Abstract Migraine and tension-type headache (TTH) are highly prevalent primary headache disorders. Neck pain is common among patients with migraine or TTH, but objective biomarkers for the neck’s myofascial involvement within the trigemino-cervical complex (TCC) are lacking. This study aimed (1) to apply texture analysis (TA) to magnetic resonance imaging (MRI) covering the upper trapezius muscles with T2 maps, which are particularly sensitive to subtle edematous changes such as those following inflammation; and (2) to associate demographics, characteristics of sleep and sports, headache, and neck pain with muscle T2 values and derived texture features. Seventy-one participants (mean age 24.0 ± 3.0 years, 59 females; 22 healthy controls [HC], 21 migraine, 16 TTH, 12 migraine with TTH) underwent MRI of the upper trapezius muscles, followed by bilateral muscle segmentations and extraction of muscle T2 and first- and second-order texture features. Comparisons between HC and participants with primary headache disorders were performed following removal of outliers with the robust regression followed by outlier identification (ROUT) method (Mann-Whitney U tests, adjustment for multiple comparisons). Principal component analysis (PCA), linear, and logistic regression analyses (using bootstrapping with 5,000 permutations) were conducted based on image-derived parameters and headache and neck pain characteristics. Significant group differences were found between HC and participants with primary headache disorders for muscle T2 (30.14 ± 1.05 vs. 31.10 ± 0.92 ms, p < 0.001), sumaverage (0.007163 ± 0.0011 vs. 0.006366 ± 0.0012, p = 0.017), kurtosisglobal (1.15 ± 0.32 vs. 1.63 ± 0.81, p = 0.024), and energy (0.003514 ± 0.0006 vs. 0.004176 ± 0.0012, p = 0.043). According to PCA, a cluster including age, days with headache over the last three months, neck pain, muscle T2, kurtosisglobal, and skewnessglobal was identified, with a cluster close to the opposite direction including average hours of sports per week, average hours of sleep per week, sex, and varianceglobal. Muscle T2 ( p = 0.015) and sumaverage ( p = 0.022) were significantly associated with days with headache over the last three months. Furthermore, sumaverage was significantly associated with the presence of neck pain ( p = 0.024). T2 mapping with TA may provide objective and quantitative parameters for myofascial involvement and peripheral sensitization within the TCC, partially associated with clinical parameters such as the number of days with headache.

Host-parasite interactions are dynamic systems, where parasites usually outnumber hosts by one or more orders of magnitude. However, our understanding is often limited by the assessment of parts of the host, at arbitrary time points, and/or aggregate parasite responses. Here we combined custom-built 3D-printed hardware and deep-learning-based algorithms to enable holistic (i.e. all infecting individuals on the whole plant), spatio-temporal, and parasite-centric analyses of plant-parasitism by nematodes from timelapse videos of infection over months. In so doing, we tracked the dynamic growth and development of all individual parasites, at the organismal level, for thousands of hosts across hundreds of genotypes of Arabidopsis thaliana. Categorising traits into the static (i.e. in an acquired image at a given time point) and dynamic (i.e. phenotypic changes over time), we revealed a greater extent of host-genetic control of parasite traits, and new physiological limits of the species under these conditions. Using this capability, we identify Quantitative Trait Loci (QTL) in the host plant associated with 18 phenotypic traits in the parasite as a resource for the community. Finally, we leverage the large and diverse dataset to understand fundamental features of the parasite, independent of host genotype, revealing aspects of the life cycle which are pseudo-deterministic as well as local, deleterious interactions between co-infecting parasites. Given that plant-parasitic nematodes cause an estimated $100 billion in agricultural damages per year, these insights are contextualised in a global challenge driven by plant-parasitic nematodes.

