performed to identify risk factors for myelosuppression. Receiver-operating characteristic (ROC) curves were generated using the results of multiple logistic regression analysis to identify data with the highest sensitivity and lowest false-negative rate. Pretreatment lymphocyte count (PLC) and the pretreatment platelet count (PPC) were identified as independent risk factors of myelosuppression (P < .05). Pretreatment hemoglobin count (PHC) was an independent risk factor for predicting myelosuppression in patients with grades III to IV disease. Patients with myelosuppression after ICT are more sensitive to chemotherapy. The PLC and PPC predicted myelosuppression in patients with HNSCC-administered ICT, and the PHC predicted grades III to IV myelosuppression. Myelosuppressed patients were more chemosensitive after ICT.

Early Detection of Parkinson's Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings.

With the development of digital medical technology, ubiquitous smartphones are emerging as valuable tools for the detection of complex and elusive diseases. This paper exploits smartphone walking recording for early detection of Parkinson's disease (PD) and finds that walking recording empowered by deep learning is a valid digital biomarker for early-recognizing PD patients. Specifically, the inertial sensor data is preprocessed, including normalization, scaling, and rotation, and then the processed data is fed into the proposed deep NeuroEnhanceNet. Finally, determine the individual prediction score using the PD-prone strategy and generate the detection results. The proposed deep NeuroEnhanceNet, specifically designed for inertial sensor data, can focus on both the long-term data characteristics within a single channel and the inter-channel correlations. Our method obtains a low false negative rate of 0.053 for the early detection of PD. We further analyze and compare the effectiveness of digital biomarkers captured from the walking and resting processes for early detection of PD. All the code for this work is available at: https://github.com/heyiyia/NeuroEnhanceNet.