Linear Support Vector Machine Algorithm Research Articles

Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing.

This article presents a computer vision-based approach to switching electric locomotive power supplies as the vehicle approaches a railway neutral section. Neutral sections are defined as a phase break in which the objective is to separate two single-phase traction supplies on an overhead railway supply line. This separation prevents flashovers due to high voltages caused by the locomotives shorting both electrical phases. The typical system of switching traction supplies automatically employs the use of electro-mechanical relays and induction magnets. In this paper, an image classification approach is proposed to replace the conventional electro-mechanical system with two unique visual markers that represent the 'Open' and 'Close' signals to initiate the transition. When the computer vision model detects either marker, the vacuum circuit breakers inside the electrical locomotive will be triggered to their respective positions depending on the identified image. A Histogram of Oriented Gradient technique was implemented for feature extraction during the training phase and a Linear Support Vector Machine algorithm was trained for the target image classification. For the task of image segmentation, the Circular Hough Transform shape detection algorithm was employed to locate the markers in the captured images and provided cartesian plane coordinates for segmenting the Object of Interest. A signal marker classification accuracy of 94% with 75 objects per second was achieved using a Linear Support Vector Machine during the experimental testing phase.

Control strategy design and performance analysis of a low carbon and high ratio green energy CCHP (LCHRG-CCHP) system

In order to solve the compatibility of combined cooling, heating and power (CCHP) system with green energy and the coupling problem with energy storage equipment, the system structure (LCHRG-CCHP system) proposed in this paper for the low-carbon and high-green energy compatible optimization research of the integrated energy system integrating green energy and flexible hybrid energy storage. Aiming at the high compatibility and low carbon emission of CCHP system to green energy, the traditional CCHP system, the combination of CCHP system and green energy, the combination of CCHP system and green energy and short-term energy storage, and the combination of CCHP system and green energy and long-term and short-term energy storage are compared. The intelligent optimization algorithm combining improved sine dung beetle optimizer and linear support vector machine algorithm (MDBO-LSVM) is used to solve the problem. The results show that in the CCHP system, the coupling of short-term energy storage and long-term energy storage can effectively improve the compatibility of the system with green energy. The proportion of green energy in the system proposed in this paper is increased by 13.32 %, which can reach an average of 23.91 %, while the carbon emissions are reduced by 37.34 %, which effectively improves the energy utilization rate.

A machine learning approach to predict the most and the least feed–efficient groups in beef cattle

The present study evaluated three strategies to find the optimum subset of DNA markers from the 50 K Illumina Bovine panel to classify beef cattle into the most and the least feed-efficient groups without using individual feed intake and performance measures. Residual feed intake (RFI) and 50 K single nucleotide polymorphisms (SNPs) genotype data of 4,057 beef animals from research and commercial herds were included. Initially, all cattle were ranked based on their phenotypic RFI values. Then different datasets were created by selecting animals from the 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, and 45% range of top and bottom of the ranked RFI values. SNP subsets were selected based on the top-ranked SNPs contributing to the variance of RFI (first strategy), selecting SNPs from the SNP subsets created in the first strategy (strategy 2), and extracting SNPs from 50k SNPs (strategy 3). Then eleven ML algorithms were employed to classify the most and the least feed-efficient groups using 260 datasets generated by combinations of ten RFI phenotype percentage groups and 6, 18, and 2 SNP subsets in the first, second and third strategies, respectively. There was a high degree of accuracy (>69%) for classifying animals in the range of 1% for all ML algorithms under the three strategies and different SNP subsets. Implementing the linear Support Vector Machine algorithm for 15 K SNPs obtained in the first strategy predicted the 1% of the most and the least feed-efficient animals with an accuracy of 84%. In the second strategy, selecting 524 SNPs from the 15 K SNPs subset outperformed the other strategies with an accuracy of 81% for 1% of the population using the Naive Bayes algorithm. It was concluded that a smaller number of SNPs (524) could be used to predict the most and the least feed-efficient animals with an acceptable accuracy to reduce the cost of selection for RFI using genomic information.

