Design Structure Matrix Research Articles

Sexual Dimorphism of Radiomic Features in the Brain: An Exploratory Study Using 700 μm MP2RAGE MRI at 7 T.

The aim of this study was to determine whether MRI radiomic features of key cerebral structures differ between women and men, and whether detection of such differences depends on the image resolution. Ultrahigh resolution (UHR) 3D MP2RAGE (magnetization-prepared 2 rapid acquisition gradient echo) T1-weighted MR images (voxel size, 0.7 × 0.7 × 0.7 mm 3 ) of the brain of 30 subjects (18 women and 12 men; mean age, 39.0 ± 14.8 years) without abnormal findings on MRI were retrospectively included. MRI was performed on a whole-body 7 T MR system. A convolutional neural network was used to segment the following structures: frontal cortex, frontal white matter, thalamus, putamen, globus pallidus, caudate nucleus, and corpus callosum. Eighty-seven radiomic features were extracted respectively: gray-level histogram (n = 18), co-occurrence matrix (n = 24), run-length matrix (n = 16), size-zone matrix (n = 16), and dependence matrix (n = 13). Feature extraction was performed at UHR and, additionally, also after resampling to 1.4 × 1.4 × 1.4 mm 3 voxel size (standard clinical resolution). Principal components (PCs) of radiomic features were calculated, and independent samples t tests with Cohen d as effect size measure were used to assess differences in PCs between women and men for the different cerebral structures. At UHR, at least a single PC differed significantly between women and men in 6/7 cerebral structures: frontal cortex ( d = -0.79, P = 0.042 and d = -1.01, P = 0.010), frontal white matter ( d = -0.81, P = 0.039), thalamus ( d = 1.43, P < 0.001), globus pallidus ( d = 0.92, P = 0.020), caudate nucleus ( d = -0.83, P = 0.039), and corpus callosum ( d = -0.97, P = 0.039). At standard clinical resolution, only a single PC extracted from the corpus callosum differed between sexes ( d = 1.05, P = 0.009). Nonnegligible differences in radiomic features of several key structures of the brain exist between women and men, and need to be accounted for. Very high spatial resolution may be required to uncover and further investigate the sexual dimorphism of brain structures on MRI.

How safety value is exchanged in Chinese modern megaprojects: A stakeholder value network perspective

To visualize and quantify these exchanges among primary stakeholders in megaprojects, this study introduces network stakeholder value network as the core methodology. Starting with a qualitative organizational safety behavior model encompassing 8 stakeholders and 61 value flows, it's built based on 3 organizational safety behavior categories. Moreover, questionnaire data from 356 experts quantifies the model, and the design structure matrix identifies 13845 safety value paths, including 4827 cycles. Analysis delves into critical cycles, stakeholders, flows, and organizational safety behavior types to refine safety strategies. The findings, based on Chinese megaprojects, suggest that contractors and governments must lead safety awareness efforts, with safety compliance paramount for all parties. The study's contributions are threefold: firstly, it broadens the scope to encompass organizational safety behavior; secondly, it introduces a novel approach to visualize and quantify safety value flows; and thirdly, it considers multiple types of value exchanges, underscores their significance in promoting safety value, and offers practical recommendations to strengthen construction safety performance.

Radiomic survival prediction with liver metastases in colorectal cancer.

e15584 Background: The survival outcome of colorectal cancer depends upon staging and the presence of metastases. Nonetheless, there isn’t a reliable prediction model for overall survival (OS) specifically tailored to patients with colorectal cancer who have liver metastases. Consequently, this study aims to find radiomic features that can be utilized to predict OS of colorectal cancer with liver metastases. Methods: We utilized the Colorectal Liver Metastases data set in The Cancer Imaging Archive (TCIA). Overall, 197 patients with colorectal cancer metastatic to the liver were identified. Semi-automatic segmentation of liver metastases was used to extract radiomic features including Gray-Level Co-occurrence (glcm), Gray-Level Run Length Matrix (glrlm), Gray-Level Size Zone Matrix (glszm), Gray-Level Dependence Matrix (gldm), and Neighborhood Gray-Tone Difference Matrix (ngtdm). 3D Slicer was used to extract radiomic features. We tested a Cox proportional hazards model in each radiomics feature to predict OS. Results: Total 107 radiomic features were extracted from pre-segmented liver metastases on patients’ Computed Tomography images. Among them, original shape sphericity, original glcm correlation, original gldm DependenceEntropy, and original glrlm RunEntropy were statistically significant for the prediction of OS, with a hazard ratio of -2.16 (CI 0.01-0.92; p value 0.04), 1.54 (CI 1.5-14.0; p value 0.006), 0.51 (CI 1.0-2.5; p value 0.02), and 0.52 (CI 1.0-2.6; p value 0.03), respectively. Conclusions: Our data analysis indicates crucial radiomic features that hold promise for predicting the clinical outcome of patients with colorectal cancer who have liver metastases. Future research with a larger sample size is warranted for the external validation of our results.

