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PurposeOur goal was to identify new anticancer agents approved by the US Food and Drug Administration (FDA) and the European Medical Agency (EMA) since the 2016 MASCC/ESMO antiemetic update and classify their emetic potential.MethodsThe MASCC/ESMO Expert Panel classified the emetogenicity of the identified new antineoplastic agents based on nonsystematic reviews of randomized controlled trials, analysis of product labeling, and evaluation of emetic classification in other international guidelines and informal consensus. The emetogenic classification system for oral anticancer agents was revised into two emetic risk categories (minimal-low; moderate-high) to be consistent with the system reported by ASCO (American Society of Clinical Oncology) in their 2017 guideline update. The previously employed four emetic risk classification categories for intravenously administered antineoplastic agents were retained for this update.ResultsFrom June 2015 to January 2023, 107 new antineoplastic agents (44 intravenously administered and 63 orally administered agents) were identified. The reported incidence of vomiting varied significantly across studies for many agents, especially for oral anticancer agents.ConclusionThe MASCC/ESMO Expert Panel acknowledges the limitations of our efforts to classify the emetic potential of anticancer agents, especially the imprecision associated with oral agents. However, we have attempted to provide a reasonable approximation of the emetic risk associated with new antineoplastic agents by searching the available literature and reviewing other available international antiemetic guidelines.

In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named βrisk. Since transactions with βrisk > = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to contribute to the development of such studies. [ABSTRACT FROM AUTHOR]

Background: There are few CT-based deep learning (DL) studies on thymoma according to the World Health Organization classification. Purpose: To develop a CT-based DL model to distinguish between low-risk and high-risk thymoma and to compare the diagnostic performance of radiologists with and without the DL model. Material and Methods: 159 patients with 160 thymomas were included. A fine-tuning VGG16 network model with Adam optimizer was used, followed by k-fold cross validation. The dataset consisted of three axial slices, including the maximum tumor size from the CT volume data. The data were augmented 50 times by rotation, zoom, shear, and horizontal/vertical flip. Three independent networks for the CT dataset were considered, and the result was determined by voting. Three radiologists independently diagnosed thymomas with and without the model. The area under the curve (AUC) of the diagnostic performance was compared using receiver operating characteristic analysis. Results: Accuracy of the DL model was 71.3%. Diagnostic performance of the radiologists was as follows: AUC and accuracy without the DL model, 0.61–0.68 and 61.9%–69.3%; and with the DL model, 0.66–0.69 and 68.1%–70.0%, respectively. AUC of the diagnostic performance showed no significant differences between radiologists with and without the DL model. The DL model tended to increase the diagnostic accuracy, but AUC was not significantly improved. Conclusion: Diagnostic performance of the DL was comparable to that of radiologists. The DL model assistance tended to increase diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Objectives: This study evaluates the efficacy and toxicity of image-guided brachytherapy combined with or without external beam radiotherapy (IGBT ± EBRT) as definitive treatment for patients with inoperable endometrial cancer (IOEC), in addition to establishing a risk classification to predict prognosis. Methods: Fifty-one IOEC patients who underwent IGBT ± EBRT at Peking Union Medical College Hospital from January 2012 to December 2021 were retrospectively analyzed, of which 42 patients (82.4%) were treated with IGBT + EBRT and 9 patients (17.6%) with IGBT alone. Establishing risk classification based on FIGO 2009 staging and biopsy pathology, stage III/IV, non-endometrioid, or Grade 3 endometrioid cancer were included in the high-risk group (n = 25), and stage I/II with Grade 1–2 endometrioid cancer was included in the low-risk group (n = 26). Results: The median follow-up time was 58.0 months (IQR, 37.0–69.0). Clinical complete remission (CR) was achieved in 92.2% of patients after radiotherapy (n = 47). The cumulative incidences of locoregional and distant failure were 19.6% (n = 10) and 7.8% (n = 4), respectively. A total of 20 patients died (39.2%), including 10 cancer-related deaths (19.6%) and 10 comorbidity-related deaths (19.6%). The 5-year locoregional control (LRC), time to progression (TTP), overall survival (OS), and cancer-specific survival (CSS) were 76.9%, 71.2%, 59.4%, and 77.0%, respectively. No Grade 3 or above acute or late toxicities were reported. In univariate analysis, LRC, TTP, and CSS were significantly higher in the low-risk group than in the high-risk group (P < 0.05). After adjusting for age, number of comorbidities, radiotherapy modality, and chemotherapy, the low-risk group was still significantly better than the high-risk group in terms of LRC (HR = 6.10, 95% CI: 1.18–31.45, P = 0.031), TTP (HR = 8.07, 95% CI: 1.64–39.68, P = 0.010) and CSS (HR = 6.29, 95% CI: 1.19–33.10, P = 0.030). Conclusions: IGBT ± EBRT is safe and effective as definitive treatment for IOEC patients, achieving satisfactory locoregional control, favorable survival outcomes, and low toxicity. Risk classification based on FIGO 2009 staging and biopsy pathology is an independent prognostic factor for LRC, TTP, and CSS. [ABSTRACT FROM AUTHOR]

This study aimed to pinpoint independent predictors influencing overall survival (OS) and cancer-specific survival (CSS) in elderly patients with small cell lung cancer (SCLC) brain metastasis (BM), and to create and validate nomograms for OS and CSS prediction. Data from elderly SCLC BM patients were extracted out of the Surveillance, Epidemiology, and End Results database, including 1200 patients identified from 2010 and 2015 who were randomly allocated into a training set and an internal validation set at a proportion of 7:3, and 666 patients diagnosed between 2018 and 2020 as a temporal external validation set. Independent predictors for OS and CSS were determined through univariate Cox analysis, least absolute shrinkage and selection operator analysis, and multivariate Cox analysis sequentially. Nomograms for OS and CSS were constructed, and validated by the internal and temporal external validation sets. Age, N stage, chemotherapy, and liver metastasis were determined as independent predictors of OS and CSS, while radiotherapy and surgery were not. Nomograms were constructed based on these independent predictors. The results of the receiver operator characteristic curves, the areas under the curve and calibration curve demonstrated that the nomograms exhibited commendable discriminative ability and calibration. Moreover, decision curve analysis, net reclassification improvement, and integrated discrimination improvement also suggested that the nomograms possessed superior clinical usefulness and predictive capability relative to the TNM system. Prognostic nomograms for elderly patients with SCLC BM have been developed, demonstrating good performance in terms of accuracy, reliability, and practicality. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate and compare the diagnostic performances of whole-lesion iodine map (IM) histogram analysis and single-slice IM measurement in the risk classification of gastrointestinal stromal tumors (GISTs). Methods: Thirty-seven patients with GISTs, including 19 with low malignant underlying GISTs (LG-GISTs) and 18 with high malignant underlying GISTs (HG-GISTs), were evaluated with dual-energy computed tomography (DECT). Whole-lesion IM histogram parameters (mean; median; minimum; maximum; standard deviation; variance; 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile; kurtosis, skewness, and entropy) were computed for each lesion. In other sessions, iodine concentrations (ICs) were derived from the IM by placing regions of interest (ROIs) on the tumor slices and normalizing them to the iodine concentration in the aorta. Both quantitative analyses were performed on the venous phase images. The diagnostic accuracies of the two methods were assessed and compared. Results: The minimum, maximum, 1st, 10th, and 25th percentile of the whole-lesion IM histogram and the IC and normalized IC (NIC) of the single-slice IC measurement significantly differed between LG- and HG-GISTs (p < 0.001 – p = 0.042). The minimum value in the histogram analysis (AUC = 0.844) and the NIC in the single-slice measurement analysis (AUC = 0.886) showed the best diagnostic performances. The NIC of single-slice measurements had a diagnostic performance similar to that of the whole-lesion IM histogram analysis (p = 0.618). Conclusions: Both whole-lesion IM histogram analysis and single-slice IC measurement can differentiate LG-GISTs and HG-GISTs with similar diagnostic performances. [ABSTRACT FROM AUTHOR]