Predicting wheat gluten concentrations in potato starch using GPR and SVM models built by terahertz time-domain spectroscopy

The purpose of this study was for the first time to explore the feasibility of terahertz (THz) spectral imaging for the detection of gluten contents in food samples. Based on the obtained 80 THz spectrum data, Gaussian process regression (GPR) and support vector machine (SVM) models were established to predict wheat gluten concentrations in 40 potato starch mixture samples. The prediction performances of GPR and SVM obtained were R2 = 0.859 and RMSE = 0.070, and R2 = 0.715 and RMSE = 0.101 in the gluten concentration range of 1.3%-100%, respectively, showing that the linear SVM algorithm had better prediction performance. The results indicated that THz spectral imaging combined with GPR could be used to predict the gluten content in food samples. It is thus hoped that this research should provide a novel technique for gluten content detection to ensure gluten-free food samples.

Preoperative prediction of extrathyroidal extension: radiomics signature based on multimodal ultrasound to papillary thyroid carcinoma

BackgroundThere is a recognized need for additional approaches to improve the accuracy of extrathyroidal extension (ETE) diagnosis in papillary thyroid carcinoma (PTC) before surgery. Up to now, multimodal ultrasound has been widely applied in disease diagnosis. We investigated the value of radiomic features extracted from multimodal ultrasound in the preoperative prediction of ETE.MethodsWe retrospectively pathologically confirmed PTC lesions in 235 patients from January 2019 to April 2022 in our hospital, including 45 ETE lesions and 205 non-ETE lesions. MaZda software was employed to obtain radiomics parameters in multimodal sonography. The most valuable radiomics features were selected by the Fisher coefficient, mutual information, probability of classification error and average correlation coefficient methods (F + MI + PA) in combination with the least absolute shrinkage and selection operator (LASSO) method. Finally, the multimodal model was developed by incorporating the clinical records and radiomics features through fivefold cross-validation with a linear support vector machine algorithm. The predictive performance was evaluated by sensitivity, specificity, accuracy, F1 scores and the area under the receiver operating characteristic curve (AUC) in the training and test sets.ResultsA total of 5972 radiomics features were extracted from multimodal sonography, and the 13 most valuable radiomics features were selected from the training set using the F + MI + PA method combined with LASSO regression. The multimodal prediction model yielded AUCs of 0.911 (95% CI 0.866–0.957) and 0.716 (95% CI 0.522–0.910) in the cross-validation and test sets, respectively. The multimodal model and radiomics model showed good discrimination between ETE and non-ETE lesions.ConclusionRadiomics features based on multimodal ultrasonography could play a promising role in detecting ETE before surgery.

Wireless Sensor Network Based Real-Time Pedestrian Detection and Classification for Intelligent Transportation System

Pedestrian safety has become a critical consideration in developing society especially road traffic, an intelligent transportation need of the hour is the solution left. India tops the world with 11% of global road accidents. With this data, we have moved in the direction of computer vision applications for efficient and accurate pedestrian detection for intelligent transportation systems (ITS). The important application of this research is robot development, traffic management and control, unmanned vehicle driving (UVD), intelligent monitoring and surveillance system, and automatic pedestrian detection system. Much research has focused on pedestrian detection, but sustainable solution-driven research must still be required to overcome road accidents. We have proposed a wireless sensor network-based pedestrian detection system that classifies the real-time set of pedestrian activity and samples the reciprocally received signal strength (RSS) from the sensor node. We applied a histogram of oriented gradient (HOG) descriptor algorithm K-nearest neighbor, decision tree and linear support vector machine to measure the performance and prediction of the target. Also, these algorithms have performed a comparative analysis under different aspects. The linear support vector machine algorithm was trained with 481 samples. The performance achieves the accuracy of 98.90%and has accomplished superior results with a maximum precision of 0.99, recall of 0.98, and F-score of 0.95 with 2% error rate. The model’s prediction indicates that it can be used in the intelligent transportation system. Finally, the limitation and the challenges discussed to provide an outlook for future research direction to perform effective pedestrian detection.

Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer

ObjectiveThe most widespread and intrusive cancer type among women is breast cancer. Globally, this type of cancer causes more mortality among women, next to lung cancer. This made the researchers to focus more on developing effective Computer-Aided Detection (CAD) methodologies for the classification of such deadly cancer types. In order to improve the rate of survival and earlier diagnosis, an optimistic research methodology is required in the classification of breast cancer. Consequently, an improved methodology that integrates the principle of deep learning with metaheuristic and classification algorithms is proposed for the severity classification of breast cancer. Hence to enhance the recent findings, an improved CAD methodology is proposed for redressing the healthcare problem. Material and MethodsThe work intends to cast a light-of-research towards classifying the severities present in digital mammogram images. For evaluating the work, the publicly available MIAS, INbreast, and WDBC databases are utilized. The proposed work employs transfer learning for extricating the features. The novelty of the work lies in improving the classification performance of the weighted k-nearest neighbor (wKNN) algorithm using particle swarm optimization (PSO), dragon-fly optimization algorithm (DFOA), and crow-search optimization algorithm (CSOA) as a transformation technique i.e., transforming non-linear input features into minimal linear separable feature vectors. ResultsThe results obtained for the proposed work are compared then with the Gaussian Naïve Bayes and linear Support Vector Machine algorithms, where the highest accuracy for classification is attained for the proposed work (CSOA-wKNN) with 84.35% for MIAS, 83.19% for INbreast, and 97.36% for WDBC datasets respectively. ConclusionThe obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the severity classification of breast cancer.

Metode Algoritma Support Vector Machine (SVM) Linier Dalam Memprediksi Kelulusan Mahasiswa

The accumulation of student databases can occur if students are unable to complete their studies, namely graduating at a predetermined time. Data mining techniques are often used to process student data so that they can produce predictions of student graduation in order to graduate at a predetermined time. One of the data mining techniques that is often used is the Support Vector Machine (SVM) algorithm. This study aims to analyze the performance of the SVM algorithm to produce a predictive model of student graduation in order to graduate at a predetermined time in the Public Health Study Program, Faculty of Public Health, Deli Husada Health Institute. The method used in this study is a linear SVM algorithm starting from data retrieval by selecting the attributes that will be used for the next stage, data processing consists of cleaning data whose contents do not exist and data transformation which is the determination of the category of each data, modeling is done with the SVM algorithm. from training data and testing and evaluation data to validate and measure the accuracy of the model. The test results with the amount of training data as much as 70% and testing data as much as 30% shows that the linear SVM algorithm provides an accuracy value of 90%

Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac Arrhythmias

Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC) framework with the linear support vector machine (LSVM) classification strategy. In the training phase, most existing convolutional sparse coding frameworks are unsupervised in the sense that label information is ignored in the convolutional filter training stage. In this work, we explicitly incorporate a label consistency constraint called “discriminative sparse-code error” into the objective function to learn discriminative dictionary filters for sparse coding. The learned dictionary filters encourage signals from the same class to have similar sparse codes, and signals from different classes to have dissimilar sparse codes. To reduce the computational complexity, we propose to perform a max-pooling operation on the sparse coefficients. Using LSVM as a classifier, we examine the performance of the proposed classification system on the MIT-BIH arrhythmia database in accordance with the AAMI EC57 standard. The experimental results show that the proposed DCSC + LSVM algorithm can obtain 99.32% classification accuracy for cardiac arrhythmia recognition.

Using Nix color sensor and Munsell soil color variables to classify contrasting soil types and predict soil organic carbon in Eastern India

Optimal soil management depends on rapid and frequent monitoring of key soil properties, which are conventionally measured in the laboratory using laborious wet-chemistry protocols. The Nix color sensor has recently exhibited promise for predicting several soil properties using soil color. This study evaluated the relationship between the Munsell Soil Color Chart (MSCC) color values of dry and ground surface soil samples to those reported by the Nix color sensor with (NixSTD) and without MSCC standardization (NixNON-STD) to classify 371 samples collected from three contrasting soil types, collected from three agro-climatic zones (coastal saline zone, red and laterite zone, and Gangetic alluvial zone) and to predict soil organic carbon (OC) using different multivariate data mining algorithms. Comparing the CIEL*a*b* color values reported by the MSCC and the NixSTD, an acceptable mean color difference (ΔE*ab) value of 5.20 was obtained, indicating the potential accuracy of the Nix sensor. Principal component analysis efficiently clustered the soil types using the RGB variables extracted from the MSCC color chips in tandem with the NixSTD/NixNON-STD data. Both classification tree and linear support vector machine algorithms perfectly classified all three contrasting soil types using NixNON-STD data alone. Besides, the combination of the MSCC and the NixNON-STD datasets produced the best OC prediction (R2 = 0.66) via random forest (RF) algorithm and indicated the potential of Nix in digital soil morphometrics. In most of the RF models, redness (a*), yellowness (b*), and yellow (Y) variables appeared influential, presumably because of their negative correlation with OC in red and laterite soils. More research is warranted to measure the impacts of variable soil moisture and other confounding soil morphological features on the soil classification and OC prediction performance to extend the approach for classifying soil types and predicting OC in-situ.