#1943 The association of uncarboxylated, non-phosphorylated Matrix Gla protein with arterial stiffness in hemodialysis and peritoneal dialysis patients

Abstract Background and Aims Vitamin K deficiency has been identified as a marker of vascular calcification AND STIFNESS and an independent cardiovascular risk factor in hemodialysis (HD) and peritoneal dialysis (PD) patients, as the most powerful inhibitor of vascular calcification, Matrix Gla Protein requires vitamin K to become activated. However, until to date, it has not yet been clarified whether hepatic or extrahepatic markers of vitamin K status reflect the arterial status of dialysis patients. In this study we aimed to investigate the association of vitamin K deficiency with arterial stiffness in End-Stage Kidney Disease (ESKD) patients. Method In 42 ESKD patients undergoing either HD or PD, we measured plasma levels of the inactive vitamin-K dependent Matrix Gla Protein (dp-ucMGP) and serum levels of prothrombin (PIVKA-II) as markers of extrahepatic and hepatic insufficiency of vitamin K (CARIM Institute, Maastricht, Netherlands). In these patients we determined arterial stiffness parameters (pulse wave velocity-PWV) by the Mobil-O-Graph device (IEM, Stolberg, Germany). Results Serum PIVKA-II was associated with T2D duration (r = −0.38, p = 0.013), phosphorus levels (r = −0.41, p = 0.009) serum albumin (r = −0.33, p = 0.036) Spearman's rho test, and the type of transporter in PD patients (p = 0.039), Kruskal-Wallis test. Plasma dp-ucMGP was associated with PWV (r = 0.51, p = 0.001), peripheral (r = −0.37, p = 0.016) and central diastolic blood pressure (BP) (r = −0.34, p = 0.026) and age (r = 0.41, p = 0.007), Spearman's rho. Patients with T2DM, history of acute myocardial infarction and those receiving warfarin had significantly increased levels of circulating dp-ucMGP (p = 0.036, p = 0.039 and p &amp;lt; 0.0001, respectively, Kruskal-Wallis). Warfarin was associated with a 2.5-fold and a 23-fold increase in dp-ucMGP and PIVKA-II levels, respectively. Stepwise multiple regression analysis showed that PWV (p = 0.001, B = 494, 95% CI: 242-746) was an independent predictor of dp-ucMGP, independent of age, T2T2, history of myocardial infraction and diastolic BP. Conclusion Elevated plasma dp-ucMGP levels (suggesting extrahepatic vitamin K deficiency) were associated with increased arterial stiffness in dialysis patients.

Clustering and Optimizing Methods for Sprints of Agile Development Projects

To address the challenges of sprint planning and sequential optimization in agile development projects, this paper presents a model for agile sprint clustering and optimization. Firstly, we build an activity similarity network and measure the tie strength between activities using social network analysis (SNA) and design structure matrix (DSM). Then, we build an “overlap-iteration” model to measure the interactions strength between activities. Based on thecoupling strength (i.e. the sum of tie strength and interaction strength) between activities, this paper uses a two-stage clustering method to cluster the process DSM to obtain agile sprints. Furthermore, we measure the rework probability and rework intensity between sprints from the rework probability and rework intensity between activities to build an agile sprint optimization method. Finally, an agile development project example is provided to illustrate the validity of the proposed models. The results demonstrate that our approach significantly reduces project duration, cost, and rework risk in agile development projects.

