Terms Of Area Under The Curve Research Articles

Explanatory deep learning to predict elevated pulmonary artery pressure in children with ventricular septal defects using standard chest x-rays: a novel approach.

Early risk assessment of pulmonary arterial hypertension (PAH) in patients with congenital heart disease (CHD) is crucial to ensure timely treatment. We hypothesize that applying artificial intelligence (AI) to chest x-rays (CXRs) could identify the future risk of PAH in patients with ventricular septal defect (VSD). A total of 831 VSD patients (161 PAH-VSD, 670 nonPAH-VSD) was retrospectively included. A residual neural networks (ResNet) was trained for classify VSD patients with different outcomes based on chest radiographs. The endpoint of this study was the occurrence of PAH in VSD children before or after surgery. In the validation set, the AI algorithm achieved an area under the curve (AUC) of 0.82. In an independent test set, the AI algorithm significantly outperformed human observers in terms of AUC (0.81 vs. 0.65). Class Activation Mapping (CAM) images demonstrated the model's attention focused on the pulmonary artery segment. The preliminary findings of this study suggest that the application of artificial intelligence to chest x-rays in VSD patients can effectively identify the risk of PAH.

Deep learning for classification of late gadolinium enhancement lesions based on the 16-segment left ventricular model

PurposeThis study aimed to develop and validate a deep learning-based method that allows for segmental analysis of myocardial late gadolinium enhancement (LGE) lesions. MethodsCardiac LGE data from 170 patients with coronary artery disease and non-ischemic heart disease were used for training, validation, and testing. Short-axis images were transformed to polar space after identification of the left ventricular (LV) center point and anterior right ventricular (RV) insertion point. Images were obtained after dividing the polar transformed images into segments based on the 16-segment LV model. Five different deep convolutional neural network (CNN) models were developed and validated using the labeled data, where the image after the division corresponded to a segment, and the lesion labeling was based on the 16-segment LV model. Unseen testing data were used to evaluate the performance of the lesion classification. ResultsWithout manual lesion segmentation and annotation, the proposed method showed an area under the curve (AUC) of 0.875, and a precision, recall, and F1-score of 0.723, 0.783, and 0.752, respectively for the lesion class when the pretrained ResNet50 model was tested for all slice images. The two pretrained models of ResNet50 and EfficientNet-B0 outperformed the three non-pretrained CNN models in terms of AUCs (0.873–0.875 vs. 0.834–0.841). ConclusionThe proposed method is based on learning a deep CNN model from polar transformed images to predict LGE lesions with good accuracy and does not require time-consuming annotation procedures such as lesion segmentation.

The Diagnosis of Malignant Pleural Effusion Using Tumor-Marker Combinations: A Cost-Effectiveness Analysis Based on a Stacking Model.

By incorporating the cost of multiple tumor-marker tests, this work aims to comprehensively evaluate the financial burden of patients and the accuracy of machine learning models in diagnosing malignant pleural effusion (MPE) using tumor-marker combinations. Carcinoembryonic antigen (CEA), carbohydrate antigen (CA)19-9, CA125, and CA15-3 were collected from pleural effusion (PE) and peripheral blood (PB) of 319 patients with pleural effusion. A stacked ensemble (stacking) model based on five machine learning models was utilized to evaluate the diagnostic accuracy of tumor markers. We evaluated the discriminatory accuracy of various tumor-marker combinations using the area under the curve (AUC), sensitivity, and specificity. To evaluate the cost-effectiveness of different tumor-marker combinations, a comprehensive score (C-score) with a tuning parameter w was proposed. In most scenarios, the stacking model outperformed the five individual machine learning models in terms of AUC. Among the eight tumor markers, the CEA in PE (PE.CEA) showed the best AUC of 0.902. Among all tumor-marker combinations, the PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA combination (C9 combination) achieved the highest AUC of 0.946. When w puts more weight on the cost, the highest C-score was achieved with the single PE.CEA marker. As w puts over 0.8 weight on AUC, the C-score favored diagnostic models with more expensive tumor-marker combinations. Specifically, when w was set to 0.99, the C9 combination achieved the best C-score. The stacking diagnostic model using PE.CEA is a relatively accurate and affordable choice in diagnosing MPE for patients without medical insurance or in a low economic level. The stacking model using the combination PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA is the most accurate diagnostic model and the best choice for patients without an economic burden. From a cost-effectiveness perspective, the stacking diagnostic model with PE.CA19-9 + PE.CA15-3 + PE.CEA combination is particularly recommended, as it gains the best trade-off between the low cost and high effectiveness.