Purpose: To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs). Methods: A total of 980 ultrasound (US) images from 245 GIST patients were retrospectively collected. These images were randomly divided (6:2:2) into a training set, a validation set, and an internal test set. Additionally, 188 US images from 47 prospective GIST patients were collected to evaluate the segmentation and diagnostic performance of the model. Five deep learning‐based segmentation networks, namely, UNet, FCN, DeepLabV3+, Swin Transformer, and SegNeXt, were employed, along with the ResNet 18 classification network, to select the most suitable network combination. The performance of the segmentation models was evaluated using metrics such as the intersection over union (IoU), Dice similarity coefficient (DSC), recall, and precision. The classification performance was assessed based on accuracy and the area under the receiver operating characteristic curve (AUROC). Results: Among the compared models, SegNeXt‐ResNet18 exhibited the best segmentation and classification performance. On the internal test set, the proposed model achieved IoU, DSC, precision, and recall values of 82.1, 90.2, 91.7, and 88.8%, respectively. The accuracy and AUC for GIST risk prediction were 87.4 and 92.0%, respectively. On the external test set, the segmentation models exhibited IoU, DSC, precision, and recall values of 81.0, 89.5, 92.8, and 86.4%, respectively. The accuracy and AUC for GIST risk prediction were 86.7 and 92.5%, respectively. Conclusion: This two‐stage SegNeXt‐ResNet18 model achieves automatic segmentation and risk stratification prediction for GISTs and demonstrates excellent segmentation and classification performance. [ABSTRACT FROM AUTHOR]

Summary: Allogeneic stem cell transplantation (alloSCT) is the only curative treatment option for patients with high-risk myelofibrosis (MF). However, it is important to bear in mind that alloSCT in MF is associated with a nonrelapse mortality that should not be underestimated. Therefore, both exact disease risk categorization and thorough evaluation of the individual transplant-related risk are mandatory to identify those patients to whom alloSCT should be offered. This short review is intended to provide a concise overview on relevant aspects to be considered for patient selection, planning, and performing alloSCT. [ABSTRACT FROM AUTHOR]

Abstract Objectives This study evaluates the efficacy and toxicity of image-guided brachytherapy combined with or without external beam radiotherapy (IGBT ± EBRT) as definitive treatment for patients with inoperable endometrial cancer (IOEC), in addition to establishing a risk classification to predict prognosis. Methods Fifty-one IOEC patients who underwent IGBT ± EBRT at Peking Union Medical College Hospital from January 2012 to December 2021 were retrospectively analyzed, of which 42 patients (82.4%) were treated with IGBT + EBRT and 9 patients (17.6%) with IGBT alone. Establishing risk classification based on FIGO 2009 staging and biopsy pathology, stage III/IV, non-endometrioid, or Grade 3 endometrioid cancer were included in the high-risk group (n = 25), and stage I/II with Grade 1–2 endometrioid cancer was included in the low-risk group (n = 26). Results The median follow-up time was 58.0 months (IQR, 37.0–69.0). Clinical complete remission (CR) was achieved in 92.2% of patients after radiotherapy (n = 47). The cumulative incidences of locoregional and distant failure were 19.6% (n = 10) and 7.8% (n = 4), respectively. A total of 20 patients died (39.2%), including 10 cancer-related deaths (19.6%) and 10 comorbidity-related deaths (19.6%). The 5-year locoregional control (LRC), time to progression (TTP), overall survival (OS), and cancer-specific survival (CSS) were 76.9%, 71.2%, 59.4%, and 77.0%, respectively. No Grade 3 or above acute or late toxicities were reported. In univariate analysis, LRC, TTP, and CSS were significantly higher in the low-risk group than in the high-risk group (P

Autonomous outdoor moving objects like cars, motorcycles, bicycles, and pedestrians present different risks to the safety of Visually Impaired People (VIPs). Consequently, many camera-based VIP mobility assistive solutions have resulted. However, they fail to guarantee VIP safety in practice, i.e., they cannot effectively prevent collisions with more dangerous threats moving at higher speeds, namely, Critical Moving Objects (CMOs). This paper presents the first practical camera-based VIP mobility assistant scheme, ARAware, that effectively identifies CMOs in real-time to give the VIP more time to avoid danger through simultaneously addressing CMO identification, CMO risk level evaluation and classification, and prioritised CMO warning notification. Experimental results based on our real-world prototype demonstrate that ARAware accurately identifies CMOs (with 97.26% mAR and 88.20% mAP) in real-time (with a 32 fps processing speed for 30 fps incoming video). It precisely classifies CMOs according to their risk levels (with 100% mAR and 91.69% mAP), and warns in a timely manner about high-risk CMOs while effectively reducing false alarms by postponing the warning of low-risk CMOs. Compared to the closest state-of-the-art approach, DEEP-SEE, ARAware achieves significantly higher CMO identification accuracy (by 42.62% in mAR and 10.88% in mAP), with a 93% faster end-to-end processing speed. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer‐related death. Gleason score (GS) is the primary driver of PCa risk‐stratification and medical decision‐making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non‐invasive method to further characterize PCa, providing additional anatomical and functional information. Meanwhile, the diagnostic power of MRI is limited by qualitative or, at best, semi‐quantitative interpretation criteria, leading to inter‐reader variability. Purposes: Computer‐aided diagnosis employing quantitative MRI analysis has yielded promising results in non‐invasive prediction of GS. However, convolutional neural networks (CNNs) do not implicitly impose a frame of reference to the objects. Thus, CNNs do not encode the positional information properly, limiting method robustness against simple image variations such as flipping, scaling, or rotation. Capsule network (CapsNet) has been proposed to address this limitation and achieves promising results in this domain. In this study, we develop a 3D Efficient CapsNet to stratify GS‐derived PCa risk using T2‐weighted (T2W) MRI images. Methods: In our method, we used 3D CNN modules to extract spatial features and primary capsule layers to encode vector features. We then propose to integrate fully‐connected capsule layers (FC Caps) to create a deeper hierarchy for PCa grading prediction. FC Caps comprises a secondary capsule layer which routes active primary capsules and a final capsule layer which outputs PCa risk. To account for data imbalance, we propose a novel dynamic weighted margin loss. We evaluate our method on a public PCa T2W MRI dataset from the Cancer Imaging Archive containing data from 976 patients. Results: Two groups of experiments were performed: (1) we first identified high‐risk disease by classifying low + medium risk versus high risk; (2) we then stratified disease in one‐versus‐one fashion: low versus high risk, medium versus high risk, and low versus medium risk. Five‐fold cross validation was performed. Our model achieved an area under receiver operating characteristic curve (AUC) of 0.83 and 0.64 F1‐score for low versus high grade, 0.79 AUC and 0.75 F1‐score for low + medium versus high grade, 0.75 AUC and 0.69 F1‐score for medium versus high grade and 0.59 AUC and 0.57 F1‐score for low versus medium grade. Our method outperformed state‐of‐the‐art radiomics‐based classification and deep learning methods with the highest metrics for each experiment. Our divide‐and‐conquer strategy achieved weighted Cohen's Kappa score of 0.41, suggesting moderate agreement with ground truth PCa risks. Conclusions: In this study, we proposed a novel 3D Efficient CapsNet for PCa risk stratification and demonstrated its feasibility. This developed tool provided a non‐invasive approach to assess PCa risk from T2W MR images, which might have potential to personalize the treatment of PCa and reduce the number of unnecessary biopsies. [ABSTRACT FROM AUTHOR]