The role of constitutive properties on the longitudinal compressive strength of composites

The longitudinal compressive strength of fibre-reinforced composites is often a limiting factor for structural design. This paper uses micromechanical finite-element analyses to examine how the constitutive properties of the matrix, fibres, and interface affect the composite’s strength. For a typical carbon/epoxy composite, the matrix shear strength has a larger effect than the modulus; a linear-elastic perfectly-plastic approximation of the matrix shear curve may overestimate the composite’s strength by over 15%. A pressure-dependent and dilatant matrix plasticity response may strengthen the composite by 30%, considering current uncertainties in friction and dilation angles of epoxy matrices. The finite shear modulus of carbon fibres may weaken the composite by more than 10%. Considering a cohesive interface using traction–separation laws significantly affects the predicted response of the composite, even for interfaces stronger than the matrix. Opportunities for improving the characterisation, modelling and performance of the constituents in terms of matrix plasticity, fibre shear modulus, and interface response are discussed.

Optimization of Aircraft Fuel Systems Considering Fluid Dynamics Principles

The fuel system is the lifeline of any aircraft, pivotal for its operation and future advancements. This study delves into aircraft fuel system design methodologies, aiming to streamline development processes. Through optimization and matrix methods like the morphological matrix, Saab Aerospace pioneer’s conceptual designs. These methods automate development and deepen our understanding of how top-level requirements impact engineering details. Quantifying matrices opens doors to design optimization and probabilistic design. Furthermore, a systematic approach to simulation modeling minimizes development time, leveraging parallel development and collaborative engineering. With stakeholder expectations in mind, experience gleaned from projects like the Gripen fuel system shapes an overarching process for future endeavors. This journey promises to reshape aircraft fuel system design, ensuring each drop of fuel propels us towards boundless horizons. Key Words: Aerospace engineering, Fuel system, Optimization techniques, Matrix methods, Simulation models, Conceptual design, System integration, Aircraft performance, Fuel tank configurations, Operational flexibility, Gripen Fuel, DSM- Design Structure Matrix

Identification of Relevant Parameters for Traceability in the Continuous Mixing Process in Battery Cell Production

The growing demand for lithium‐ion batteries in electric vehicles pose challenges, particularly for the use of critical materials such as cobalt in electrode slurry mixing. Due to regulatory requirements such as the battery passport, an accurate traceability system is mandatory. However, there is no solution for the continuous mixing process, which is a major challenge in electrode production. This article describes an approach to solve this problem. Based on the results of a literature review, a design structure matrix is constructed to analyze parameter influences. The overall result is a list of relevant parameters that should be recorded in the continuous mixing process in order to characterize the mixing process.

Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability.

To assess how radiomic features may be combined with plaque morphological and compositional features identified by multi-contrast MRI to improve upon conventional risk assessment models in determining culprit carotid artery lesions. Fifty-five patients (mean age: 62.6; 35 males) with bilateral carotid stenosis who experienced transient ischaemic attack (TIA) or stroke were included from the CARE-II multi-centre carotid imaging trial (ClinicalTrials.gov Identifier: NCT02017756). They underwent MRI within 2 weeks of the event. Classification capability in distinguishing culprit lesions was assessed by machine learning. Repeatability and reproducibility of the results were investigated by assessing the robustness of the radiomic features. Radiomics combined with a relatively conventional plaque morphological and compositional metric-based model provided incremental value over a conventional model alone (area under curve [AUC], 0.819 ± 0.002 vs 0.689 ± 0.019, respectively, P = .014). The radiomic model alone also provided value over the conventional model (AUC, 0.805 ± 0.003 vs 0.689 ± 0.019, respectively, P = .031). T2-weighted imaging-based radiomic features had consistently higher robustness and classification capabilities compared with T1-weighted images. Higher-dimensional radiomic features outperformed first-order features. Grey Level Co-occurrence Matrix, Grey Level Dependence Matrix, and Grey Level Size Zone Matrix sub-types were particularly useful in identifying textures which could detect vulnerable lesions. The combination of MRI-based radiomic features and lesion morphological and compositional parameters provided added value to the reference-standard risk assessment for carotid atherosclerosis. This may improve future risk stratification for individuals at risk of major adverse ischaemic cerebrovascular events. The clinical relevance of this work is that it addresses the need for a more comprehensive method of risk assessment for patients at risk of ischaemic stroke, beyond conventional stenosis measurement. This paper shows that in the case of carotid stroke, high-dimensional radiomics features can improve classification capabilities compared with stenosis measurement alone.