The majority of commercially cultivated soybean cyst nematode (SCN)-resistant soybean cultivars rely on the Rhg1 locus to defend against SCN, the most economically destructive soybean root pathogen. The decline of Rhg1's effectiveness due to its continuous use creates an urgent need for alternative genetic sources to sustain SCN resistance. Although pathogen effectors are known to perturb host circadian clocks in other plant-pathogen systems, whether and how plant-parasitic nematodes interfere with the host clock to promote virulence remains unexplored. Here, we report that the soybean circadian clock gates rhythmic defense against SCN, and its core component, GmCCA1, plays a dual role in both circadian regulation and pathogen defense. Surprisingly, overexpression of GmCCA1 disrupts circadian rhythms but confers sustained resistance against SCN by activating defense genes both within and beyond the Rhg1 locus, including various pathogenesis-related and resistance genes. On the pathogen side, we identify Hg4E02-an evolutionarily conserved effector in plant-parasitic nematodes-as a transcriptional regulator that directly binds to the promoters of multiple clock genes via novel cis-elements, suppressing their expression. Hg4E02 also represses defense gene expression to enhance virulence. Notably, we uncover a previously unknown layer of antagonism: GmCCA1 and Hg4E02 exert opposing effects on both defense genes and translation-related genes. GmCCA1 inhibits translation and root growth, whereas Hg4E02 promotes translation, likely facilitating nutrient acquisition for the nematode. Collectively, our study reveals a novel mechanism by which a nematode effector directly targets the host circadian clock and translation machinery to promote parasitism, and proposes GmCCA1 as a promising engineering target for enhancing SCN resistance in soybean.

ObjectiveTo assess the discriminatory ability of vertebra-specific volumetric bone mineral density (vBMD), finite element analysis-derived fracture load (FEA-derived FL), and texture analysis (TA) features for incidental vertebral fractures, and to compare performance between thoracic and lumbar levels.Materials and methodsWe retrospectively reviewed baseline and follow-up thoracolumbar CT scans from 420 patients and identified 11 patients with incidental vertebral fractures contributing to 20 fractured vertebrae (7 females; mean age 65.5years). For each fractured vertebra, three level-matched control vertebrae from patients without fractures were selected, yielding 58 controls across 29 control patients (total 78 vertebrae). Parameters evaluated include vBMD, FEA-derived FL, and TA features (24 total). Discriminatory ability was assessed using area under the curve (AUC) values.ResultsvBMD, FEA-derived FL, and 4 of 24 TA features showed group-wise differences between fractured and control vertebrae groups. AUCs were 0.76 [95% CI 0.55-0.90] (vBMD) and 0.73 [95% CI 0.52-0.90] (FEA-derived FL); selected texture features ranged 0.70-0.72. Region-stratified AUC point estimates were higher in the lumbar than in the thoracic vertebrae, but the 95% CIs were wide/overlapping; comparisons are descriptive.ConclusionvBMD had the numerically largest AUC point estimate for discriminating fractured from control vertebrae; FEA-derived FL was similar, and selected texture features showed modest discrimination with comparable point estimates across lumbar and thoracic levels, generating the hypothesis of less region dependence. Regional comparisons are descriptive. Findings are exploratory and intended to prioritize candidate measures for validation and future multivariable modeling before any clinical application.

To evaluate the accuracy of opportunistic measurements of volumetric bone mineral density (vBMD) in intraoperative multi-detector CT (MDCT) scans, using preoperative MDCT as the reference. This retrospective, single-center study included 105 patients (mean age: 73 ± 12.6 years, 53 women) who underwent spine surgery for various indications. All patients had preoperative MDCT with/without intravenous contrast and unenhanced intraoperative scans. VBMD of thoracolumbar vertebrae was automatically extracted using a convolutional neural network (CNN)-based framework with asynchronous calibration and contrast-phase correction. Vertebrae affected by artifacts, fractures, or severe degenerations were excluded. Root-mean-square errors (RMSEs) for associations between pair-wise vertebrae from preoperative and intraoperative vBMD values were calculated in linear regression models. Mean bias and 95