Using different machine learning models CT-based radiomics to integrate clinical radiological features to discriminating the risk stratification of pheochromocytoma/paragangliomas (PPGLs). The present study included 201 patients with PPGLs from three hospitals (training set: n = 125; external validation set: n = 45; external test set: n = 31). Patients were divided into low-risk and high-risk groups using a staging system for adrenal pheochromocytoma and paraganglioma (GAPP). We extracted and selected CT radiomics features, and built radiomics models using support vector machines (SVM), k-nearest neighbors, random forests, and multilayer perceptrons. Using receiver operating characteristic curve analysis to select the optimal radiomics model, a combined model was built using the output of the optimal radiomics model and clinical radiological features, and its accuracy and clinical applicability were evaluated using calibration curves and clinical decision curve analysis (DCA). Finally, 13 radiomics features were selected to construct machine learning models. In the radiomics model, the SVM model demonstrated higher accuracy and stability, with an AUC value of 0.915 in the training set, 0.846 in external validation set, and 0.857 in external test set. Combining the outputs of SVM models with two clinical radiological features, a combined model constructed has demonstrated optimal risk stratification ability for PPGLs with an AUC of 0.926 for the training set, 0.883 for the external validation set, and 0.899 for the external test set. The calibration curve and DCA show good calibration accuracy and clinical effectiveness for the combined model. Combined model that integrates radiomics and clinical radiological features can discriminate the risk stratification of PPGLs. [ABSTRACT FROM AUTHOR]

This paper proposes a new boosting machine based on forward stagewise additive modeling with cost-complexity pruned trees. In the Tweedie case, it deals directly with observed responses, not gradients of the loss function. Trees included in the score progressively reduce to the root-node one, in an adaptive way. The proposed Adaptive Boosting Tree (ABT) machine thus automatically stops at that time, avoiding to resort to the time-consuming cross validation approach. Case studies performed on motor third-party liability insurance claim data demonstrate the performances of the proposed ABT machine for ratemaking, in comparison with regular gradient boosting trees. [ABSTRACT FROM AUTHOR]

BackgroundMetabolic dysfunction-associated steatotic liver disease (MASLD, formerly NAFLD), emerges as major cause of morbidity and mortality globally, with chronic patients facing increased risk. Guidelines on MASLD management in primary care (PC) are limited. This study aimed to develop and evaluate a clinical care pathway for use in PC to improve MASLD screening and management, including early detection, communication and treatment, in three European countries (Greece, Spain, the Netherlands).MethodsAn international multidisciplinary panel of experts oversaw pathway development, which was designed as a two-step algorithm with defined and sequenced tasks. To evaluate algorithm implementation, a controlled pilot study was conducted. Patients at risk of MASLD were assigned to general practitioners (GPs) trained in algorithm implementation (active group) or usual care (control group) and followed for 4–8 weeks. Primary outcomes were the number of patients screened for MASLD, managed in PC and referred to specialists.ResultsIn this algorithm, patients with metabolic or liver dysfunction, confirmed MASLD or cardiovascular disease are screened with FIB-4 and classified as having risk of low-level (FIB-4

Abstract In this paper, we propose a novel pricing model for delivery insurance in a food delivery company in Latin America, with the aim of reducing the high costs associated with the premium paid to the insurer. To achieve this goal, a thorough analysis was conducted to estimate the probability of losses based on delivery routes, transportation modes, and delivery drivers’ profiles. A large amount of data was collected and used as a database, and various statistical models and machine learning techniques were employed to construct a comprehensive risk profile and perform risk classification. Based on the risk classification and the estimated probability associated with it, a new pricing model for delivery insurance was developed using advanced mathematical algorithms and machine learning techniques. This new pricing model took into account the pattern of loss occurrence and high and low-risk behaviors, resulting in a significant reduction of insurance costs for both the contracting company and the insurer. The proposed pricing model also allowed for greater flexibility in insurance contracting, making it more accessible and appealing to delivery drivers. The use of estimated loss probabilities and a risk score for the pricing of delivery insurance proved to be a highly effective and efficient alternative for reducing the high costs associated with insurance, while also improving the profitability and competitiveness of the food delivery company in Latin America.

Water treatment public–private partnership (PPP) projects are pivotal for sustainable water management but are often challenged by complex risk factors. Efficient risk management in these projects is crucial, yet traditional methodologies often fall short of addressing the dynamic and intricate nature of these risks. Addressing this gap, this comprehensive study introduces an advanced risk classification prediction model tailored for water treatment PPP projects, aimed at enhancing risk management capabilities. The proposed model encompasses an intricate evaluation of crucial risk areas: the natural and ecological environments, socio-economic factors, and engineering entities. It delves into the complex relationships between these risk elements and the overall risk profile of projects. Grounded in a sophisticated ensemble learning framework employing stacking, our model is further refined through a weighted voting mechanism, significantly elevating its predictive accuracy. Rigorous validation using data from the Jiujiang City water environment system project Phase I confirms the model's superiority over standard machine learning models. The development of this model marks a significant stride in risk classification for water treatment PPP projects, offering a powerful tool for enhancing risk management practices. Beyond accurately predicting project risks, this model also aids in developing effective government risk management strategies. HIGHLIGHTS Pioneers data-driven risk management in water treatment PPP projects using machine learning.; Introduces an effective weighted voting mechanism for handling data irregularities in risk assessment.; Demonstrates superior performance of WETPR-SVM model over conventional machine learning models.;