Texture identification in liquid crystal-protein droplets using evaporative drying, generalized additive modeling, and K-means Clustering

Sessile drying droplets manifest distinct morphological patterns, encompassing diverse systems, viz., DNA, proteins, blood, and protein-liquid crystal (LC) complexes. This study employs an integrated methodology that combines drying droplet, image texture analysis (features from First Order Statistics, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, and Gray Level Dependence Matrix), and statistical data analysis (Generalized Additive Modeling and K-means clustering). It provides a comprehensive qualitative and quantitative exploration by examining LC-protein droplets at varying initial phosphate buffered concentrations (0x, 0.25x, 0.5x, 0.75x, and 1x) during the drying process under optical microscopy with crossed polarizing configuration. Notably, it unveils distinct LC-protein textures across three drying stages: initial, middle, and final. The Generalized Additive Modeling (GAM) reveals that all the features significantly contribute to differentiating LC-protein droplets. Integrating the K-means clustering method with GAM analysis elucidates how textures evolve through the three drying stages compared to the entire drying process. Notably, the final drying stage stands out with well-defined, non-overlapping clusters, supporting the visual observations of unique LC textures. Furthermore, this paper contributes valuable insights, showcasing the efficacy of drying droplets as a rapid and straightforward tool for characterizing and classifying dynamic LC textures.Graphical

Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans.

This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients. Computed tomography scans were collected from 78 patients with OSCC who underwent surgical treatment at a single medical center. We extracted 1,092 radiomic features from gross tumor volume in each patient's pre-treatment CT. Clinical characteristics were also obtained, including race, sex, age, tobacco and alcohol use, tumor staging, and treatment modality. A feature selection algorithm was used to eliminate the most redundant features, followed by a selection of the best subset of the Logistic regression model (LRM). The best LRM model was determined based on the best prediction accuracy in terms of the area under Receiver operating characteristic curve. Finally, significant radiomic features in the final LRM model were identified as imaging biomarkers. Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ=3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE ≤ 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE ≤ 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.

Robustness of radiomic features in healthy abdominal parenchyma of patients with repeated examinations on dual-layer dual-energy CT

ObjectivesRobustness of radiomic features in physiological tissue is an important prerequisite for quantitative analysis of tumor biology and response assessment. In contrast to previous studies which focused on different tumors with mostly short scan-re-scan intervals, this study aimed to evaluate the robustness of radiomic features in cancer-free patients and over a clinically encountered inter-scan interval. Materials and methodsPatients without visible tumor burden who underwent at least two portal-venous phase dual energy CT examinations of the abdomen between May 2016 and January 2020 were included, while macroscopic tumor burden was excluded based upon follow-up imaging for all patients (≥3 months). Further, patients were excluded if no follow-up imaging was available, or if the CT protocol showed deviations between repeated examinations. Circular regions of interest were placed and proofread by two board-certified radiologists (4 years and 5 years experience) within the liver (segments 3 and 6), the psoas muscle (left and right), the pancreatic head, and the spleen to obtain radiomic features from normal-appearing organ parenchyma using PyRadiomics. Radiomic feature robustness was tested using the concordance correlation coefficient with a threshold of 0.75 considered indicative for deeming a feature robust. ResultsIn total, 160 patients with 480 repeated abdominal CT examinations (range: 2–4 per patient) were retrospectively included in this single-center, IRB-approved study. Considering all organs and feature categories, only 4.58 % (25/546) of all features were robust with the highest rate being found in the first order feature category (20.37 %, 22/108). Other feature categories (grey level co-occurrence matrix, grey level dependence matrix, grey level run length matrix, grey level size zone matrix, and neighborhood gray-tone difference matrix) yielded an overall low percentage of robust features (range: 0.00 %-1.19 %). A subgroup analysis revealed the reconstructed field of view and the X-ray tube current as determinants of feature robustness (significant differences in subgroups for all organs, p < 0.001) as well as the size of the region of interest (no significant difference for the pancreatic head with p = 0.135, significant difference with p < 0.001 for all other organs). ConclusionRadiomic feature robustness obtained from cancer-free subjects with repeated examinations using a consistent protocol and CT scanner was limited, with first order features yielding the highest proportion of robust features.

A Nonlinear Beam Finite Element with Bending–Torsion Coupling Formulation for Dynamic Analysis with Geometric Nonlinearities

Vibration analysis of wing-box structures is a crucial aspect of the aeronautic design to avoid aeroelastic effects during normal flight operations. The deformation of a wing structure can induce nonlinear couplings, causing a different dynamic behavior from the linear counterpart, and nonlinear effects should be considered for more realistic simulations. Moreover, composite materials and aeroelastic tailoring require new simulation tools to include bending–torsion coupling effects. In this research, a beam finite element with bending–torsion coupling formulation is used to investigate the effects of the deflection of beam structures with different aspect ratios. The nonlinear effects are included in the finite element formulation. The geometrical effect is considered, applying a deformation dependent transformation matrix. Stiffness effects are introduced in the stiffness matrix with Hamilton’s Principle and a perturbation approach. The results obtained with the beam finite element model are compared with numerical and experimental evidence.