Anthropometric and DXA-derived measures of body composition in relation to pre-diabetes among adults

IntroductionAbdominal obesity is the most common risk factor of pre-diabetes and diabetes. Currently, several types of indices are used for the determination of visceral fat-related abdominal obesity. To better understand...

Landslide Susceptibility Mapping: Analysis of Different Feature Selection Techniques with Artificial Neural Network Tuned by Bayesian and Metaheuristic Algorithms

The most frequent and noticeable natural calamity in the Karakoram region is landslides. Extreme landslides have occurred frequently along Karakoram Highway, particularly during monsoons, causing a major loss of life and property. Therefore, it is necessary to look for a solution to increase growth and vigilance in order to lessen losses related to landslides caused by natural disasters. By utilizing contemporary technologies, an early warning system might be developed. Artificial neural networks (ANNs) are widely used nowadays across many industries. This paper’s major goal is to provide new integrative models for assessing landslide susceptibility in a prone area in the north of Pakistan. To achieve this, the training of an artificial neural network (ANN) was supervised using metaheuristic and Bayesian techniques: Particle Swarm Optimization (PSO) algorithm, Genetic algorithm (GA), Bayesian Optimization Gaussian Process (BO_GP), and Bayesian Optimization Tree-structured Parzen Estimator (BO_TPE). In total, 304 previous landslides and the eight most prevalent conditioning elements were combined to form a geospatial database. The models were hyperparameter optimized, and the best ones were employed to generate susceptibility maps. The obtained area under the curve (AUC) accuracy index demonstrated that the maps produced by both Bayesian and metaheuristic algorithms are highly accurate. The effectiveness and efficiency of applying ANNs for landslide mapping, susceptibility analysis, and forecasting were studied in this research, and it was observed from experimentation that the performance differences for GA, BO_GP, and PSO compared to BO_TPE were relatively small, ranging from 0.32% to 1.84%. This suggests that these techniques achieved comparable performance to BO_TPE in terms of AUC. However, it is important to note that the significance of these differences can vary depending on the specific context and requirements of the ML task. Additionally, in this study, we explore eight feature selection algorithms to determine the geospatial variable importance for landslide susceptibility mapping along the Karakoram Highway (KKH). The algorithms considered include Information Gain, Variance Inflation Factor, OneR Classifier, Subset Evaluators, principal components, Relief Attribute Evaluator, correlation, and Symmetrical Uncertainty. These algorithms enable us to evaluate the relevance and significance of different geospatial variables in predicting landslide susceptibility. By applying these feature selection algorithms, we aim to identify the most influential geospatial variables that contribute to landslide occurrences along the KKH. The algorithms encompass a diverse range of techniques, such as measuring entropy reduction, accounting for attribute bias, generating single rules, evaluating feature subsets, reducing dimensionality, and assessing correlation and information sharing. The findings of this study will provide valuable insights into the critical geospatial variables associated with landslide susceptibility along the KKH. These insights can aid in the development of effective landslide mitigation strategies, infrastructure planning, and targeted hazard management efforts. Additionally, the study contributes to the field of geospatial analysis by showcasing the applicability and effectiveness of various feature selection algorithms in the context of landslide susceptibility mapping.

Intratumoral and peritumoral radiomics model based on abdominal ultrasound for predicting Ki-67 expression in patients with hepatocellular cancer.

Hepatocellular cancer (HCC) is one of the most common tumors worldwide, and Ki-67 is highly important in the assessment of HCC. Our study aimed to evaluate the value of ultrasound radiomics based on intratumoral and peritumoral tissues in predicting Ki-67 expression levels in patients with HCC. We conducted a retrospective analysis of ultrasonic and clinical data from 118 patients diagnosed with HCC through histopathological examination of surgical specimens in our hospital between September 2019 and January 2023. Radiomics features were extracted from ultrasound images of both intratumoral and peritumoral regions. To select the optimal features, we utilized the t-test and the least absolute shrinkage and selection operator (LASSO). We compared the area under the curve (AUC) values to determine the most effective modeling method. Subsequently, we developed four models: the intratumoral model, the peritumoral model, combined model #1, and combined model #2. Of the 118 patients, 64 were confirmed to have high Ki-67 expression while 54 were confirmed to have low Ki-67 expression. The AUC of the intratumoral model was 0.796 (0.649-0.942), and the AUC of the peritumoral model was 0.772 (0.619-0.926). Furthermore, combined model#1 yielded an AUC of 0.870 (0.751-0.989), and the AUC of combined model#2 was 0.762 (0.605-0.918). Among these models, combined model#1 showed the best performance in terms of AUC, accuracy, F1-score, and decision curve analysis (DCA). We presented an ultrasound radiomics model that utilizes both intratumoral and peritumoral tissue information to accurately predict Ki-67 expression in HCC patients. We believe that incorporating both regions in a proper manner can enhance the diagnostic performance of the prediction model. Nevertheless, it is not sufficient to include both regions in the region of interest (ROI) without careful consideration.