In this paper, we propose a novel pricing model for delivery insurance in a food delivery company in Latin America, with the aim of reducing the high costs associated with the premium paid to the insurer. To achieve this goal, a thorough analysis was conducted to estimate the probability of losses based on delivery routes, transportation modes, and delivery drivers' profiles. A large amount of data was collected and used as a database, and various statistical models and machine learning techniques were employed to construct a comprehensive risk profile and perform risk classification. Based on the risk classification and the estimated probability associated with it, a new pricing model for delivery insurance was developed using advanced mathematical algorithms and machine learning techniques. This new pricing model took into account the pattern of loss occurrence and high and low-risk behaviors, resulting in a significant reduction of insurance costs for both the contracting company and the insurer. The proposed pricing model also allowed for greater flexibility in insurance contracting, making it more accessible and appealing to delivery drivers. The use of estimated loss probabilities and a risk score for the pricing of delivery insurance proved to be a highly effective and efficient alternative for reducing the high costs associated with insurance, while also improving the profitability and competitiveness of the food delivery company in Latin America. [ABSTRACT FROM AUTHOR]

The interrelation and complementary nature of multi-omics data can provide valuable insights into the intricate molecular mechanisms underlying diseases. However, challenges such as limited sample size, high data dimensionality and differences in omics modalities pose significant obstacles to fully harnessing the potential of these data. The prior knowledge such as gene regulatory network and pathway information harbors useful gene–gene interaction and gene functional module information. To effectively integrate multi-omics data and make full use of the prior knowledge, here, we propose a Multilevel-graph neural network (GNN): a hierarchically designed deep learning algorithm that sequentially leverages multi-omics data, gene regulatory networks and pathway information to extract features and enhance accuracy in predicting survival risk. Our method achieved better accuracy compared with existing methods. Furthermore, key factors nonlinearly associated with the tumor pathogenesis are prioritized by employing two interpretation algorithms (i.e. GNN-Explainer and IGscore) for neural networks, at gene and pathway level, respectively. The top genes and pathways exhibit strong associations with disease in survival analyses, many of which such as SEC61G and CYP27B1 are previously reported in the literature. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to assess the prognostic outcomes in mRCC patients receiving second‐line TKI following first‐line IO combination therapy. Methods: This study retrospectively included 243 mRCC patients receiving second‐line TKI after first‐line IO combination therapy: nivolumab plus ipilimumab (n = 189, IO–IO group) and either pembrolizumab plus axitinib or avelumab plus axitinib (n = 54, IO–TKI group). Oncological outcomes between the two groups were compared, and prognostication systems were developed for these patients. Results: In the IO–IO and IO–TKI groups, the objective response rates to second‐line TKI were 34.4% and 25.9% (p = 0.26), the median PFS periods were 9.7 and 7.1 months (p = 0.79), and the median OS periods after the introduction of second‐line TKI were 23.1 and 33.5 months (p = 0.93), respectively. Among the several factors examined, non‐CCRCC, high CRP, and low albumin levels were identified as independent predictors of both poor PFS and OS by multivariate analyses. It was possible to precisely classify the patients into 3 risk groups regarding both PFS and OS according to the positive numbers of the independent prognostic factors. Furthermore, the c‐indices of this study were superior to those of previous systems as follows: 0.75, 0.64, and 0.61 for PFS prediction and 0.76, 0.70, and 0.65 for OS prediction by the present, IMDC, and MSKCC systems, respectively. Conclusions: There were no significant differences in the prognostic outcomes after introducing second‐line TKI between the IO–IO and IO–TKI groups, and the histopathology, CRP and albumin levels had independent impacts on the prognosis in mRCC patients receiving second‐line TKI, irrespective of first‐line IO combination therapies. [ABSTRACT FROM AUTHOR]

Background: Toxicity or treatment failure related to drug-drug interactions (DDIs) are known to significantly affect morbidity and hospitalization rates. Despite the availability of numerous databases for DDIs identification and management, their information often differs. Oral anticoagulants are deemed at risk of DDIs and a leading cause of adverse drug events, most of which being preventable. Although many databases include DDIs involving anticoagulants, none are specialized in them. Aim and method: This study aims to compare the DDIs information content of four direct oral anticoagulants and two vitamin K antagonists in three major DDI databases used in Switzerland: Lexi-Interact, Pharmavista, and MediQ. It evaluates the consistency of DDIs information in terms of differences in severity rating systems, mechanism of interaction, extraction and documentation processes and transparency. Results: This study revealed 2'496 DDIs for the six anticoagulants, with discrepant risk classifications. Only 13.2% of DDIs were common to all three databases. Overall concordance in risk classification (high, moderate, and low risk) was slight (Fleiss' kappa = 0.131), while high-risk DDIs demonstrated a fair agreement (Fleiss' kappa = 0.398). The nature and the mechanism of the DDIs were more consistent across databases. Qualitative assessments highlighted differences in the documentation process and transparency, and similarities for availability of risk classification and references. Discussion: This study highlights the discrepancies between three commonly used DDI databases and the inconsistency in how terminology is standardised and incorporated when classifying these DDIs. It also highlights the need for the creation of specialised tools for anticoagulant-related interactions. [ABSTRACT FROM AUTHOR]

This study employed an advanced geospatial methodology using the Google Earth Engine (GEE) platform to assess soil erosion in the Satluj Watershed thoroughly. To achieve this, the Revised Universal Soil Loss Equation (RUSLE) model was integrated into the study, which was revealed through several analytical tiers, each with a unique function. The study commenced with estimating the R factor, which was carried out using annual precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS). The erodibility of the soil, which the K factor describes, was then calculated using the USDA soil texture classifications taken from the Open Land Map. The third layer emphasizes the LS factor, which analyzes slope data and how they affect soil erosion rates, using digital elevation models. To understand the impact of vegetation on soil conservation, the fourth layer presents the C factor, which evaluates changes in land cover, and the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 data. The P factor incorporates MODIS data to assess the types of land cover and slope conditions. Combining these layers with the RUSLE model produces a thorough soil loss map, revealing different levels of soil erosion throughout the Satluj Watershed. The preliminary findings indicate that 3.3% of the watershed had slight soil loss, 0.2% had moderate loss, and 1.2% had high soil erosion rates. And 92% had severe rates of soil erosion. After a thorough investigation, the detected regions were divided into risk classifications, providing vital information for the watershed's land management and conservation plans. The mean soil loss throughout the watershed was determined to be 10,740 tons/ha/year. This novel method creates a strong foundation for evaluating soil erosion, while also highlighting the value of the cloud-based geospatial analysis and the RUSLE model in comprehending intricate environmental processes. [ABSTRACT FROM AUTHOR]

