MOX Reports
The preprint collection of the Laboratory for Modeling and Scientific Computation MOX. It mainly contains works on numerical
analysis and mathematical modeling applied to engineering problems. MOX web site is mox.polimi.it
Found 1239 products
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24/2023 - 03/02/2023
Costa, G.; Cavinato, L.; Fiz, F.; Sollini, M.; Chiti, A.; Torzilli, G.; Ieva, F.; Viganò, L.
Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible | Abstract | | Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based
measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking
clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make
entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data. We analyzed the portal phase of preoperative
CT of 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5×5×5 voxels)
was created and used to compute the local entropy value for each voxel of the tumor. The map was encoded with a color
palette. We performed two analyses: (a) qualitative assessment of tumors’ detectability and pattern of entropy distribution;
(b) quantitative analysis of the entropy values distribution. The latter data were compared with standard Hounsfield data
as predictors of post-chemotherapy tumor regression grade (TRG). Entropy maps were successfully built for all tumors.
Metastases were qualitatively hyper-entropic compared to surrounding parenchyma. In four cases hyper-entropic areas
exceeded the tumor margin visible at CT. We identified four “entropic” patterns: homogeneous, inhomogeneous, peripheral rim, and mixed. At quantitative analysis, entropy-derived data (percentiles/mean/median/root mean square) predicted
TRG (p<0.05) better than Hounsfield-derived ones (p=n.s.). We present a standardized imaging technique to visualize
tumor heterogeneity built on a voxel-by-voxel entropy assessment. The association of local entropy with pathology data
supports its role as a biomarker. |
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23/2023 - 02/27/2023
Bertoletti, A.; Cannistrà, M.; Diaz Lema, M.; Masci, C.; Mergoni, A.; Rossi, L.; Soncin, M.
The Determinants of Mathematics Achievement: A Gender Perspective Using Multilevel Random Forest | Abstract | | This paper investigates the determinants of mathematics performance
by gender, exploiting a multilevel random forest approach. OECD PISA
2018 data from 28 European countries are employed to explore the performance
of male and female students as a function of students’ family
characteristics, their attitudes towards education, and class and school
environment. Results show that the gender gap in favour of boys persists
in most European countries. However, teacher and school practices like
fostering student reading and creating a cooperative environment allow
mitigating the influence of family background in countries without gender
gap. Policy implications to foster performance equality are provided. |
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22/2023 - 02/27/2023
Su, Y.; Riccobelli, D.; Chen, Y; Chen, W; Ciarletta, P
Tunable morphing of electroactive dielectric-elastomer balloons | Abstract | | Designing smart devices with tunable shapes has important applications in industrial manufacture. In this paper, we investigate the nonlinear deformation and the morphological transitions between buckling, necking, and snap-through instabilities of layered DE balloons in response to an applied radial voltage and an inner pressure. We propose a general mathematical theory of nonlinear electro-elasticity able to account for finite inhomogeneous strains provoked by the electro-mechanical coupling.
We investigate the onsets of morphological transitions of the spherically symmetric balloons using the surface impedance matrix method. Moreover, we study the nonlinear evolution of the bifurcated branches through finite element numerical simulations. Our analysis demonstrates the possibility to design tunable DE spheres, where the onset of buckling and necking can be controlled by geometrical and mechanical properties of the passive elastic layers. Relevant applications include soft robotics and mechanical actuators. |
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21/2023 - 02/26/2023
Cavinato, L.; Sollini, M.; Ragni, A.; Bartoli, F.; Zanca, R.; Pasqualetti, F.; Marciano, A.; Ieva, F.; and Erba, A.P.
Radiomics-based Inter-lesion Relation Network to Describe [18F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer | Abstract | | Advanced image analysis, including radiomics, has recently acquired recognition as a source of biomarkers, although there are some technical and methodological challenges to face for its application in the clinic. Among others, proper phenotyping of metastatic or systemic disease where multiple lesions coexist is an issue, since each lesion contributes to characterization of the disease. Therefore, the radiomic profile of each lesion should be modeled into a more complex architecture able to reproduce each “unit” (lesion) as a part of the “entire” (patient). This work aimed to characterize intra-tumor heterogeneity underpinning metastatic prostate cancer using an exhaustive innovative approach which consist of a i) feature transformation method to build an agnostic (i.e., irrespective of pre-existence knowledge, experience, and expertise) radiomic pro-file of lesions extracted from [18F]FMCH PET/CT, ii) qualitative assessment of intra-tumor heterogeneity of patients, iii) quantitative representation of the intra-tumor heterogeneity of patients in terms of the relationship between their lesions’ profiles, to be associated with prognostic factors. We confirmed that metastatic prostate cancer patients encompassed lesions with different radio-mic profiles that exhibited intra-tumor radiomic heterogeneity and that the presence of many radiomic profiles within the same patient impacted the outcome. |
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20/2023 - 02/24/2023
Ciaramella, G.; Nobile, F.; Vanzan, T.
