Evaluation of inter- and intra-operator reliability of manual segmentation of femoral metastatic lesions

Ataei, Ali; Eggermont, Florieke; Baars, Milan; Linden, Yvette M. van der; Rooy, Jacky de; Tanck, E.

doi:10.1007/s11548-021-02450-w

Cited by 4 publications

(2 citation statements)

References 19 publications

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“… 23 Also, implementation of the strain-dependent approach requires well-defined lytic lesions to evaluate lesion region strain as mixed/diffuse metastatic lesion geometries/boundaries are harder to identify. 46 However, identifying lesion boundaries is not critical in nonlinear material-based global femur strength outcome simulations, which inherently account for change in material behavior resulting from MBD. Also, nonlinear models have been shown to explain more variance in failure load.…”

Section: Discussionmentioning

confidence: 99%

Finite Element Model-Computed Mechanical Behavior of Femurs with Metastatic Disease Varies Between Physiologic and Idealized Loading Simulations

Johnson

Brouillette

Miller

et al. 2023

Biomed Eng Comput Biol

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Background and objectives: Femurs affected by metastatic bone disease (MBD) frequently undergo surgery to prevent impending pathologic fractures due to clinician-perceived increases in fracture risk. Finite element (FE) models can provide more objective assessments of fracture risk. However, FE models of femurs with MBD have implemented strain- and strength-based estimates of fracture risk under a wide variety of loading configurations, and “physiologic” loading models typically simulate a single abductor force. Due to these variations, it is currently difficult to interpret mechanical fracture risk results across studies of femoral MBD. Our aims were to evaluate (1) differences in mechanical behavior between idealized loading configurations and those incorporating physiologic muscle forces, and (2) differences in the rankings of mechanical behavior between different loading configurations, in FE simulations to predict fracture risk in femurs with MBD. Methods: We evaluated 9 different patient-specific FE loading simulations for a cohort of 54 MBD femurs: strain outcome simulations—physiologic (normal walking [NW], stair ascent [SA], stumbling), and joint contact only (NW contact force, excluding muscle forces); strength outcome simulations—physiologic (NW, SA), joint contact only, offset torsion, and sideways fall. Tensile principal strain and femur strength were compared between simulations using statistical analyses. Results: Tensile principal strain was 26% higher ( R2 = 0.719, P < .001) and femur strength was 4% lower ( R2 = 0.984, P < .001) in simulations excluding physiologic muscle forces. Rankings of the mechanical predictions were correlated between the strain outcome simulations (ρ = 0.723 to 0.990, P < .001), and between strength outcome simulations (ρ = 0.524 to 0.984, P < .001). Conclusions: Overall, simulations incorporating physiologic muscle forces affected local strain outcomes more than global strength outcomes. Absolute values of strain and strength computed using idealized (no muscle forces) and physiologic loading configurations should be used within the appropriate context when interpreting fracture risk in femurs with MBD.

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Section: Discussionmentioning

confidence: 99%

Finite Element Model-Computed Mechanical Behavior of Femurs with Metastatic Disease Varies Between Physiologic and Idealized Loading Simulations

Johnson

Brouillette

Miller

et al. 2023

Biomed Eng Comput Biol

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“…Selection of the depth-varying threshold is done based on the thresholds recommended by the automatic “Edge Finder” and “Mean + Standard Deviation” approaches, but the selection of which algorithm to use, and the exact depth-varying threshold, as well as manual removal of branching vessels, all introduce some degree of subjectivity into the segmentation process. The intra- and inter-operator variability in the resulting segmentation is calculated using the 3D dice-coefficient, DC [ 32 ]: where A and B are two different segmentation volumes. The average intra-operator variability was 0.89, and the average inter-operator variability was 0.86.…”

Section: Methodsmentioning

confidence: 99%

Sizes and shapes of perivascular spaces surrounding murine pial arteries

Raicevic

Forer

Ladrón-de-Guevara

et al. 2023

Fluids Barriers CNS

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Background Flow of cerebrospinal fluid (CSF) through brain perivascular spaces (PVSs) is essential for the clearance of interstitial metabolic waste products whose accumulation and aggregation is a key mechanism of pathogenesis in many diseases. The PVS geometry has important implications for CSF flow as it affects CSF and solute transport rates. Thus, the size and shape of the perivascular spaces are essential parameters for models of CSF transport in the brain and require accurate quantification. Methods We segmented two-photon images of pial (surface) PVSs and the adjacent arteries and characterized their sizes and shapes of cross sections from 14 PVS segments in 9 mice. Based on the analysis, we propose an idealized model that approximates the cross-sectional size and shape of pial PVSs, closely matching their area ratios and hydraulic resistances. Results The ratio of PVS-to-vessel area varies widely across the cross sections analyzed. The hydraulic resistance per unit length of the PVS scales with the PVS cross-sectional area, and we found a power-law fit that predicts resistance as a function of the area. Three idealized geometric models were compared to PVSs imaged in vivo, and their accuracy in reproducing hydraulic resistances and PVS-to-vessel area ratios were evaluated. The area ratio was obtained across different cross sections, and we found that the distribution peaks for the original PVS and its closest idealized fit (polynomial fit) were 1.12 and 1.21, respectively. The peak of the hydraulic resistance distribution is $$1.73\times 10^{15}$$ 1.73 × 10 15 Pa s/m$$^{5}$$ 5 and $$1.44\times 10^{15}$$ 1.44 × 10 15 Pa s/m$$^{5}$$ 5 for the segmentation and its closest idealized fit, respectively. Conclusions PVS hydraulic resistance can be reasonably predicted as a function of the PVS area. The proposed polynomial-based fit most closely captures the shape of the PVS with respect to area ratio and hydraulic resistance. Idealized PVS shapes are convenient for modeling, which can be used to better understand how anatomical variations affect clearance and drug transport.

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The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis

Ataei,

Eggermont,

Verdonschot

et al. 2024

Bone

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Evaluation of inter- and intra-operator reliability of manual segmentation of femoral metastatic lesions

Cited by 4 publications

References 19 publications

Finite Element Model-Computed Mechanical Behavior of Femurs with Metastatic Disease Varies Between Physiologic and Idealized Loading Simulations

Finite Element Model-Computed Mechanical Behavior of Femurs with Metastatic Disease Varies Between Physiologic and Idealized Loading Simulations

Sizes and shapes of perivascular spaces surrounding murine pial arteries

The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis

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