A multi-purpose and multi-scale tool for the seismic vulnerability and risk classification of critical buildings, such as schools, is proposed for pre- and post-event decision-making to mitigate the risk and reduce losses. The herein proposed "RiskSchools" system, is capable of performing the seismic risk assessment and grading of school buildings at various scales (district, municipality, region etc.), using (a) a pre-seismic rapid visual screening and grading of the school buildings in different vulnerability-risk classes and (b) a seismic risk assessment of the school buildings population, applying probabilistic or scenario-based methods for the seismic hazard and analytical methods for the vulnerability and risk assessment, also leading to a grading of the buildings' risk. The results of the two approaches are compared and combined through a flexible and adaptable expert elicitation scheme to provide a final classification of the seismic risk of the school buildings in the scale of interest and a prioritization scheme with respect to the need for seismic upgrade and retrofitting. The RiskSchools system consists of a powerful, state-of-the-art, user-friendly, and easy-to-use smartphone application for the compilation of the inventory and the rapid visual screening, and a project-dedicated multi-purpose webGIS platform for the seismic vulnerability and risk classification of school buildings at any scale. Although it is initially developed and applied to the school building stock of the Region of Central Macedonia in Greece, it has been specifically designed to be easily applied to other regions of Greece and worldwide and adapted to other critical buildings, like health care and hospital buildings. The ultimate scope of the RiskSchools System is to allow for the optimal design of decision-making procedures in support of disaster management to enhance critical buildings resilience. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusion This study aimed to evaluate the risk classification system using the detailed positive surgical margin (PSM) status to predict biochemical recurrence (BCR) after robot‐assisted radical prostatectomy (RARP).We retrospectively analyzed 427 patients who underwent RARP between January 2016 and March 2020. We investigated risk factors for BCR using univariate and multivariate Cox proportional hazard regression models. The biochemical recurrence‐free survival (BRFS) rate was assessed using the Kaplan‐Meier method.The median follow‐up period was 43.4 months and 99 patients developed BCR. In the multivariate analysis, maximum PSM length > 5.0 mm and the International Society of Urological Pathology grade group (ISUP GG) at the PSM ≥3 were predictive factors for BCR in patients with a PSM. In the multivariate analysis, these factors were also independent predictive factors in the overall study population, including patients without a PSM. We classified the patients into four groups using these factors and found that the 1‐year BRFS rates in the negative surgical margin (NSM) group, low‐risk group (PSM and neither factor), intermediate‐risk group (either factor), and high‐risk group (both factors) were 94.9%, 94.5%, 83.1%, and 52.9%, respectively. The low‐risk group showed similar BRFS to the NSM group (p = 0.985), while the high‐risk group had significantly worse BRFS than the other groups (p < 0.001).Maximum PSM length > 5.0 mm and ISUP GG at the PSM ≥3 were independent predictive factors for BCR after RARP. Risk classification for BCR using these factors is considered to be useful and might help urologists decide on additional treatment after RARP. [ABSTRACT FROM AUTHOR]

To enhance risk stratification in patients suspected of coronary artery disease, the assessment of coronary artery calcium (CAC) could be incorporated, especially when CAC can be readily assessed on previously performed non-gated chest computed tomography (CT). Guidelines recommend reporting on patients' extent of CAC on these non-cardiac directed exams and various studies have shown the diagnostic and prognostic value. However, this method is still little applied, and no current consensus exists in clinical practice. This review aims to point out the clinical utility of different kinds of CAC assessment on non-gated CTs. It demonstrates that these scans indeed represent a merely untapped and underestimated resource for risk stratification in patients with stable chest pain or an increased risk of cardiovascular events. To our knowledge, this is the first review to describe the clinical utility of different kinds of visual CAC evaluation on non-gated unenhanced chest CT. Various methods of CAC assessment on non-gated CT are discussed and compared in terms of diagnostic and prognostic value. Furthermore, the application of these non-gated CT scans in the general practice of cardiology is discussed. The clinical utility of coronary calcium assessed on non-gated chest CT, according to the current literature, is evident. This resource of information for cardiac risk stratification needs no specific requirements for scan protocol, and is radiation-free and cost-free. However, some gaps in research remain. In conclusion, the integration of CAC on non-gated chest CT in general cardiology should be promoted and research on this method should be encouraged. [ABSTRACT FROM AUTHOR]

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of urbanization in China, the gap between urban and rural areas is decreasing. The traditional approach of constructing fire stations based on urban built-up areas is no longer suitable for the needs of modern fire rescue. Therefore, a comprehensive fire station location model is proposed based on fire risk assessment. This method divides the protected area units based on the urban road network. By evaluating different regions based on spatial position, land attributes, population density, floor area ratio, and fire incident indicators, the fire rescue risk levels and categorize regions into four risk levels are assessed. Corresponding response times were determined, and an objective model was developed to maximize the coverage area for fire response. The Baidu API was utilized to accurately calculate driving distances and times, and Gurobi optimization software was used to solve the model. Taking Shanghai as an example, the fire station location and layout from two perspectives—re-planning based on overall station placement and re-planning based on existing stations—were analyzed. The results suggest that constructing around 150 fire stations in Shanghai would effectively meet the fire rescue needs, which aligns with the actual situation in Shanghai and demonstrates the strong applicability of this model. This approach enables the meeting of new demand for fire station construction due to the significant increase in the coverage area while effectively utilizing firefighting resources. [ABSTRACT FROM AUTHOR]

The development of new technologies and big data analytics tools has had a profound impact on the insurance industry. A new wave of insurance economics research has emerged to study the changes and challenges those big data analytics developments engendered on the insurance industry. We provide a comprehensive literature review on big data, risk classification, and privacy in insurance markets, and discuss avenues for future research. Our study is complemented by an application of the use of big data in risk classification, considering individuals' privacy preferences. We propose a framework for analyzing the trade-off between the accuracy of risk classification and the discount offered to policyholders as an incentive to share private data. Furthermore, we discuss the conditions under which using policyholders' private data to classify risks more accurately is profitable for an insurer. In particular, we find that improving the accuracy of risk classification, if achieved by requiring the use of private data, does not necessarily provide an incentive for insurers to create more granular risk classes. [ABSTRACT FROM AUTHOR]

Space assets are crucial resources for a nation, and effectively managing the various risks associated with them will help maximize their benefits, enhance their contribution to national modernization and security, and improve the strength of the space industry. First, this study clarifies the definition and scope of space assets. In a narrow sense, space assets encompass all spacecraft in orbit. Broadly speaking, space assets also comprise related physical and intangible assets. Second, potential risk sources are identified from multiple dimensions and categorized into technical risks (such as collision disintegration, explosive disintegration, and load failure), military risks (including the use of spacecraft as weapons or weapon platforms), and management risks (referring mainly to mismatches in management systems, leading to loss of intangible assets). Subsequently, this study explores a quantitative assessment method for the technical risks, establishes a model for quantifying space asset values, and briefly analyzes possible risk control methods. Furthermore, a preliminary classification model for risk events is proposed along with several considerations including establishment of systems and mechanisms, formulation of response plans, responsibility allocation, and implementation and supervision of disposal actions.