Radiomics predictive modeling from dual-time-point FDG PET Ki parametric maps: application to chemotherapy response in lymphoma

BackgroundTo investigate the use of dynamic radiomics features derived from dual-time-point (DTP-feature) [18F]FDG PET metabolic uptake rate Ki parametric maps to develop a predictive model for response to chemotherapy in lymphoma patients.MethodsWe analyzed 126 lesions from 45 lymphoma patients (responding n = 75 and non-responding n = 51) treated with chemotherapy from two different centers. Static and DTP radiomics features were extracted from baseline static PET images and DTP Ki parametric maps. Spearman’s rank correlations were calculated between static and DTP features to identify features with potential additional information. We first employed univariate analysis to determine correlations between individual features, and subsequently utilized multivariate analysis to derive predictive models utilizing DTP and static radiomics features before and after ComBat harmonization. For multivariate modeling, we utilized both the minimum redundancy maximum relevance feature selection technique and the XGBoost classifier. To evaluate our model, we partitioned the patient datasets into training/validation and testing sets using an 80/20% split. Different metrics for classification including area under the curve (AUC), sensitivity (SEN), specificity (SPE), and accuracy (ACC) were reported in test sets.ResultsVia Spearman’s rank correlations, there was negligible to moderate correlation between 32 out of 65 DTP features and some static features (ρ < 0.7); all the other 33 features showed high correlations (ρ ≥ 0.7). In univariate modeling, no significant difference between AUC of DTP and static features was observed. GLRLM_RLNU from static features demonstrated a strong correlation (AUC = 0.75, p value = 0.0001, q value = 0.0007) with therapy response. The most predictive DTP features were GLCM_Energy, GLCM_Entropy, and Uniformity, each with AUC = 0.73, p value = 0.0001, and q value < 0.0005. In multivariate analysis, the mean ranges of AUCs increased following harmonization. Use of harmonization plus combining DTP and static features was shown to provide significantly improved predictions (AUC = 0.97 ± 0.02, accuracy = 0.89 ± 0.05, sensitivity = 0.92 ± 0.09, and specificity = 0.88 ± 0.05). All models depicted significant performance in terms of AUC, ACC, SEN, and SPE (p < 0.05, Mann–Whitney test).ConclusionsOur results demonstrate significant value in harmonization of radiomics features as well as combining DTP and static radiomics models for predicting response to chemotherapy in lymphoma patients.

O-240 Does embryo categorisation by existing artificial intelligence, morphokinetic, or morphological embryo selection models correlate with blastocyst euploidy rates?