A multigrid method for PDE-constrained optimization with uncertain inputs | Abstract | | We present a multigrid algorithm to solve efficiently the large saddle-point systems of equations that typically arise in PDE-constrained optimization under uncertainty. The algorithm is based on a collective smoother that at each iteration sweeps over the nodes of the computational mesh, and solves a reduced saddle-point system whose size depends on the number N of samples used to discretized the probability space. We show that this reduced system can be solved with optimal O(N) complexity. We test the multigrid method on three problems: a linear-quadratic problem for which the multigrid method is used to solve directly the linear optimality system; a nonsmooth problem with box constraints and L 1 -norm penalization on the control, in which the multigrid scheme is used within a semismooth Newton iteration; a risk-adverse problem with the smoothed CVaR risk measure where the multigrid method is called within a preconditioned Newton iteration. In all cases, the multigrid algorithm exhibits very good performances and robustness with respect to all parameters of interest. |
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19/2023 - 02/24/2023
Marcinno', F.; Vergara, C.; Giovannacci, L.; Quarteroni, A.; Prouse, G.
Computational fluid-structure interaction analysis of the end-to-side radio-cephalic arteriovenous fistula | Abstract | | In the current work, we present a fluid-structure interaction study of the end-tosideradio-
cephalic arteriovenous fistula. The core of the work consists in simulating
different arteriovenous fistula configurations obtained by virtually varying the
anastomosis angle, i.e. the angle between the end of the cephalic vein and the side of
the radial artery.The mesh used to solve the structural problem takes into account the
different thickness and Young's modulus of the vessel walls. In particular, since the aim
of the work is to understand the blood dynamics in the very first days after the surgical
intervention, the radial artery is considered stiffer and thicker than the cephalic
vein.Our results demonstrate that both the diameter of the cephalic vein and the
anastomosis angle play a crucial role in order to obtain a regular blood dynamics that
could prevent fistula failure.In particular, when a high anastomosis angle is combined
with a large diameter of the cephalic vein, the recirculation regions and the low WSS
(wall shear stress) zones are reduced. Conversely, from a structural point of view, a
low anastomosis angle with a large diameter of the cephalic vein reduce the
mechanical stress acting on the vessel walls. |
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17/2023 - 02/24/2023
Savin, M.S.; Cavinato, L.; Costa, G.; Fiz, F.; Torzilli, G.; Vigano', L.; Ieva, F.
Distant supervision for imaging-based cancer sub-typing in Intrahepatic Cholangiocarcinoma | Abstract | | Finding effective ways to perform cancer sub-typing is currently a trending research topic for therapy optimization and personalized medicine. Stemming from genomic field, several algorithms have been proposed. In the context of texture analysis, limited efforts have been attempted, yet imaging information is known to entail useful knowledge for clinical practice. We propose a distant supervision model for imaging-based cancer sub-typing in Intrahepatic Cholangiocarcinoma patients. A clinically informed stratification of patients is built and homogeneous groups of patients are characterized in terms of survival probabilities, qualitative cancer variables and radiomic feature description. Moreover, the contributions of the information derived from the ICC area and from the peritumoral area are evaluated. The findings suggest the reliability of the proposed model in the context of cancer research and testify the importance of accounting for data coming from both the tumour and the tumour-tissue interface. |
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15/2023 - 02/22/2023
Ragni, A.; Masci, C.; Ieva, F.; Paganoni, A. M.
Clustering Hierarchies via a Semi-Parametric Generalized Linear Mixed Model: a statistical significance-based approach | Abstract | | We introduce a novel statistical significance-based approach for clustering hierarchical data using semi-parametric linear mixed-effects models designed for responses with laws in the exponential family (e.g., Poisson and Bernoulli). Within the family of semi-parametric mixed-effects models, a latent clustering structure of the highest-level units can be identified by assuming the random effects to follow a discrete distribution with an unknown number of support points. We achieve this by computing alpha-level confidence regions of the estimated support point and identifying statistically different clusters. At each iteration of a tailored Expectation Maximization algorithm, the two closest estimated support points for which the confidence regions overlap collapse. Unlike the related state-of-the-art methods that rely on arbitrary thresholds to determine the merging of close discrete masses, the proposed approach relies on conventional statistical confidence levels, thereby avoiding the use of discretionary tuning parameters. To demonstrate the effectiveness of our approach, we apply it to data from the Programme for International Student Assessment (PISA - OECD) to cluster countries based on the rate of innumeracy levels in schools. Additionally, a simulation study and comparison with classical parametric and state-of-the-art models are provided and discussed. |
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