Abstract No established predictive or risk classification tool exists for the neurological outcomes of post-cardiac arrest syndrome (PCAS) in patients with in-hospital cardiac arrest (IHCA). This study aimed to investigate whether the revised post-cardiac arrest syndrome for therapeutic hypothermia score (rCAST), which was developed to estimate the prognosis of PCAS patients with out-of-hospital cardiac arrest (OHCA), was applicable to patients with IHCA. A retrospective, multicenter observational study of 140 consecutive adult IHCA patients admitted to three intensive care units. The area under the receiver operating characteristic curves (AUCs) of the rCAST for poor neurological outcome and mortality at 30 days were 0.88 (0.82–0.93) and 0.83 (0.76–0.89), respectively. The sensitivity and specificity of the risk classification according to rCAST for poor neurological outcomes were 0.90 (0.83–0.96) and 0.67 (0.55–0.79) for the low, 0.63 (0.54–0.74) and 0.67 (0.55–0.79) for the moderate, and 0.27 (0.17–0.37) and 1.00 (1.00–1.00) for the high-severity grades. All 22 patients classified with a high-severity grade showed poor neurological outcomes. The rCAST showed excellent predictive accuracy for neurological prognosis in patients with PCAS after IHCA. The rCAST may be useful as a risk classification tool for PCAS after IHCA.

Abstract To enhance risk stratification in patients suspected of coronary artery disease, the assessment of coronary artery calcium (CAC) could be incorporated, especially when CAC can be readily assessed on previously performed non-gated chest computed tomography (CT). Guidelines recommend reporting on patients’ extent of CAC on these non-cardiac directed exams and various studies have shown the diagnostic and prognostic value. However, this method is still little applied, and no current consensus exists in clinical practice. This review aims to point out the clinical utility of different kinds of CAC assessment on non-gated CTs. It demonstrates that these scans indeed represent a merely untapped and underestimated resource for risk stratification in patients with stable chest pain or an increased risk of cardiovascular events. To our knowledge, this is the first review to describe the clinical utility of different kinds of visual CAC evaluation on non-gated unenhanced chest CT. Various methods of CAC assessment on non-gated CT are discussed and compared in terms of diagnostic and prognostic value. Furthermore, the application of these non-gated CT scans in the general practice of cardiology is discussed. The clinical utility of coronary calcium assessed on non-gated chest CT, according to the current literature, is evident. This resource of information for cardiac risk stratification needs no specific requirements for scan protocol, and is radiation-free and cost-free. However, some gaps in research remain. In conclusion, the integration of CAC on non-gated chest CT in general cardiology should be promoted and research on this method should be encouraged.

Abstract Background Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its characteristics of being high-dimensional, non-linear, and highly correlated. The study goal was to use ML algorithms to classify fall risks in community-dwelling older adults with the aid of an explainable artificial intelligence (XAI) approach to increase interpretability. Methods A total of 215 participants were included for analysis. The input information included personal metrics and posturographic parameters obtained from a tracker-based posturography of four standing postures. Two classification criteria were used: with a previous history of falls and the timed-up-and-go (TUG) test. We used three meta-heuristic methods for feature selection to handle the large numbers of parameters and improve efficacy, and the SHapley Additive exPlanations (SHAP) method was used to display the weights of the selected features on the model. Results The results showed that posturographic parameters could classify the participants with TUG scores higher or lower than 10 s but were less effective in classifying fall risk according to previous fall history. Feature selections improved the accuracy with the TUG as the classification label, and the Slime Mould Algorithm had the best performance (accuracy: 0.72 to 0.77, area under the curve: 0.80 to 0.90). In contrast, feature selection did not improve the model performance significantly with the previous fall history as a classification label. The SHAP values also helped to display the importance of different features in the model. Conclusion Posturographic parameters in standing can be used to classify fall risks with high accuracy based on the TUG scores in community-dwelling older adults. Using feature selection improves the model’s performance. The results highlight the potential utility of ML algorithms and XAI to provide guidance for developing more robust and accurate fall classification models. Trial registration Not applicable

Abstract Background Due to the abundant usage of chemotherapy in young triple-negative breast cancer (TNBC) patients, the unbiased prognostic value of BRCA1-related biomarkers in this population remains unclear. In addition, whether BRCA1-related biomarkers modify the well-established prognostic value of stromal tumor-infiltrating lymphocytes (sTILs) is unknown. This study aimed to compare the outcomes of young, node-negative, chemotherapy-naïve TNBC patients according to BRCA1 status, taking sTILs into account. Methods We included 485 Dutch women diagnosed with node-negative TNBC under age 40 between 1989 and 2000. During this period, these women were considered low-risk and did not receive chemotherapy. BRCA1 status, including pathogenic germline BRCA1 mutation (gBRCA1m), somatic BRCA1 mutation (sBRCA1m), and tumor BRCA1 promoter methylation (BRCA1-PM), was assessed using DNA from formalin-fixed paraffin-embedded tissue. sTILs were assessed according to the international guideline. Patients’ outcomes were compared using Cox regression and competing risk models. Results Among the 399 patients with BRCA1 status, 26.3% had a gBRCA1m, 5.3% had a sBRCA1m, 36.6% had tumor BRCA1-PM, and 31.8% had BRCA1-non-altered tumors. Compared to BRCA1-non-alteration, gBRCA1m was associated with worse overall survival (OS) from the fourth year after diagnosis (adjusted HR, 2.11; 95% CI, 1.18–3.75), and this association attenuated after adjustment for second primary tumors. Every 10% sTIL increment was associated with 16% higher OS (adjusted HR, 0.84; 95% CI, 0.78–0.90) in gBRCA1m, sBRCA1m, or BRCA1-non-altered patients and 31% higher OS in tumor BRCA1-PM patients. Among the 66 patients with tumor BRCA1-PM and ≥ 50% sTILs, we observed excellent 15-year OS (97.0%; 95% CI, 92.9–100%). Conversely, among the 61 patients with gBRCA1m and

No established predictive or risk classification tool exists for the neurological outcomes of post-cardiac arrest syndrome (PCAS) in patients with in-hospital cardiac arrest (IHCA). This study aimed to investigate whether the revised post-cardiac arrest syndrome for therapeutic hypothermia score (rCAST), which was developed to estimate the prognosis of PCAS patients with out-of-hospital cardiac arrest (OHCA), was applicable to patients with IHCA. A retrospective, multicenter observational study of 140 consecutive adult IHCA patients admitted to three intensive care units. The area under the receiver operating characteristic curves (AUCs) of the rCAST for poor neurological outcome and mortality at 30 days were 0.88 (0.82–0.93) and 0.83 (0.76–0.89), respectively. The sensitivity and specificity of the risk classification according to rCAST for poor neurological outcomes were 0.90 (0.83–0.96) and 0.67 (0.55–0.79) for the low, 0.63 (0.54–0.74) and 0.67 (0.55–0.79) for the moderate, and 0.27 (0.17–0.37) and 1.00 (1.00–1.00) for the high-severity grades. All 22 patients classified with a high-severity grade showed poor neurological outcomes. The rCAST showed excellent predictive accuracy for neurological prognosis in patients with PCAS after IHCA. The rCAST may be useful as a risk classification tool for PCAS after IHCA. [ABSTRACT FROM AUTHOR]