Multi-objective optimization of clustered wind farms based on potential game approach

With the rapid development of offshore wind power, the construction and operation of multiple adjacent wind farms (WFs) in the form of cluster has become a trend. However, the method for single WF planning can not address the WF cluster layout optimization in the complex relationship of competition or cooperation. This paper proposes a novel method to design clustered offshore WFs. The objectives to be optimized include the maximization of WF generated power and the minimization of wind turbine (WT) turbulence intensity. To solve this problem, a mathematical model based on the potential game theory is established. In this planning model, each WF in the cluster is assumed to be an individual stakeholder, the decision variables and the constraints are mapped to be the strategies and the strategy sets of the stakeholders, respectively, and the objectives are set as their payoffs. The non-dominated sorting genetic algorithm II (NSGA-II) is synthesized with the dependency structure matrix genetic algorithm II (DSMGA-II) to tackle the multi-objective optimization problem (MOOP) and the fuzzy-membership function (FMF) method is used to select the final solution from the Pareto optimal set. The best-strategy-response iterative algorithm is put forward to search for the Nash equilibrium (NE) of the game. Case studies on three clustered offshore WFs in Qidong, China, are carried out to validate the effectiveness of the proposed approach. Also, the simulation results indicate that the synthesized algorithm outperforms the NSGA-II in solving this optimization problem.

Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study.

Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC). This is a retrospective study. The IDC of 30 and ILC of 28 patients from Dukes breast cancer MRI data set of The Cancer Imaging Archive (TCIA), were included. The RF categories such as shape based, Gray level dependence matrix (GLDM), Gray level co-occurrence matrix (GLCM), First order, Gray level run length matrix (GLRLM), Gray level size zone matrix (GLSZM), NGTDM (Neighbouring gray tone difference matrix) were extracted from the DCE-MRI sequence using a 3D slicer. The maximum relevance and minimum redundancy (mRMR) was applied using Google Colab for identifying the top fifteen relevant radiomic features. The Mann-Whitney U test was performed to identify significant RF for differentiating IDC and ILC. Receiver Operating Characteristic (ROC) curve analysis was performed to ascertain the accuracy of RF in distinguishing between IDC and ILC. Ten DCE MRI-based RFs used in our study showed a significant difference (p <0.001) between IDC and ILC. We noticed that DCE RF, such as Gray level run length matrix (GLRLM) gray level variance (sensitivity (SN) 97.21%, specificity (SP) 96.2%, area under curve (AUC) 0.998), Gray level co-occurrence matrix (GLCM) difference average (SN 95.72%, SP 96.34%, AUC 0.983), GLCM interquartile range (SN 95.24%, SP 97.31%, AUC 0.968), had the strongest ability to differentiate IDC and ILC. MRI-based RF derived from DCE sequences can be used in clinical settings to differentiate malignant lesions of the breast, such as IDC and ILC, without requiring intrusive procedures.

How Much Do Urban Terminal Delivery Paths Depend on Urban Roads – A Research Based on Bipartite Graph Network

The structural deficiencies of the terminal delivery path often make it the main culprit of urban traffic congestion and environmental pollution. Traditional studies of express networks regarded them as an independent entity, ignoring the endogenous role of urban road network morphology and structure. To solve this problem, this paper explored the spatial dependency of terminal delivery routes in Xi'an City based on the idea of a bipartite graph network. A spatial dependency matrix of delivery paths–urban roads was constructed by abstracting delivery paths as node-set A and urban roads as node-set B. In addition, three spatial dependencies indexes, including degree centrality, betweenness centrality and closeness centrality were introduced to analyse the coupling features of these two objects. The results show that these dependency measures can reflect the coupling features of urban terminal delivery paths and urban roads. Firstly, degree centrality demonstrates terminal delivery path coverage and coupling hierarchy and scale-free nature. Secondly, betweenness centrality presents the road utilisation balance of terminal delivery paths. Thirdly, closeness centrality explains how easy it is for delivery paths to connect with others.