July 2022 Stroke Highlights.

July 2022 Stroke Highlights.

A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG.

This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.

Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations.

The present study was conducted on three commercial laying breeder strains to evaluate differences of sensory qualities, including texture, smell, and taste parameters. A total of 140 eggs for each breed were acquired from Beinong No.2 (B) laying hens, Hy-Line Brown (H) laying hens, and Wuhei (W) laying hens. Sensory qualities of egg yolks and albumen from three breeds were detected and discriminated based on different algorithms. Texture profile analysis (TPA) showed that the eggs from three breeds had no differences in hardness, adhesiveness, springiness, and chewiness other than cohesiveness. The smell profiles measured by electronic nose illustrated that differences existed in all 10 sensors for albumen and 8 sensors for yolks. The taste profiles measured by electronic tongue found that the main difference of egg yolks and albumen existed in bitterness and astringency. Principal component analysis (PCA) successfully showed grouping of three breeds based on electronic nose data and failed in grouping based on electronic tongue data. Based on electronic nose data, linear discriminant analysis (LDA), fine k-nearest neighbor (KNN) and linear support vector machine (SVM) were performed to discriminate yolks, albumen, and the whole eggs with 100% classification accuracy. While based on electronic tongue data, the best classification accuracy was 96.7% for yolks by LDA and fine tree, 88.9% for albumen by LDA, and 87.5% for the whole eggs by fine KNN. The experiment results showed that three breeds’ eggs had main differences in smells and could be successfully discriminated by LDA, fine KNN, and linear SVM algorithms based on electronic nose.

Classification of Medical Ultrasound Transducer Using Neural Network

Diagnostic medical ultrasound makes a significant contribution to patient care and is increasingly used in a variety of clinical settings by many different professionals with varying technical backgrounds (e.g. hepatocellular carcinoma). Therefore, the importance of ultrasound quality control is not only necessary for patient and operator safety but is also essential for maintaining the performance of the equipment to the highest-level achievable and it is required by various regulatory and accrediting agencies. Ultrasound image degradation originates primarily from transducer defects and potentially undermines reliable image interpretation. The ultrasound probe in-air reverberation pattern is used in routine quality assurance. We produce a quantitative quality control based on in-air reverberation images. They are easily generated for any probe independent of the level of expertise of the operator. The results are available to the sonographer prior to clinical use and transducer status can be remotely monitored with trend analysis over time. The method presents a scheme for the classification of normal functioning and defect transducers Region of Interest selected ROIs of 65 probes based on texture analysis that automatically detects in-air reverberation regions and recognizes them as normal functioning and defect transducers. However, feature selection is done by Twin Support Vector Machine. The accuracy of these features in distinguishing normal functioning and defect transducers has been evaluated by artificial neural network, and linear support vector machine algorithms classifiers. From the analysis of results, it was found that artificial neural network classifier gave an overall classification accuracy of 100% with 100% sensitivity. The results show that it is feasible to identify defect transducers based on texture features extracted from in-air reverberation ultrasound images. This method is shown to be useful for increased accuracy and increased speed for classification of functioning and defect transducers for improving the quality assurance of ultrasound.