Abstract Study question Does embryo categorization by existing artificial intelligence, morphokinetic, or morphological embryo selection models correlate with blastocyst euploidy? Summary answer Our results show that existing blastocyst scoring models correlate with ploidy status. What is known already A previous study suggested that morphokinetic parameters should not be used yet as a surrogate for PGT-A to determine embryo ploidy in vitro. As for predicting pregnancy likelihood, AI models have been proposed for predicting ploidy status. Discrimination performance in terms of AUC has been reported of up to 0.80 when including metadata such as maternal age, and 0.63 when AI predictions were made based on time-lapse images alone. However, as the studies only looked at models trained for ploidy prediction, it is unclear how existing blastocyst scoring systems would perform at discriminating euploids and aneuploids. Study design, size, duration A total of 834 patients, 3,573 blastocysts were retrospectively analyzed. The cycles were stratified into five maternal age groups according to the Society for Assisted Reproductive Technology (SART) age groups (&amp;lt; 35, 35–37, 38–40, 41–42, and &amp;gt;42 years). The quality and scoring of embryos were assessed by iDAScore v1.0 (iDA, Vitrolife, Sweden), KIDScoreTM D5 v3 (KS; Vitrolife), and Gardner grading (GG). Participants/materials, setting, methods Embryos were cultured in the EmbryoScope+ and EmbryoScopeFlex (Vitrolife). iDA was automatically calculated using the iDAScore model running on the EmbryoViewer (Vitrolife). KS was calculated in EmbryoViewer after annotation of the required parameters. ICM and TE were annotated according to the Gardner grading. The degree of expansion in all blastocysts was Grade 4 due to our freezing policy. Furthermore, Gardner's scores were stratified into four grades (A: AA, B: AB BA, C: BB, D: others). Main results and the role of chance At first, the correlation between all assessment methods and euploid rates were analyzed using a trend-test. Euploidy rates significantly correlated with iDA in each age group (p = 0.035 for age &amp;lt;35 years, p &amp;lt; 0.001 for all other age groups) and decreased progressively with increasing maternal age in each score group. Furthermore, euploidy rates were significantly correlated with KS (P &amp;lt; 0.0001), except in the youngest age group (p = 0.07). They decreased progressively with increasing maternal age in each score group. Similarly, euploidy rates were significantly correlated with GG (P &amp;lt; 0.0001), except in the youngest age group (p = 0.06). They decreased progressively with increasing maternal age in each score group. Secondly, the performance of the euploidy prediction for each embryo scoring model was compared using the area under the curve (AUC) of the receiver operating characteristic curve. The AUCs for euploid prediction of iDA, KS, and GG for all ages were 0.666, 0.655, and 0.642, respectively. iDA and KS were significantly higher than Gardner grading (iDA vs. GG: p = 0.004; KS vs. GG: p = 0.02). Additionally, there was no significant difference between iDA and KS. Limitations, reasons for caution It was based on minimal stimulation and the use of natural cycle for IVF treatment, which involved only insemination by ICSI. Furthermore, the blastocyst assessment models that we tested were initially developed for pregnancy prediction and not for the prediction of the chromosomal status of an embryo. Wider implications of the findings Existing blastocyst assessment models that reflect blastocyst viability in view of implantation potential to some extent correspond with ploidy status. Therefore, existing blastocyst scoring models can be used to support the decision for embryo biopsy and/or to reduce the patient’s cost by deciding on which embryo(s) to biopsy. Trial registration number not applicable

Predicting EGFR Mutation Status in Non–Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Recent advancements in artificial intelligence (AI) render a substantial promise for epidermal growth factor receptor (EGFR) mutation status prediction in non-small cell lung cancer (NSCLC). We aimed to evaluate the performance and quality of AI algorithms that use radiomics features in predicting EGFR mutation status in patient with NSCLC. We searched PubMed (Medline), EMBASE, Web of Science, and IEEExplore for studies published up to February 28, 2022. Studies utilizing an AI algorithm (either conventional machine learning [cML] and deep learning [DL]) for predicting EGFR mutations in patients with NSLCL were included. We extracted binary diagnostic accuracy data and constructed a bivariate random-effects model to obtain pooled sensitivity, specificity, and 95% confidence interval. This study is registered with PROSPERO, CRD42021278738. Our search identified 460 studies, of which 42 were included. Thirty-five studies were included in the meta-analysis. The AI algorithms exhibited an overall area under the curve (AUC) value of 0.789 and pooled sensitivity and specificity levels of 72.2% and 73.3%, respectively. The DL algorithms outperformed cML in terms of AUC (0.822 vs. 0.775) and sensitivity (80.1% vs. 71.1%), but had lower specificity (70.0% vs. 73.8%, p-value<0.001) compared to cML. Subgroup analysis revealed that the use of positron-emission tomography/computed tomography, additional clinical information, deep feature extraction, and manual segmentation can improve diagnostic performance. DL algorithms can serve as a novel method for increasing predictive accuracy and thus have considerable potential for use in predicting EGFR mutation status in patient with NSCLC. We also suggest that guidelines on using AI algorithms in medical image analysis should be developed with a focus on oncologic radiomics.