To determine how automation bias (inclination of humans to overly trust-automated decision-making systems) can affect radiologists when interpreting AI-detected cerebral aneurysm findings in time-of-flight magnetic resonance angiography (TOF-MRA) studies. Nine radiologists with varying levels of experience evaluated twenty TOF-MRA examinations for the presence of cerebral aneurysms. Every case was evaluated with and without assistance by the AI software © mdbrain, with a washout period of at least four weeks in-between. Half of the cases included at least one false-positive AI finding. Aneurysm ratings, follow-up recommendations, and reading times were assessed using the Wilcoxon signed-rank test. False-positive AI results led to significantly higher suspicion of aneurysm findings (p = 0.01). Inexperienced readers further recommended significantly more intense follow-up examinations when presented with false-positive AI findings (p = 0.005). Reading times were significantly shorter with AI assistance in inexperienced (164.1 vs 228.2 s; p < 0.001), moderately experienced (126.2 vs 156.5 s; p < 0.009), and very experienced (117.9 vs 153.5 s; p < 0.001) readers alike. Our results demonstrate the susceptibility of radiology readers to automation bias in detecting cerebral aneurysms in TOF-MRA studies when encountering false-positive AI findings. While AI systems for cerebral aneurysm detection can provide benefits, challenges in human–AI interaction need to be mitigated to ensure safe and effective adoption.

To investigate lumbar vertebral volumetric bone mineral density (vBMD) from ex vivo opportunistic multi-detector computed tomography (MDCT) scans using different protocols, and compare it to dedicated quantitative CT (QCT) values from the same specimens. Cadavers from two female donors (ages 62 and 68 years) were scanned (L1–L5) using six different MDCT protocols and one dedicated QCT scan. Opportunistic vBMD was extracted using an artificial intelligence-based algorithm. The vBMD measurements from the six MDCT protocols, which varied in peak tube voltage (80–140 kVp), tube load (72–200 mAs), slice thickness (0.75–1 mm), and/or slice increment (0.5–0.75 mm), were compared to those obtained from dedicated QCT. A strong positive correlation was observed between vBMD from opportunistic MDCT and reference QCT (ρ = 0.869, p < 0.01). Agreement between vBMD measurements from MDCT protocols and the QCT reference standard according to the intraclass correlation coefficient (ICC) was 0.992 (95

Tension-type headache (TTH) is the most prevalent primary headache disorder. Neck pain is commonly associated with primary headaches and the trigemino-cervical complex (TCC) refers to the convergence of trigeminal and cervical afferents onto neurons of the brainstem, thus conceptualizes the emergence of headache in relation to neck pain. However, no objective biomarkers exist for the myofascial involvement in primary headaches. This study aimed to investigate the involvement of the trapezius muscles in primary headache disorders by quantitative magnetic resonance imaging (MRI), and to explore associations between muscle T2 values and headache frequency and neck pain. This cohort study prospectively enrolled fifty participants (41 females, age range 20–31 years): 16 subjects with TTH only (TTH-), 12 with mixed-type TTH plus migraine (TTH+), and 22 healthy controls (HC). The participants completed fat-suppressed T2‐prepared three-dimensional turbo spin-echo MRI, a headache diary (over 30 days prior to MRI), manual palpation (two weeks before MRI), and evaluation of neck pain (on the day of MRI). The bilateral trapezius muscles were manually segmented, followed by muscle T2 extraction. Associations between muscle T2 and the presence of neck pain as well as the number of days with headache (considering the 30 days prior to imaging using the headache calendar) were analyzed using regression models (adjusting for age, sex, and body mass index). The TTH+ group demonstrated the highest muscle T2 values (right side: 31.4 ± 1.2 ms, left side: 31.4 ± 0.8 ms) as compared to the TTH- group or HC group (p < 0.001). Muscle T2 was significantly associated with the number of headache days (β-coefficient: 2.04, p = 0.04) and the presence of neck pain (odds ratio: 2.26, p = 0.04). With muscle T2 as the predictor, the area under the curve for differentiating between HC and the TTH+ group was 0.82. Increased T2 of trapezius muscles may represent an objective imaging biomarker for myofascial involvement in primary headache disorders, which could help to improve patient phenotyping and therapy evaluation. Pathophysiologically, the increased muscle T2 values could be interpreted as a surrogate of neurogenic inflammation and peripheral sensitization within myofascial tissues.