MP76-05 VALIDATION OF THE BEST MODELS TO PREDICT PATHOHISTOLOGY IN GERM CELL TUMOR PATIENTS UNDERGOING POSTCHEMOTHERAPY RETROPERITONEAL LYMPH NODE DISSECTION

You have accessJournal of UrologyPenile & Testicular Cancer: Penile & Testicular Cancer III (MP76)1 Apr 2020MP76-05 VALIDATION OF THE BEST MODELS TO PREDICT PATHOHISTOLOGY IN GERM CELL TUMOR PATIENTS UNDERGOING POSTCHEMOTHERAPY RETROPERITONEAL LYMPH NODE DISSECTION Tim Nestler*, Pia Paffenholz, Bettina Baeßler, Martin Hellmich, Andreas Hiester, Alessandro Nini, David Pfister, Peter Albers, and Axel Heidenreich Tim Nestler*Tim Nestler* More articles by this author , Pia PaffenholzPia Paffenholz More articles by this author , Bettina BaeßlerBettina Baeßler More articles by this author , Martin HellmichMartin Hellmich More articles by this author , Andreas HiesterAndreas Hiester More articles by this author , Alessandro NiniAlessandro Nini More articles by this author , David PfisterDavid Pfister More articles by this author , Peter AlbersPeter Albers More articles by this author , and Axel HeidenreichAxel Heidenreich More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000962.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The aim of our study was to validate and evaluate the two currently best performing prediction models (Vergouwe and Leao) for final pathohistology in NSGCT patients undergoing postchemotherapy retroperitoneal lymph node dissection (PC-RPLND) and we introduce a new radiomics approach. METHODS: We performed a retrospective analysis including 496 patients who underwent a PC-RPLND between 2008 and 2018 to validate the two prediction models using published formulas and thresholds. ROC were plotted and AUC was calculated. We determined the optimal cut point and used bootstrapping (1,000 replications) to estimate its variability. For radiomics, lymph nodes of 80 patients were identified on CT images, semiautomatically segmented with 93 radiographic features (pyRadiomics package). A linear support vector machine algorithm was applied to analyze reproducible radiomics features. A continuous reduction of features analyzed was performed using Random Forest algorithms and ROC analysis. RESULTS: In our validation cohort, the Vergouwe model had a significantly better AUC compared to Leao model (0.749 [CI 0.706-0.792] vs. 0.689 [0.642-0.736], p = 0.004) to predict benign histology. At a threshold of > 70% for the probability of benign disease, the Leao model would have avoided PC-RPLND in 8.6% with benign disease with an error rate of 5.6% for viable tumor. The Vergouwe model would avoid PC-RPLND in 23.4% with benign disease with an error rate of 12.7% for viable tumor/teratoma. Of the 93 radiomic features analyzed, 51 features were reproducible. Applying the trained algorithm on the training dataset resulted in an accuracy of 0.96 (93% sensitivity, 100% specificity, 100% PPV), on an independent validation cohort the accuracy was 0.81 (88% sensitivity, 72% specificity, 78% PPV). CONCLUSIONS: According to our data, the discriminatory accuracy of both models is not sufficient to safely select patients for surveillance strategy instead of PC-RPLND. The radiomics model is promising but needs prospective validation. Further studies including new biomarkers are needed to optimize the accuracy of potential prediction models. Source of Funding: none © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1153-e1153 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Tim Nestler* More articles by this author Pia Paffenholz More articles by this author Bettina Baeßler More articles by this author Martin Hellmich More articles by this author Andreas Hiester More articles by this author Alessandro Nini More articles by this author David Pfister More articles by this author Peter Albers More articles by this author Axel Heidenreich More articles by this author Expand All Advertisement PDF downloadLoading ...

Validation of the two best models to predict “benign” pathohistology in patients with advanced nonseminomatous germ cell tumors (NSGCT) undergoing postchemotherapy retroperitoneal lymph node dissection (PC-RPLND).