Efficient Bioinspired Feature Selection and Machine Learning Based Framework Using Omics Data and Biological Knowledge Data Bases in Cancer Clinical Endpoint Prediction

Cancer Research has advanced during the past few years. Using high throughput technology and advances in artificial intelligence, it is now possible to improve cancer diagnosis and targeted therapy, by integrating the investigation and analysis of clinical and omics profiles. The high dimensionality and class imbalance of the majority of available data sets represent a serious challenge to the development of computational methods and tools for cancer diagnosis and biomarker discovery. Taking into account multi-omics data further complicates the undertaking. In this paper, we describe a five-step integrative architecture for dealing with the three aforementioned problems by incorporating proteomics data, protein-protein interaction networks, and signaling pathways in order to identify protein biomarkers with a direct association to cancerous patients’ overall survival (OS) and progression free interval (PFI). The core parts of this architecture are a cluster based grey wolf optimization algorithm (CB-GWO) for feature selection and a deep stacked canonical correlation autoencoder (DSCC-AE) for clinical endpoint prediction. A thorough experimental study was carried out to evaluate the performance of the proposed optimization algorithm for feature selection, as well as the performance of the deep learning model in terms of Mathew coefficient correlation (MCC) and Area under the curve (AUC) on breast, lung, colon, and rectum cancers. The results were compared to other methods in the literature. The results are very promising and show the effectiveness of the proposed framework and its ability to outperform the other algorithms and models in terms of AUC (0.91) and MCC (0.64). In addition, hub marker genes with the potential occurence of alterations in colorectal cancer, breast cancer, and lung cancer have been identified.

Identifying Prerequisite Courses in Undergraduate Biology Using Machine Learning

Many undergraduate students who matriculated in Science, Technology, Engineering and Mathematics (STEM) degree programs drop out or switch their major. Previous studies indicate that performance of students in prerequisite courses is important for attrition of students in STEM. This study analyzed demographic information, ACT/SAT score, and performance of students in freshman year courses to develop machine learning models predicting their success in earning a bachelor’s degree in biology. The predictive model based on Random Forest (RF) and Extreme Gradient Boosting (XGBoost) showed a better performance in terms of AUC (Area Under the Curve) with more balanced sensitivity and specificity than Logistic Regression (LR), K-Nearest Neighbor (KNN), and Neural Network (NN) models. An explainable machine learning approach called break-down was employed to identify important freshman year courses that could have a larger impact on student success at the biology degree program and student levels. More important courses identified at the program level can help program coordinators to prioritize their effort in addressing student attrition while more important courses identified at the student level can help academic advisors to provide more personalized, data-driven guidance to students.

Brachytherapy outcome modeling in cervical cancer patients: A predictive machine learning study on patient-specific clinical, physical and dosimetric parameters

PurposeTo predict clinical response in locally advanced cervical cancer (LACC) patients by a combination of measures, including clinical and brachytherapy parameters and several machine learning (ML) approaches. MethodsBrachytherapy features such as insertion approaches, source metrics, dosimetric, and clinical measures were used for modeling. Four different ML approaches, including LASSO, Ridge, support vector machine (SVM), and Random Forest (RF), were applied to extracted measures for model development alone or in combination. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristics curve, sensitivity, specificity, and accuracy. Our results were compared with a reference model developed by simple logistic regression applied to three distinct clinical features identified by previous papers. ResultsOne hundred eleven LACC patients were included. Nine data sets were obtained based on the features, and 36 predictive models were built. In terms of AUC, the model developed using RF applied to dosimetric, physical, and total BT sessions features were found as the most predictive [AUC; 0.82 (0.95 confidence interval (CI); 0.79 –0.93), sensitivity; 0.79, specificity; 0.76, and accuracy; 0.77]. The AUC (0.95 CI), sensitivity, specificity, and accuracy for the reference model were found as 0.56 (0.52 ...0.68), 0.51, 0.51, and 0.48, respectively. Most RF models had significantly better performance than the reference model (Bonferroni corrected p-value < 0.0014). ConclusionBrachytherapy response can be predicted using dosimetric and physical parameters extracted from treatment parameters. Machine learning algorithms, including Random Forest, could play a critical role in such predictive modeling.

Modelling the no-show of patients to exam appointments of computed tomography.

Patients' no-shows negatively impact healthcare systems, leading to resources' underutilisation, efficiency loss, and cost increase. Predicting no-shows is key to developing strategies that counteract their effects. In this paper, we propose a model to predict the no-show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital. We carried out a retrospective study on 8382 appointments to computed tomography (CT) exams between January and December 2017. Penalised logistic regression and multivariate logistic regression were used to model the influence of 15 candidate variables on patients' no-shows. The predictive capabilities of the models were evaluated by analysing the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). The no-show rate in computerised tomography exams appointments was 6.65%. The two models performed similarly in terms of AUC. The penalised logistic regression model was selected using the parsimony criterion, with 8 of the 15 variables analysed appearing as significant. One of the variables included in the model (number of exams scheduled in the previous year) had not been previously reported in the related literature. Our findings may be used to guide the development of strategies to reduce the no-show of patients to exam appointments.