This study investigated simulated tube current reduction and sparse sampling for low-dose computed tomography (CT) regarding volumetric bone mineral density (vBMD) and cortical bone thickness (Ct.Th) of the proximal femur. Sparse sampling with dose reductions of up to 90

BACKGROUND AND OBJECTIVES:Migraine is a multifaceted primary headache disorder. In neuroimaging of migraine, fMRI has been used to elucidate pathophysiology or monitor treatment effects. The current literature, however, is highly heterogeneous regarding reported variables and methodologies. This begets a lack of comparability and complicates synthesis of results across studies. We developed a framework for standardized reporting of fMRI studies in migraine. METHODS:Experts on fMRI in migraine were identified from the literature and subjected to structured questionnaires in 2 iterations of 3 rounds according to the DELPHI method. A total of 157 statements across 17 reporting domains were rated on 5-point Likert scales (strong support to strong opposition). The first iteration covered demographic data, migraine-specific factors, medication, scan timing, healthy controls (HCs), participant sampling/recruiting, standardized forms, study preregistration, region of interest (ROI) analyses, validation data sets, data sharing, preprocessing documentation, and analysis software. The second iteration of the questionnaire covered scanner-related factors, sequence-related factors, physiology monitoring, and stimulation-related factors. Items showing strong consensus/consensus (≥90%/≥75% of participants indicating scores 4 or 5) were included as standard reporting items. RESULTS:All 3 rounds of the first/second iteration were completed by 29 and 26 researchers (age 46 ± 11 years; 38% female/age 46 ± 12 years; 44% female) from 23 and 21 institutions. Across both iterations, strong consensus and consensus was achieved for 34 (3 scanner-related factors, 9 sequence-related factors, 1 stimulation-related factor, 2 demographic factors, 7 migraine-specific factors, 2 medication-factors, 2 scan timing factors, 4 HC factors, 1 preregistration factor, 1 analysis software factor, and 2 ROI analyses factors) and 33 (1 scanner-related factors, 4 sequence related factors, 1 factor related to physiology monitoring, 1 stimulation-related factor, 3 demographic factors, 6 migraine-specific factors, 4 medication factors, 3 HC factors, 2 sampling factors, 1 standardized form, 1 preregistration factor, 1 data sharing factor, 2 analysis software factors, and 3 ROI analyses factors) items, respectively. From these, a checklist covering 63 items from 14 reporting domains was created. DISCUSSION:We present an expert-based framework for reporting standards in fMRI studies of migraine, which can be used for future studies to homogenize cohort characterization, fMRI acquisitions, and analysis protocols.

The soybean-cyst nematode (SCN; Heterodera glycines) is one of the most destructive pests affecting soybean crops. Effective management of SCN is imperative for the sustainability of soybean agriculture. A promising approach to achieving this goal is the development and breeding of new resistant soybean varieties. Researchers and breeders typically employ exploratory methods such as Genome-Wide Association Studies or Quantitative Trait Loci mapping to identify genes linked to resistance. These methods depend on extensive phenotypic screening. The primary phenotypic measure for assessing SCN resistance is often the number of cysts that form on a plant’s root system. Manual counting hundreds of cysts on a given root system is not only laborious but also subject to variability due to individual assessor differences. Additionally, while measuring cyst size could provide valuable insights due to its correlation with cyst development, this aspect is frequently overlooked because it demands even more hands-on work. To address these challenges, we have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously. Nemacounter boasts a user-friendly graphical interface, simplifying the process for users to obtain reliable results. It enhances productivity by delivering annotated images and compiling data into csv files for easy analysis and reporting.