An effective design freeze strategy for establishing design rules in modular product development

In rapidly changing market conditions, the timing of a product release is crucial for its success. Design iteration, especially between modules, poses a significant challenge in the development of modular products. This study proposes an effective design freeze strategy as design rules aimed at reducing such iterations by finalising specific tasks before others. However, applying a design freeze without considering dependencies among tasks may lead to the unfreezing of previously frozen tasks in the subsequent design and result in the restart of the design process. In response to this issue, this paper presents a methodology that identifies a set of dependent tasks to be frozen simultaneously, constituting freeze alternatives. Furthermore, the methodology evaluates the reduction of rework due to design freeze for each alternative and determines the effective design freeze strategy that can most efficiently eliminate design iterations. The proposed approach employs a network representation of the Design Structure Matrix (DSM) and network analysis techniques to determine the optimal alternative. A case study on a formula car is used to demonstrate the effectiveness of the suggested strategy.

Systematic design of a wind turbine by Design Structure Matrix (DSM) method, case study: Lootak region

The systematic design of a wind turbine is investigated using the Design Structure Matrix (DSM) method. As a complex system, a wind turbine has many interconnections between its components, which could be analyzed by the DSM method. First, the system was decomposed into subsystems; then, the design matrix was prepared and optimised according to the dependencies between the design parameters. Finally, the primary matrix was optimised, and a design matrix was obtained as a wind turbine systematic design guide. By the presented tool, the design parameters of a 2000kW wind turbine for the Lootak region of Zabol, Sistan and Baluchestan province were obtained. To achieve the rated power of 2000kW, the optimum wind turbine parameters were found to be as the rotor diameter of 77, 60 m hub height, rated wind speed of 11.85 m/s, and speed increase transmission ratio of 1: 11.

Quantitative peritumoral magnetic resonance imaging fingerprinting improves machine learning-based prediction of overall survival in colorectal cancer.

To investigate magnetic resonance imaging (MRI)-based peritumoral texture features as prognostic indicators of survival in patients with colorectal liver metastasis (CRLM). From 2007-2015, forty-eight patients who underwent MRI within 3 months prior to initiating treatment for CRLM were identified. Clinicobiological prognostic variables were obtained from electronic medical records. Ninety-four metastatic hepatic lesions were identified on T1-weighted post-contrast images and volumetrically segmented. A total of 112 radiomic features (shape, first-order, texture) were derived from a 10 mm region surrounding each segmented tumor. A random forest model was applied, and performance was tested by receiver operating characteristic (ROC). Kaplan-Meier analysis was utilized to generate the survival curves. Forty-eight patients (male:female = 23:25, age 55.3 years ± 18 years) were included in the study. The median lesion size was 25.73 mm (range 8.5-103.8 mm). Microsatellite instability was low in 40.4% (38/94) of tumors, with Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation detected in 68 out of 94 (72%) tumors. The mean survival was 35 months ± 21 months, and local disease progression was observed in 35.5% of patients. Univariate regression analysis identified 42 texture features [8 first order, 5 gray level dependence matrix (GLDM), 5 gray level run time length matrix (GLRLM), 5 gray level size zone matrix (GLSZM), 2 neighboring gray tone difference matrix (NGTDM), and 17 gray level co-occurrence matrix (GLCM)] independently associated with metastatic disease progression (P < 0.03). The random forest model achieved an area under the curve (AUC) of 0.88. MRI-based peritumoral heterogeneity features may serve as predictive biomarkers for metastatic disease progression and patient survival in CRLM.

Spatial patterns and indices for heat waves and droughts over Europe using a decomposition of extremal dependency

Abstract. We present a method for the analysis and compact description of large-scale multivariate weather extremes. Spatial patterns of extreme events are identified using the tail pairwise dependence matrix (TPDM) proposed by Cooley and Thibaud (2019). We also introduce the cross-TPDM to identify patterns of common extremes in two variables. An extremal pattern index (EPI) is developed to provide a pattern-based aggregation of temperature. A heat wave definition based on EPI is able to detect the most important heat waves over Europe. As an extension for considering simultaneous extremes in two variables, we propose the threshold-based EPI (TEPI) that captures the compound character of spatial extremes. We investigate daily temperature maxima and precipitation deficits at different accumulation times and find evidence that preceding precipitation deficits have a significant influence on the development of heat waves and that heat waves often co-occur with short-term drought conditions. We exemplarily show for the European heat waves of 2003 and 2010 that TEPI is suitable for describing the large-scale compound character of heat waves.