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Associate Laboratory of Energy, Transports and Aeronautics
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Publicações
Pubovisceralis muscle fiber architecture determination: comparison between biomechanical modeling and diffusion tensor imaging
Publication . Brandão, Sofia; Parente, Marco; Silva, Elisabete; Da Roza, Thuane; Mascarenhas, Teresa; Leitão, João; Cunha, João; Natal Jorge, Renato; Nunes, Rita G.
Biomechanical analysis of pelvic floor dysfunction requires knowledge of certain biomechanical parameters, such as muscle fiber direction, in order to adequately model function. Magnetic resonance (MR) diffusion tensor imaging (DTI) provides an estimate of overall muscle fiber directionality based on the mathematical description of water diffusivity. This work aimed at evaluating the concurrence between pubovisceralis muscle fiber representations obtained from DTI, and the maximum principal stress lines obtained through the finite element method. Seven datasets from axial T2-weighted images were used to build numerical models, and muscle fiber orientation estimated from the DT images. The in-plane projections of the first eigenvector of both vector fields describing muscle fiber orientation were extracted and compared. The directional consistency was evaluated by calculating the angle between the normalized vectors for the entire muscle and also for the right and left insertions, middle portions, and anorectal area. The values varied between 28° ± 6 (right middle portion) and 34° ± 9 (anorectal area), and were higher than the angular precision of the DT estimates, evaluated using wild bootstrapping analysis. Angular dispersion ranged from 17° ± 4 (left middle portion) to 23° ± 5 (anorectal area). Further studies are needed to examine acceptability of these differences when integrating the vectors estimated from DTI in the numerical analysis.
Ensemble outlier detection and gene selection in triple-negative breast cancer data
Publication . Lopes, Marta B.; Veríssimo, André; Carrasquinha, Eunice; Casimiro, Sandra; Beerenwinkel, Niko; Vinga, Susana
Background: Learning accurate models from 'omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental errors or interesting abnormal clinical cases, may severely hamper a correct classification of patients and the identification of reliable biomarkers for a particular disease. We propose to address this problem through an ensemble classification setting based on distinct feature selection and modeling strategies, including logistic regression with elastic net regularization, Sparse Partial Least Squares - Discriminant Analysis (SPLS-DA) and Sparse Generalized PLS (SGPLS), coupled with an evaluation of the individuals' outlierness based on the Cook's distance. The consensus is achieved with the Rank Product statistics corrected for multiple testing, which gives a final list of sorted observations by their outlierness level.
Results: We applied this strategy for the classification of Triple-Negative Breast Cancer (TNBC) RNA-Seq and clinical data from the Cancer Genome Atlas (TCGA). The detected 24 outliers were identified as putative mislabeled samples, corresponding to individuals with discrepant clinical labels for the HER2 receptor, but also individuals with abnormal expression values of ER, PR and HER2, contradictory with the corresponding clinical labels, which may invalidate the initial TNBC label. Moreover, the model consensus approach leads to the selection of a set of genes that may be linked to the disease. These results are robust to a resampling approach, either by selecting a subset of patients or a subset of genes, with a significant overlap of the outlier patients identified.
Conclusions: The proposed ensemble outlier detection approach constitutes a robust procedure to identify abnormal cases and consensus covariates, which may improve biomarker selection for precision medicine applications. The method can also be easily extended to other regression models and datasets.
Bone remodelling of the humerus after a resurfacing and a stemless shoulder arthroplasty
Publication . Santos, B.; Quental, Carlos; Folgado, João; Sarmento, Marco; Monteiro, Jacinto
Background: New implant designs, such as resurfacing and stemless implants, have been developed to improve the long-term outcomes of the shoulder arthroplasty. However, it is not yet fully understood if their influence on the bone load distribution can compromise the long-term stability of the implant due to bone mass changes. Using three-dimensional finite element models, the aim of the present study was to analyse the bone remodelling process of the humerus after the introduction of resurfacing and stemless implants based on the Global C.A.P. and Sidus Stem-Free designs, respectively.
Methods: The 3D geometric model of the humerus was generated from the CT data of the Visible Human Project and the resurfacing and stemless implants were modelled in Solidworks. Considering a native humerus model, a humerus model with the resurfacing implant, and a humerus model with the stemless implant, three finite element models were developed in Abaqus. Bone remodelling simulations were performed considering healthy and poor bone quality conditions. The loading condition considered comprised 6 load cases of standard shoulder movements, including muscle and joint reaction forces estimated by a multibody model of the upper limb.
Findings: The results showed similar levels of bone resorption for the resurfacing and stemless implants for common humeral regions. The regions underneath the head of the resurfacing implant, unique to this design, showed the largest bone loss. For both implants, bone resorption was more pronounced for the poor bone quality condition than for the healthy bone quality condition.
Interpretation: The stemless implant lost less density at the fixation site, which might suggest that these implants may be better supported in the long-term than the resurfacing implants. However, further investigation is necessary to allow definite recommendations.
Robust identification of target genes and outliers in triple-negative breast cancer data
Publication . Segaert, Pieter; Lopes, Marta B.; Casimiro, Sandra; Vinga, Susana; Rousseeuw, Peter J.
Correct classification of breast cancer subtypes is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma transcriptomic data publicly available from The Cancer Genome Atlas data portal. Our analysis identifies statistical outliers that may correspond to misdiagnosed patients. Furthermore, it is illustrated that classical statistical methods may fail to identify outliers due to their heavy influence, prompting the need for robust statistics. Using robust sparse logistic regression we obtain 36 relevant genes, of which ca. 60% have been previously reported as biologically relevant to triple-negative breast cancer, reinforcing the validity of the method. The remaining 14 genes identified are new potential biomarkers for triple-negative breast cancer. Out of these, JAM3, SFT2D2, and PAPSS1 were previously associated to breast tumors or other types of cancer. The relevance of these genes is confirmed by the new DetectDeviatingCells outlier detection technique. A comparison of gene networks on the selected genes showed significant differences between triple-negative breast cancer and non-triple-negative breast cancer data. The individual role of FOXA1 in triple-negative breast cancer and non-triple-negative breast cancer, and the strong FOXA1-AGR2 connection in triple-negative breast cancer stand out. The goal of our paper is to contribute to the breast cancer/triple-negative breast cancer understanding and management. At the same time it demonstrates that robust regression and outlier detection constitute key strategies to cope with high-dimensional clinical data such as omics data.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UID/EMS/50022/2013