We developed a set of GATEWAY vectors to accelerate gene function analysis in soybean composite plants to rapidly screen transgenic roots and investigate subcellular localization, protein–protein interactions, and root-pathogen interactions. The generation of transgenic plants is essential for plant biology research to investigate plant physiology, pathogen interactions, and gene function. However, producing stable transgenic plants for plants such as soybean is a laborious and time-consuming process, which can impede research progress. Composite plants consisting of wild-type shoots and transgenic roots are an alternative method for generating transgenic plant tissues that can facilitate functional analysis of genes-of-interest involved in root development or root-microbe interactions. In this report, we introduce a novel set of GATEWAY-compatible vectors that enable a wide range of molecular biology uses in roots of soybean composite plants. These vectors incorporate in-frame epitope fusions of green fluorescent protein, 3x-HA, or miniTurbo-ID, which can be easily fused to a gene-of-interest using the GATEWAY cloning system. Moreover, these vectors allow for the identification of transgenic roots using either mCherry fluorescence or the RUBY marker. We demonstrate the functionality of these vectors by expressing subcellular markers in soybean, providing evidence of their effectiveness in generating protein fusions in composite soybean plants. Furthermore, we show how these vectors can be used for gene function analysis by expressing the bacterial effector, AvrPphB in composite roots, enabling the identification of soybean targets via immunoprecipitation followed by mass spectrometry. Additionally, we demonstrate the successful expression of stable miniTurbo-ID fusion proteins in composite roots. Overall, this new set of vectors is a powerful tool that can be used to assess subcellular localization and perform gene function analyses in soybean roots without the need to generate stable transgenic plants.

Plant-parasitic nematodes constrain global food security. During parasitism, they secrete effectors into the host plant from two types of pharyngeal gland cells. These effectors elicit profound changes in host biology to suppress immunity and establish a unique feeding organ from which the nematode draws nutrition. Despite the importance of effectors in nematode parasitism, there has been no comprehensive identification and characterisation of the effector repertoire of any plant-parasitic nematode. To address this, we advance techniques for gland cell isolation and transcriptional analysis to define a stringent annotation of putative effectors for the cyst nematode Heterodera schachtii at three key life-stages. We define 717 effector gene loci: 269 “known” high-confidence homologs of plant-parasitic nematode effectors, and 448 “novel” effectors with high gland cell expression. In doing so we define the most comprehensive “effectorome” of a plant-parasitic nematode to date. Using this effector definition, we provide the first systems-level understanding of the origin, deployment and evolution of a plant-parasitic nematode effectorome. The robust identification of the effector repertoire of a plant-parasitic nematode will underpin our understanding of nematode pathology, and hence, inform strategies for crop protection.

The soybean cyst nematode (SCN; Heterodera glycines) facilitates infection by secreting a repertoire of effector proteins into host cells to establish a permanent feeding site composed of a syncytium of root cells. Among the diverse proteins secreted by the nematode, we were specifically interested in identifying proteases to pursue our goal of engineering decoy substrates that elicit an immune response when cleaved by an SCN protease. We identified a cysteine protease that we named Cysteine Protease 1 (CPR1), which was predicted to be a secreted effector based on transcriptomic data obtained from SCN esophageal gland cells, the presence of a signal peptide, and the lack of transmembrane domains. CPR1 is conserved in all isolates of SCN sequenced to date, suggesting it is critical for virulence. Transient expression of CPR1 in Nicotiana benthamiana leaves suppressed cell death induced by a constitutively active nucleotide binding leucine-rich repeat protein, RPS5, indicating that CPR1 inhibits effector-triggered immunity. CPR1 localizes in part to the mitochondria when expressed in planta. Proximity-based labeling in transgenic soybean roots, co-immunoprecipitation, and cleavage assays identified a branched-chain amino acid aminotransferase from soybean (GmBCAT1) as a substrate of CPR1. Consistent with this, GmBCAT1 also localizes to mitochondria. Silencing of the CPR1 transcript in the nematode reduced penetration frequency in soybean roots, while the expression of CPR1 in soybean roots enhanced susceptibility. Our data demonstrates that CPR1 is a conserved effector protease with a direct target in soybean roots, highlighting it as a promising candidate for decoy engineering. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