389 Background: The aim of our study is to validate and evaluate the two currently best performing prediction models (Vergouwe and Leao) for final pathohistology in NSGCT patients undergoing PC-RPLND and we introduce a new radiomics approach. Methods: We performed a retrospective analysis including 496 patients who underwent a PC-RPLND between 2008 and 2018 to validate the two prediction models using published formulas and thresholds. ROC were plotted and AUC was calculated. We determined the optimal cut point and used bootstrapping (1,000 replications) to estimate its variability. For radiomics, lymph nodes of 80 patients were identified on CT images, semiautomatically segmented with 93 radiographic features (pyRadiomics package). A linear support vector machine algorithm was applied to analyze reproducible radiomics features. A continuous reduction of features analyzed was performed using Random Forest algorithms and ROC analysis. Results: In our validation cohort, the Vergouwe model had a significantly better AUC compared to Leao model (0.749 [CI 0.706-0.792] vs. 0.689 [0.642-0.736], p=0.004) to predict benign histology. At a threshold of > 70% for the probability of benign disease, the Leao model would have avoided PC-RPLND in 8.6% with benign disease with an error rate of 5.6% for viable tumor. The Vergouwe model would avoid PC-RPLND in 23.4% with benign disease with an error rate of 12.7% for viable tumor/teratoma. Of the 93 radiomic features analyzed, 51 features were reproducible. Applying the trained algorithm on the training dataset resulted in an accuracy of 0.96 (93% sensitivity, 100% specificity, 100% PPV), on an independent validation cohort the accuracy was 0.81 (88% sensitivity, 72% specificity, 78% PPV). Conclusions: According to our data, the discriminatory accuracy of both models is not sufficient to safely select patients for surveillance strategy instead of PC-RPLND. The radiomics model is promising but needs prospective validation. Further studies including new biomarkers are needed to optimize the accuracy of potential prediction models.

Analysis of three models to predict pathohistology in patients undergoing postchemotherapy RPLND for (pcRPLND) advanced nonseminomatous germ cell tumors (NSGCT).

e16053 Background: We aim to validate the two best performing prediction models (Vergouwe and Leao) for final pathohistology in NSGCT patients undergoing pcRPLND and we introduce a new radiomics approach. Methods: A cohort of 496 patients treated between 2008 and 2018 was used to validate the 2 prediction modelsusing published formulas and thresholds.For group comparisons, we used t-test or chi-square test. ROC were plotted (sensitivity against 1-specificity) and AUC was calculated. We determined the optimal cut point and used bootstrapping (1,000 replications) to estimate its variability. A p-value of < 0.05 was considered statistically significant. For radiomics, lymph nodes identified on CT images, were semiautomatically segmented with 93 radiographic features (pyRadiomics package). A linear support vector machine algorithm was applied to analyze reproducible radiomics features. A continuous reduction of features analyzed was performed using Random Forest algorithms and ROC analysis. Results: The Vergouwe model had a significantly better AUC compared to Leao model (0.749 [CI 0.706-0.792] vs. 0.689 [0.642-0.736], p = 0.004) to predict benign histology. At a threshold of > 70% for the probability of benign disease, the Leao model would have avoided RLND in 8.6% with benign disease with an error rate of 5.6% for viable tumor. The Vergouwe model would avoid pcRPLND in 23.4% with benign disease with an error rate of 12.7% for viable tumor/teratoma. Of the 93 radiomic features analyzed, 51 features were reproducible. Applying the trained algorithm resulted in a AUC of 82% with a diagnostic sensitivity of 81% and a specificity of 83%. Conclusions: The discriminatory accuracy of both models is not sufficient to safely select patients for surveillance strategy instead of pcRPLND. The radiomics model is promising but needs prospective validation. Further studies including new biomarkers are needed to optimize the accuracy of potential prediction models.

White matter tract microstructure of the mPFC-amygdala predicts interindividual differences in placebo response related to treatment in migraine patients.