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data

BackgroundMedical decision-making impacts both individual and public health. Clinical scores are commonly used among various decision-making models to determine the degree of disease deterioration at the bedside. AutoScore was proposed as a useful clinical score generator based on machine learning and a generalized linear model. However, its current framework still leaves room for improvement when addressing unbalanced data of rare events. MethodsUsing machine intelligence approaches, we developed AutoScore-Imbalance, which comprises three components: training dataset optimization, sample weight optimization, and adjusted AutoScore. Baseline techniques for performance comparison included the original AutoScore, full logistic regression, stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), full random forest, and random forest with a reduced number of variables. These models were evaluated based on their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i.e., mean value of sensitivity and specificity). By utilizing a publicly accessible dataset from Beth Israel Deaconess Medical Center, we assessed the proposed model and baseline approaches to predict inpatient mortality. ResultsAutoScore-Imbalance outperformed baselines in terms of AUC and balanced accuracy. The nine-variable AutoScore-Imbalance sub-model achieved the highest AUC of 0.786 (0.732–0.839), while the eleven-variable original AutoScore obtained an AUC of 0.723 (0.663–0.783), and the logistic regression with 21 variables obtained an AUC of 0.743 (0.685–0.801). The AutoScore-Imbalance sub-model (using a down-sampling algorithm) yielded an AUC of 0.771 (0.718–0.823) with only five variables, demonstrating a good balance between performance and variable sparsity. Furthermore, AutoScore-Imbalance obtained the highest balanced accuracy of 0.757 (0.702–0.805), compared to 0.698 (0.643–0.753) by the original AutoScore and the maximum of 0.720 (0.664–0.769) by other baseline models. ConclusionsWe have developed an interpretable tool to handle clinical data imbalance, presented its structure, and demonstrated its superiority over baselines. The AutoScore-Imbalance tool can be applied to highly unbalanced datasets to gain further insight into rare medical events and facilitate real-world clinical decision-making.

A deep matrix completion method for imputing missing histological data in breast cancer by integrating DCE-MRI radiomics.

Clinical indicators of histological information are important for breast cancer treatment and operational decision making, but these histological data suffer from frequent missing values due to various experimental/clinical reasons. The limited amount of histological information from breast cancer samples impedes the accuracy of data imputation. The purpose of this study was to impute missing histological data, including Ki-67 expression level, luminal A subtype, and histological grade, by integrating tumor radiomics. To this end, a deep matrix completion (DMC) method was proposed for imputing missing histological data using nonmissing features composed of histological and tumor radiomics (termed radiohistological features). DMC finds a latent nonlinear association between radiohistological features across all samples and samples for all the features. Radiomic features of morphologic, statistical, and texture were extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) inside the tumor. Experiments on missing histological data imputation were performed with a variable number of features and missing data rates. The performance of the DMC method was compared with those of the nonnegative matrix factorization (NMF) and collaborative filtering (MCF)-based data imputation methods. The area under the curve (AUC) was used to assess the performance of missing histological data imputation. By integrating radiomics from DCE-MRI, the DMC method showed significantly better performance in terms of AUC than that using only histological data. Additionally, DMC using 120 radiomic features showed an optimal prediction performance (AUC=0.793), which was better than the NMF (AUC=0.756) and MCF methods (AUC=0.706; corrected p=0.001). The DMC method consistently performed better than the NMF and MCF methods with a variable number of radiomic features and missing data rates. DMC improves imputation performance by integrating tumor histological and radiomics data. This study transforms latent imaging-scale patterns for interactions with molecular-scale histological information and is promising in the tumor characterization and management of patients.

Automated Skin Lesion Classification on Ultrasound Images.