ObjectivesTo quantitatively investigate the age- and sex-related longitudinal changes in trabecular volumetric bone mineral density (vBMD) and vertebral body volume at the thoracolumbar spine in adults.MethodsWe retrospectively included 168 adults (mean age 58.7 ± 9.8 years, 51 women) who received ≥7 MDCT scans over a period of ≥6.5 years (mean follow-up 9.0 ± 2.1 years) for clinical reasons. Level-wise vBMD and vertebral body volume were extracted from 22720 thoracolumbar vertebrae using a convolutional neural network (CNN)-based framework with asynchronous calibration and correction of the contrast media phase. Human readers conducted semiquantitative assessment of fracture status and bony degenerations.ResultsIn the 40-60 years age group, women had a significantly higher trabecular vBMD than men at all thoracolumbar levels (p<0.05 to p<0.001). Conversely, men, on average, had larger vertebrae with lower vBMD. This sex difference in vBMD did not persist in the 60-80 years age group. While the lumbar (T12-L5) vBMD slopes in women only showed a non-significant trend of accelerated decline with age, vertebrae T1-11 displayed a distinct pattern, with women demonstrating a significantly accelerated decline compared to men (p<0.01 to p<0.0001). Between baseline and last follow-up examinations, the vertebral body volume slightly increased in women (T1-12: 1.1 ± 1.0 cm3; L1-5: 1.0 ± 1.4 cm3) and men (T1-12: 1.2 ± 1.3 cm3; L1-5: 1.5 ± 1.6 cm3). After excluding vertebrae with bony degenerations, the residual increase was only small in women (T1-12: 0.6 ± 0.6 cm3; L1-5: 0.7 ± 0.7 cm3) and men (T1-12: 0.7 ± 0.6 cm3; L1-5: 1.2 ± 0.8 cm3). In non-degenerated vertebrae, the mean change in volume was <5% of the respective vertebral body volumes.ConclusionSex differences in thoracolumbar vBMD were apparent before menopause, and disappeared after menopause, likely attributable to an accelerated and more profound vBMD decline in women at the thoracic spine. In patients without advanced spine degeneration, the overall volumetric changes in the vertebral body appeared subtle.

BACKGROUND:Magnetic resonance (MRI) imaging of the skeletal muscles (muscle MRI for short) is increasingly being used in clinical routine for diagnosis and longitudinal assessment of muscle disorders. However, cross-centre standards for measurement protocol and radiological assessment are still lacking. OBJECTIVES:The aim of this expert recommendation is to present standards for the application and interpretation of muscle MRI in hereditary and inflammatory muscle disorders. METHODS:This work was developed in collaboration between neurologists, neuroradiologists, radiologists, neuropaediatricians, neuroscientists and MR physicists from different university hospitals in Germany. The recommendations are based on expert knowledge and a focused literature search. RESULTS:The indications for muscle MRI are explained, including the detection and monitoring of structural tissue changes and oedema in the muscle, as well as the identification of a suitable biopsy site. Recommendations for the examination procedure and selection of appropriate MRI sequences are given. Finally, steps for a structured radiological assessment are presented. CONCLUSIONS:The present work provides concrete recommendations for the indication, implementation and interpretation of muscle MRI in muscle disorders. Furthermore, it provides a possible basis for the standardisation of the measurement protocols at all clinical centres in Germany.