To investigate whether interindividual variability of white matter (WM) tract microstructure of the medial prefrontal cortex (mPFC)-amygdala circuit could predict 8-week placebo treatment outcomes in patients with migraine without aura (MO) using diffusion tensor imaging (DTI) with a tractography atlas-based analysis algorithm and a linear support vector machine algorithm. This study received institutional review board approval, and all subjects gave informed consent. One hundred and twenty-four MO had an 8-week sham acupuncture treatment. Patients were subdivided into recovering (MOr, >50% improvement in migraine attack frequency after treatment) and persisting (MOp, <50% reduction in number of migraine days). Neuroimaging was collected via magnetic resonance imaging (MRI) in all subjects. Patients were imaged during the interictal phase of migraine (at least 72 hr after, and not within 24 hr of a migraine) before the treatment. WM microstructures were quantified along the selected fiber pathway and were used to evaluate the discrimination performance for classifying MOr and MOp. The combined features of diffusion measures from vertices along the pathways of the mPFC-amygdala accurately discriminated MOr from MOp migraineurs with an accuracy of 84.0% (p < .005, permutation test). The most discriminative WM features that contributed to the classification were located in the external capsule and ACC/mPFC. Our findings suggested that the variability of placebo treatment outcomes in migraineurs could be predicted from priori diffusion measures along the fiber pathways of the mPFC-amygdala, which may demonstrate a potential of WM neuroimaging features as imaging markers for identifying placebo responders in migraine patients.

Multivariate pattern classification of brain white matter connectivity predicts classic trigeminal neuralgia.

Trigeminal neuralgia (TN) is a severe form of chronic facial neuropathic pain. Increasing interest in the neuroimaging of pain has highlighted changes in the root entry zone in TN, but also group-level central nervous system gray and white matter (WM) abnormalities. Group differences in neuroimaging data are frequently evaluated with univariate statistics; however, this approach is limited because it is based on single, or clusters of, voxels. By contrast, multivariate pattern analyses consider all the model's neuroanatomical features to capture a specific distributed spatial pattern. This approach has potential use as a prediction tool at the individual level. We hypothesized that a multivariate pattern classification method can distinguish specific patterns of abnormal WM connectivity of classic TN from healthy controls (HCs). Diffusion-weighted scans in 23 right-sided TN and matched controls were processed to extract whole-brain interregional streamlines. We used a linear support vector machine algorithm to differentiate interregional normalized streamline count between TN and HC. This algorithm successfully differentiated between TN and HC with an accuracy of 88%. The structural pattern emphasized WM connectivity of regions that subserve sensory, affective, and cognitive dimensions of pain, including the insula, precuneus, inferior and superior parietal lobules, and inferior and medial orbital frontal gyri. Normalized streamline counts were associated with longer pain duration and WM metric abnormality between the connections. This study demonstrates that machine-learning algorithms can detect characteristic patterns of structural alterations in TN and highlights the role of structural brain imaging for identification of neuroanatomical features associated with neuropathic pain disorders.

Classification analyses for prostate cancer, benign prostate hyperplasia and healthy subjects by SERS-based immunoassay of multiple tumour markers

Prostate cancer (PCa) is a leading cause of cancer-related death among males globally. To date, prostate-specific antigen (PSA), as a typical tumour marker, has been widely used in the early diagnosis of PCa. However, in practical clinical tests, high serum levels of PSA show a high probability for false-positive results, leading to misdiagnoses. In this study, we developed a new classification system for PCa, benign prostate hyperplasia (BPH) and healthy subjects by using a surface-enhanced Raman scattering (SERS)-based immunoassay of multiple tumour markers along with a support vector machine (SVM) algorithm. Silver nanoparticles (AgNPs) as immune probes and SiC@Ag@Ag-NPs SERS as immune substrates were constructed into a sandwich structure to serve as an ultrasensitive SERS-based immunoassay platform of tumour markers. With this assay, the limits of detection for PSA, prostate-specific membrane antigen (PSMA) and human kallikrein 2 (hK2) were as low as 0.46 fg mL−1, 1.05 fg mL−1 and 0.67 fg mL−1, respectively. Furthermore, the serum levels of PSA, PSMA and hK2 in clinical samples were successfully detected using the SERS-based immunoassay platform, and correct classifications of PCa, BPH and healthy subjects were feasible with help of the linear SVM algorithm. These results demonstrate the potential for improving the diagnostic accuracy of PCa. Overall, the linear SVM classification model with multiple tumour markers exhibited good classifications of PCa, BPH and healthy subjects, with a PCa diagnostic accuracy of 70% that was significantly superior to that of the linear SVM classification model based only on the serum level of PSA (50%). Therefore, combining the SERS-based immunoassay with pattern recognition technology can allow for comprehensive analyses of the serum levels of multiple tumour markers to effectively improve the diagnostic accuracy of cancer with potential applications in point-of-care testing.