• The characteristics and prognostic value of all ECG parameters were investigated. • Quantitative ECG parameters correlated with disease severity in non-high-risk PE. • A lesser S-V5 (≤ 0.15mv) was predictive for early discharge in non-high-risk PE. Electrocardiogram (ECG) abnormalities indicating right ventricular strain have been reported to have prognostic value in severe cases of acute pulmonary embolism (PE). We aimed to analyze the prognostic significance of other quantitative ECG parameters in non-high-risk acute PE. Consecutive patients with non-high-risk acute PE were prospectively enrolled. The following baseline ECG parameters were collected: rhythm, heart rate, QRS axis, right bundle branch block (RBBB) pattern, S1Q3T3 pattern, T-wave inversion, ST-segment elevation, Qr in lead V1, PR Interval, QRS complex duration, QT interval, P-wave amplitude and duration, R- and S-wave amplitudes. The primary endpoint was early discharge within three days. Associations between ECG parameters and early discharge were analyzed. Overall, 383 patients were enrolled (median age: 67 years, 57% female): 277 (72.3%) with low-risk and 106 (27.7%) with intermediate-risk. The two groups of patients differed in several ECG signs of right ventricular strain and many other quantitative parameters like R- and S-wave amplitudes. In the multivariate logistic regression analysis, the S-wave depth in lead V5 (S-V5) was the only independent prognostic factor for early discharge (odds ratio = 0.137, 95% confidence interval = 0.031-0.613, p = 0.009). The optimum cutoff value of S-V5 for predicting early discharge derived from the receiver operative characteristic curve was 0.15 mv (c-statistic = 0.66, p =0.003). Several ECG signs of right ventricular strain and many other quantitative parameters were associated with disease severity in non-high-risk acute PE. An S-V5 lesser than 0.15 mv was predictive for early discharge in these patients. [ABSTRACT FROM AUTHOR]

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and aggressive myeloid malignancy which characteristically expresses an atypical phenotype including CD123+, CD56+, and CD4+. We are aimed to investigate the clinical and prognostic characteristics of AML patients exhibiting BPDCN-like immunophenotype and provide additional insights for risk stratification of AML. A total of 241 newly diagnosed AML patients were enrolled in this retrospective study and categorized into BPDCN-like positive (n = 125)/negative (n = 116) groups, determined by the present with CD123+ along with either CD56+ or CD4+, or both. Subsequently, an analysis was conducted to examine the general clinical characteristics, genetic profiles, and prognosis of the two respective groups. Patients with BPDCN-like immunophenotype manifested higher frequencies of acute myelomonocytic leukemia and acute monoblastic leukemia. Surprisingly, the presence of the BPDCN-like immunophenotype exhibited an inverse relationship with CEBPA bZIP mutation. Notably, patients with BPDCN-like phenotype had both worse OS and EFS compared to those without BPDCN-like phenotype. In the CN-AML subgroups, the BPDCN-like phenotype was associated with worse EFS. Similarly, a statistically significant disparity was observed in both OS and EFS within the favorable-risk subgroup, while only OS was significant within the adverse-risk subgrouMoreover, patients possessing favorable-risk genetics without BPDCN-like phenotype had the longest survival, whereas those who had both adverse-risk genetics and BPDCN-like phenotype exhibited the worst survival. Our study indicated that BPDCN-like phenotype negatively associated with CEBPA bZIP mutation and revealed a significantly poor prognosis in AML. Moreover, the 2022 ELN classification, in combination with the BPDCN-like phenotype, may better distinguish between different risk groups. [ABSTRACT FROM AUTHOR]

Background: Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its characteristics of being high-dimensional, non-linear, and highly correlated. The study goal was to use ML algorithms to classify fall risks in community-dwelling older adults with the aid of an explainable artificial intelligence (XAI) approach to increase interpretability. Methods: A total of 215 participants were included for analysis. The input information included personal metrics and posturographic parameters obtained from a tracker-based posturography of four standing postures. Two classification criteria were used: with a previous history of falls and the timed-up-and-go (TUG) test. We used three meta-heuristic methods for feature selection to handle the large numbers of parameters and improve efficacy, and the SHapley Additive exPlanations (SHAP) method was used to display the weights of the selected features on the model. Results: The results showed that posturographic parameters could classify the participants with TUG scores higher or lower than 10 s but were less effective in classifying fall risk according to previous fall history. Feature selections improved the accuracy with the TUG as the classification label, and the Slime Mould Algorithm had the best performance (accuracy: 0.72 to 0.77, area under the curve: 0.80 to 0.90). In contrast, feature selection did not improve the model performance significantly with the previous fall history as a classification label. The SHAP values also helped to display the importance of different features in the model. Conclusion: Posturographic parameters in standing can be used to classify fall risks with high accuracy based on the TUG scores in community-dwelling older adults. Using feature selection improves the model's performance. The results highlight the potential utility of ML algorithms and XAI to provide guidance for developing more robust and accurate fall classification models. Trial registration Not applicable [ABSTRACT FROM AUTHOR]