The growing incidence of skin cancer makes computer-aided diagnosis tools for this group of diseases increasingly important. The use of ultrasound has the potential to complement information from optical dermoscopy. The current work presents a fully automatic classification framework utilizing fully-automated (FA) segmentation and compares it with classification using two semi-automated (SA) segmentation methods. Ultrasound recordings were taken from a total of 310 lesions (70 melanoma, 130 basal cell carcinoma and 110 benign nevi). A support vector machine (SVM) model was trained on 62 features, with ten-fold cross-validation. Six classification tasks were considered, namely all the possible permutations of one class versus one or two remaining classes. The receiver operating characteristic (ROC) area under the curve (AUC) as well as the accuracy (ACC) were measured. The best classification was obtained for the classification of nevi from cancerous lesions (melanoma, basal cell carcinoma), with AUCs of over 90% and ACCs of over 85% obtained with all segmentation methods. Previous works have either not implemented FA ultrasound-based skin cancer classification (making diagnosis more lengthy and operator-dependent), or are unclear in their classification results. Furthermore, the current work is the first to assess the effect of implementing FA instead of SA classification, with FA classification never degrading performance (in terms of AUC or ACC) by more than 5%.

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients.

Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions.Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting–XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables.Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64–0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69).Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.

Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China

This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides.

Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery.

Study Design:retrospective cohort study.Objectives:To test and compare 2 machine learning algorithms to define characteristics associated with candidates for ambulatory same day laminectomy surgery.Methods:The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent single level laminectomy in 2017 and 2018. The main outcome was ambulatory same day discharge. Study variables of interest included demographic information, comorbidities, preoperative laboratory values, and intra-operative information. Two machine learning predictive modeling algorithms, artificial neural network (ANN) and random forest, were trained to predict same day discharge. The quality of models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) measures.Results:Among 35,644 patients, 13,230 (37.1%) were discharged on the day of surgery. Both ANN and RF demonstrated a satisfactory model quality in terms of AUC (0.77 and 0.77), accuracy (0.69 and 0.70), sensitivity (0.83 and 0.58), specificity (0.55 and 0.80), PPV (0.77 and 0.69), and NPV (0.64 and 0.70). Both models highlighted several important predictive variables, including age, duration of operation, body mass index and preoperative laboratory values including, hematocrit, platelets, white blood cells, and alkaline phosphatase.Conclusion:Machine learning approaches provide a promising tool to identify candidates for ambulatory laminectomy surgery. Both machine learning algorithms highlighted the as yet unrecognized importance of preoperative laboratory testing on patient pathway design.

How to score acute pancreatitis in the emergency setting: five systems against ED-SAS

To assess the effectiveness of a new scale known as “Emergency department SpO2 (peripheral capillary oxygen saturation), age, and SIRS (Systemic inflammatory response syndrome)” (ED-SAS) that can be used to predict prognosis within 24 hours following presentation compatible with acute pancreatitis in patients admitted to the emergency department. This research project was conducted as a single-center, retrospective, cohort study. The Acute Physiology and Chronic Health Evaluation II (APACHE II), SIRS, Bedside Index for Severity in Acute Pancreatitis (BISAP), ED-SAS, modified Glasgow Scale and Ranson criteria scoring of the patients were evaluated using their presentation data screened from the hospital automation system. Then, the efficiencies of these evaluation systems were compared using the receiving operating curve (ROC). The conformity of the data to the normal distribution was checked with the Kolmogorov-Smirnov test. The ROC analyses were employed to identify the cut-off values of the scoring systems in calculating death rates. The method developed by DeLong et al. was used to compare the ROC curves of the scoring systems. The study has been completed with 235 patients, 91 (38.7%) male and 144 (61.3%) female, with a mean age of 63.1 ± 17.7 years. In the ROC analysis of the ED-SAS evaluation tool to predict death rates, the area under the curve (AUC) value was found to be 0.85 (95% confidence interval: 0.79–0.89), and the Youden index was 0.62, with a p value of 0.001. Mortality prediction with ED-SAS significantly differed compared to the Ranson and SIRS scoring systems (P = 0.001 and P = 0.03, respectively). However, no statistically significant difference was found in the comparison of the ED-SAS score with the modified Glasgow and APACHE II scores (P = 0.12 and P = 0.54, respectively). It was concluded that the Baseband ED-SAS scores provided equally significant results in terms of AUC at the 95% confidence interval (P = 0.05). Statistical analyses revealed that the APACHE II, SIRS, BISAP, modified Glasgow and ED-SAS scores were found to be significantly higher among the dead in comparison to the survivors (P &lt; 0.05). ED-SAS constitutes a simple, fast, expedient and effective evaluation system that can be utilized to predict mortality in acute pancreatitis in the emergency setting.