Abstract Background To investigate the reproducibility of automated volumetric bone mineral density (vBMD) measurements from routine thoracoabdominal computed tomography (CT) assessed with segmentations by a convolutional neural network and automated correction of contrast phases, on diverse scanners, with scanner-specific asynchronous or scanner-agnostic calibrations. Methods We obtained 679 observations from 278 CT scans in 121 patients (77 males, 63.6%) studied from 04/2019 to 06/2020. Observations consisted of two vBMD measurements from Δdifferent reconstruction kernels (n = 169), Δcontrast phases (n = 133), scan Δsessions (n = 123), Δscanners (n = 63), or Δall of the aforementioned (n = 20), and observations lacking scanner-specific calibration (n = 171). Precision was assessed using root-mean-square error (RMSE) and root-mean-square coefficient of variation (RMSCV). Cross-measurement agreement was assessed using Bland-Altman plots; outliers within 95% confidence interval of the limits of agreement were reviewed. Results Repeated measurements from Δdifferent reconstruction kernels were highly precise (RMSE 3.0 mg/cm3; RMSCV 1.3%), even for consecutive scans with different Δcontrast phases (RMSCV 2.9%). Measurements from different Δscan sessions or Δscanners showed decreased precision (RMSCV 4.7% and 4.9%, respectively). Plot-review identified 12 outliers from different scan Δsessions, with signs of hydropic decompensation. Observations with Δall differences showed decreased precision compared to those lacking scanner-specific calibration (RMSCV 5.9 and 3.7, respectively). Conclusion Automatic vBMD assessment from routine CT is precise across varying setups, when calibrated appropriately. Low precision was found in patients with signs of new or worsening hydropic decompensation, what should be considered an exclusion criterion for both opportunistic and dedicated quantitative CT. Relevance statement Automated CT-based vBMD measurements are precise in various scenarios, including cross-session and cross-scanner settings, and may therefore facilitate opportunistic screening for osteoporosis and surveillance of BMD in patients undergoing routine clinical CT scans. Key Points Artificial intelligence-based tools facilitate BMD measurements in routine clinical CT datasets. Automated BMD measurements are highly reproducible in various settings. Reliable, automated opportunistic osteoporosis diagnostics allow for large-scale application. Graphical Abstract

In patient-specific biomechanical modeling, the process of image-to-mesh-material mapping is important, and various strategies have been explored for assigning the number of groups of unique material properties to the mesh. This study aims to cross-compare different grouping strategies to identify the minimum number of unique groups necessary for accurately calculating the fracture load of vertebral bones. We analyzed 12 vertebral specimens by experimentally determining the biomechanical fracture load and acquiring corresponding CT scans. After geometry extraction and meshing, we applied commonly used fixed-value strategies for reducing the number of unique groups, such as Modulus Gaping and Percentual Thresholding. Additionally, we introduced a patient-specific adaptive grouping method based on K-means clustering, which allowed us to maintain a consistent number of groups of unique material properties across the study. A total of 204 simulations were performed, achieving a potential 98% reduction in the number of individual material parameters while maintaining a strong correlation with experimental results when utilizing Percentual Thresholding or Adaptive Clustering, compared to Modulus Gaping. The findings demonstrate the feasibility of significantly reducing simulation complexity while maintaining the accuracy of patient-specific models that strongly correlate with experimental results. This reduction enables efficient processing of patient-specific biomechanical models derived from image data, offering potential benefits for clinicians, particularly in resource-constrained settings.

The use of opportunistic computed tomography (CT) image-based biomarkers may be a low-cost strategy for screening older individuals at high risk for osteoporotic fractures and populations that are not sufficiently targeted. This review aimed to assess the discriminative ability of image-based biomarkers derived from existing clinical routine CT scans for hip, vertebral, and major osteoporotic fracture prediction. A systematic search in PubMed MEDLINE, Embase, Cochrane, and Web of Science was conducted from the earliest indexing date until July 2023. The evaluation of study quality was carried out using a modified Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2) checklist. The primary outcome of interest was the area under the curve (AUC) and its corresponding 95% confidence intervals (CIs) obtained for four main categories of biomarkers: areal bone mineral density (BMD), image attenuation, volumetric BMD, and finite element (FE)-derived biomarkers. The meta-analyses were performed using random effects models. Sixty-one studies were included in this review, among which 35 were synthesized in a meta-analysis and the remaining articles were qualitatively synthesized. In comparison to the pooled AUC of areal BMD (0.73 [95% CI 0.71-0.75]), the pooled AUC values for predicting osteoporotic fractures for FE-derived parameters (0.77 [95% CI 0.72-0.81]; p < 0.01) and volumetric BMD (0.76 [95% CI 0.71-0.81]; p < 0.01) were significantly higher, but there was no significant difference with the pooled AUC for image attenuation (0.73 [95% CI 0.66-0.79]; p = 0.93). Compared to areal BMD, volumetric BMD and FE-derived parameters may provide a significant improvement in the discrimination of osteoporotic fractures using opportunistic CT assessments.