Background: Due to the abundant usage of chemotherapy in young triple-negative breast cancer (TNBC) patients, the unbiased prognostic value of BRCA1-related biomarkers in this population remains unclear. In addition, whether BRCA1-related biomarkers modify the well-established prognostic value of stromal tumor-infiltrating lymphocytes (sTILs) is unknown. This study aimed to compare the outcomes of young, node-negative, chemotherapy-naïve TNBC patients according to BRCA1 status, taking sTILs into account. Methods: We included 485 Dutch women diagnosed with node-negative TNBC under age 40 between 1989 and 2000. During this period, these women were considered low-risk and did not receive chemotherapy. BRCA1 status, including pathogenic germline BRCA1 mutation (gBRCA1m), somatic BRCA1 mutation (sBRCA1m), and tumor BRCA1 promoter methylation (BRCA1-PM), was assessed using DNA from formalin-fixed paraffin-embedded tissue. sTILs were assessed according to the international guideline. Patients' outcomes were compared using Cox regression and competing risk models. Results: Among the 399 patients with BRCA1 status, 26.3% had a gBRCA1m, 5.3% had a sBRCA1m, 36.6% had tumor BRCA1-PM, and 31.8% had BRCA1-non-altered tumors. Compared to BRCA1-non-alteration, gBRCA1m was associated with worse overall survival (OS) from the fourth year after diagnosis (adjusted HR, 2.11; 95% CI, 1.18–3.75), and this association attenuated after adjustment for second primary tumors. Every 10% sTIL increment was associated with 16% higher OS (adjusted HR, 0.84; 95% CI, 0.78–0.90) in gBRCA1m, sBRCA1m, or BRCA1-non-altered patients and 31% higher OS in tumor BRCA1-PM patients. Among the 66 patients with tumor BRCA1-PM and ≥ 50% sTILs, we observed excellent 15-year OS (97.0%; 95% CI, 92.9–100%). Conversely, among the 61 patients with gBRCA1m and < 50% sTILs, we observed poor 15-year OS (50.8%; 95% CI, 39.7–65.0%). Furthermore, gBRCA1m was associated with higher (adjusted subdistribution HR, 4.04; 95% CI, 2.29–7.13) and tumor BRCA1-PM with lower (adjusted subdistribution HR, 0.42; 95% CI, 0.19–0.95) incidence of second primary tumors, compared to BRCA1-non-alteration. Conclusions: Although both gBRCA1m and tumor BRCA1-PM alter BRCA1 gene transcription, they are associated with different outcomes in young, node-negative, chemotherapy-naïve TNBC patients. By combining sTILs and BRCA1 status for risk classification, we were able to identify potential subgroups in this population to intensify and optimize adjuvant treatment. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to use conventional ultrasound features, ultrasound radiomics, and machine learning algorithms to establish a predictive model to assess the risk of post-surgical recurrence of gastrointestinal stromal tumors (GISTs). Methods: This retrospective analysis included 230 patients with pathologically diagnosed GISTs. Radiomic features were extracted from manually annotated images. Radiomic features plus conventional ultrasound features were selected using the SelectKbest analysis of variance and stratified tenfold cross-validation recursive elimination methods. Finally, five different machine learning algorithms (logistic regression [LR], support vector machine [SVM], random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) were established to predict risk stratification of GISTs. The predictive performance of the established model was mainly evaluated based on the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy, whereas the predictive performance of the optimal machine learning algorithm and a radiologist's subjective assessment were compared using McNemar's test. Results: Seven radiomics features and one conventional ultrasound feature were selected to construct the machine learning models for GIST risk classification. The mentioned five machine learning models were able to predict the malignant potential of GISTs. LR and SVM outperformed other classifiers on the test set, with LR achieving an accuracy of 0.852 (AUC, 0.881; sensitivity, 0.871; specificity, 0.826) and SVM achieving an accuracy of 0.852 (AUC, 0.879; sensitivity, 0.839; specificity, 0.870), and proved significantly better than the radiologist (accuracy, 0.691; sensitivity, 0.645; specificity, 0.813). Conclusion: Machine learning-based ultrasound radiomics features are able to noninvasively predict the biological risk of GISTs. [ABSTRACT FROM AUTHOR]

Background: The survival trends and prognostic factors of patients with extraosseous plasmacytoma (EOP) or extramedullary plasmacytoma (EMP) have not been reported in recent years. The objective of this study was to develop a novel nomogram and risk stratification system for predicting the overall survival (OS) of elderly patients with EOP based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods: The demographic characteristics of 900 patients aged 60 years and above, diagnosed with EOP between 2000 and 2019, were extracted from the SEER database. The patient population was randomly divided into a training cohort and an internal validation cohort in a ratio of 7:3. Univariate and multivariate Cox regression analyses were conducted to identify independent predictors of prognosis in elderly EOP patients, followed by developing a nomogram for prognostic assessment. The performance of the model was evaluated through receiver-operating characteristic (ROC) curves, C-index, calibration curves for calibration accuracy assessment, and decision curve analysis (DCA) to assess its clinical utility. All elderly EOP patients were stratified into three risk subgroups by cutoff value utilizing X-tile software based on their total OS scores for comparative analysis purposes. Kaplan–Meier (K–M) survival curve analysis was employed to validate any observed differences in OS among these three risk groups. Results: Six factors including age, year of diagnosis, marital status, primary site, surgery, and prior tumor history were identified to be independently predictive of the OS of elderly patients with EOP, and these predictors were included in the construction of the nomogram. The 1-, 3-, and 5-year area under the curves (AUCs) for OS were 0.717, 0.754, and 0.734 in the training cohort and 0.740, 0.730, and 0.765 in the validation cohort, respectively. The C-index values in the two cohorts were 0.695 and 0.690. The calibration curves and DCA exhibit commendable consistency and validity, respectively, thereby demonstrating their robust performance. The training set was stratified into low-, medium-, and high-risk subgroups based on the optimal cutoff points (167.8 and 264.8) identified. The K–M curve and cumulative risk curve exhibited statistically significant disparities in survival rates among the groups. Conclusions: We developed a nomogram and risk classification system, which can serve as an intuitive and effective tool for clinicians to enhance the prediction of OS in elderly EOP patients, thereby facilitating the formulation of more rational and personalized treatment strategies. [ABSTRACT FROM AUTHOR]

Predictive modeling has been widely used for insurance rate making. In this paper, we focus on insurance claim count data and address their common issues with more flexible modeling techniques. In particular, we study the zero-inflated and hurdle-generalized Poisson and negative binomial distributions in a functional form for modeling insurance claim count data. It is shown that these models are useful in addressing the problem of excess zeros and over-dispersion of the claim count variable. In addition, we show that including the exposure as a covariate in both the zero and the count part of the model is an effective approach to incorporating exposure information in zero-inflated and hurdle models. We illustrate the effectiveness and versatility of the introduced models using three real datasets. The results suggest their promising applications in insurance risk classification and beyond. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

On‐demand insurance is an innovative business model from the InsurTech space, which provides coverage for episodic risks. It makes use of a simple fact in a practical way: People differ in their frequency of exposure as well as the probability of loss. The extra dimension of heterogeneity can be used to screen the insured and shifts the utility‐possibility frontier outward. We provide a sufficient condition under which type‐specific full insurance at the actuarially fair price is incentive compatible. We also show that our results hold for various real‐world implementations of on‐demand insurance. [ABSTRACT FROM AUTHOR]

Background: Toxicity or treatment failure related to drug-drug interactions (DDIs) are known to significantly affect morbidity and hospitalization rates. Despite the availability of numerous databases for DDIs identification and management, their information often differs. Oral anticoagulants are deemed at risk of DDIs and a leading cause of adverse drug events, most of which being preventable. Although many databases include DDIs involving anticoagulants, none are specialized in them.Aim and method: This study aims to compare the DDIs information content of four direct oral anticoagulants and two vitamin K antagonists in three major DDI databases used in Switzerland: Lexi-Interact, Pharmavista, and MediQ. It evaluates the consistency of DDIs information in terms of differences in severity rating systems, mechanism of interaction, extraction and documentation processes and transparency.Results: This study revealed 2’496 DDIs for the six anticoagulants, with discrepant risk classifications. Only 13.2% of DDIs were common to all three databases. Overall concordance in risk classification (high, moderate, and low risk) was slight (Fleiss’ kappa = 0.131), while high-risk DDIs demonstrated a fair agreement (Fleiss’ kappa = 0.398). The nature and the mechanism of the DDIs were more consistent across databases. Qualitative assessments highlighted differences in the documentation process and transparency, and similarities for availability of risk classification and references.Discussion: This study highlights the discrepancies between three commonly used DDI databases and the inconsistency in how terminology is standardised and incorporated when classifying these DDIs. It also highlights the need for the creation of specialised tools for anticoagulant-related interactions.