Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Purpose.</jats:italic> To utilize radiomic features extracted from CT images to characterize Human Papilloma Virus (HPV) for patients with oropharyngeal cancer squamous cell carcinoma (OPSCC). <jats:italic>Methods.</jats:italic> One hundred twenty-eight OPSCC patients with known HPV-status (60-HPV + and 68-HPV-, confirmed by immunohistochemistry-P16-protein testing) were retrospectively studied. Radiomic features (11 feature-categories) were extracted in 3D from contrast-enhanced (CE)-CT images of gross-tumor-volumes using ‘in-house’ software (‘ROdiomiX’) developed and validated following the image-biomarker-standardization-initiative (IBSI) guidelines. Six clinical factors were investigated: Age-at-Diagnosis, Gender, Total-Charlson, Alcohol-Use, Smoking-History, and T-Stage. A Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) technique combined with a Generalized-Linear-Model (Lasso-GLM) were applied to perform regularization in the radiomic and clinical feature spaces to identify the ranking of optimal feature subsets with most representative information for prediction of HPV. Lasso-GLM models/classifiers based on clinical factors only, radiomics only, and combined clinical and radiomics (ensemble/integrated) were constructed using random-permutation-sampling. Tests of significance (One-way ANOVA), average Area-Under-Receiver-Operating-Characteristic (AUC), and Positive and Negative Predictive values (PPV and NPV) were computed to estimate the generalization-error and prediction performance of the classifiers. <jats:italic>Results.</jats:italic> Five clinical factors, including T-stage, smoking status, and age, and 14 radiomic features, including tumor morphology, and intensity contrast were found to be statistically significant discriminators between HPV positive and negative cohorts. Performances for prediction of HPV for the 3 classifiers were: Radiomics-Lasso-GLM: AUC/PPV/NPV = 0.789/0.755/0.805; Clinical-Lasso-GLM: 0.676/0.747/0.672, and Integrated/Ensemble-Lasso-GLM: 0.895/0.874/0.844. Results imply that the radiomics-based classifier enabled better characterization and performance prediction of HPV relative to clinical factors, and that the combination of both radiomics and clinical factors yields even higher accuracy characterization and predictive performance. <jats:italic>Conclusion.</jats:italic> Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results in support of the role of radiomic features towards characterization of HPV in patients with OPSCC.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The linear-quadratic (LQ) model is the most commonly used mechanism to predict radiobiological outcomes. It has been used extensively to describe dose-response <jats:italic>in vitro</jats:italic> and <jats:italic>in vivo</jats:italic>. There are, however, some questions about its applicability in terms of its capacity to represent some profound mechanistic behaviour. Specifically, empirical evidence suggests that the LQ model underestimates the survival of cells at low doses while overestimating cell death at higher doses. It is believed to be driven from the usual LQ model assumption that radiogenic lesions are Poisson distributed. In this context, we use a negative binomial (NB) distribution to study the effect of overdispersion on the shapes and the possibility of reducing dose-response curvature at higher doses. We develop an overdispersion model for cell survival using the non-homologous end-joining (NHEJ) pathway double-strand break (DSB) repair mechanism to investigate the effects of the overdispersion on probabilities of repair of DSBs. The error distribution is customised to ensure that the refined overdispersion parameter depends on the mean of the distribution. The predicted cell survival responses for V79, AG and HSG cells exposed to protons, helium and carbon ions are compared with the experimental data in low and high dose regions at various linear energy transfer (LET) values. The results indicate straightening of dose-response and approaching a log-linear behaviour at higher doses. The model predictions with the measured data show that the NB modelled survival curves agree with the data following medium and high doses. Model predictions are not validated at very tiny and very high doses; the approach presented provides an analysis of mechanisms at the microscopic level. This may help improve the understanding of radiobiological responses of survival curves and resolve discrepancies between experimental and theoretical predictions of cell survival models.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Epithelial to Mesenchymal Transition (EMT) plays an important role in tissue regeneration, embryonic development, and cancer metastasis. Several signaling pathways are known to regulate EMT, among which the modulation of TGF<jats:italic>β</jats:italic> (Transforming Growth Factor-<jats:italic>β</jats:italic>) induced EMT is crucial in several cancer types. Several mathematical models were built to explore the role of core regulatory circuit of ZEB/miR-200, SNAIL/miR-34 double negative feedback loops in modulating TGF<jats:italic>β</jats:italic> induced EMT. Different emergent behavior including tristability, irreversible switching, existence of hybrid EMT states were inferred though these models. Some studies have explored the role of TGF<jats:italic>β</jats:italic> receptor activation, SMADs nucleocytoplasmic shuttling and complex formation. Recent experiments have revealed that MDM2 along with SMAD complex regulates SNAIL expression driven EMT. Encouraged by this, in the present study we developed a mathematical model for p53/MDM2 dependent TGF<jats:italic>β</jats:italic> induced EMT regulation. Inclusion of p53 brings in an additional mechanistic perspective in exploring the EM transition. The network formulated comprises a C1FFL moderating SNAIL expression involving MDM2 and SMAD complex, which functions as a noise filter and persistent detector. The C1FFL was also observed to operate as a coincidence detector driving the SNAIL dependent downstream signaling into phenotypic switching decision. Systems modelling and analysis of the devised network, displayed interesting dynamic behavior, systems response to various inputs stimulus, providing a better understanding of p53/MDM2 dependent TGF-<jats:italic>β</jats:italic> induced Epithelial to Mesenchymal Transition.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Noise reduction while preserving spatial resolution is one of the most important challenges in the reconstructing of emission tomography images. One of the resolving methods is the Bowsher maximum a-posteriori expectation-maximization reconstruction (MAPEM) algorithm. This method considers a binary selection of the neighbors of each voxel based on the prior anatomical values to use in the regularization function. This method is particularly susceptible to imposing the wrong data into the reconstructed image due to the spatial or functional inconsistencies between the anatomical image and the actual activity distribution. Because of the poor spatial resolution of single-photon emission tomography (SPECT) images and the different nature of emission and anatomical imaging, there is not enough certainty of inconsistency with anatomical images. Therefore, we proposed a new weighted Bowsher method that can overcome this weakness while the image quality indexes, especially the spatial resolution, are almost preserved. In the proposed method, each of the neighbors of a specific voxel takes a constant weight depending on the order of its value and independent of its intensity quantity. The proposed method was evaluated using some different physical phantoms and a patient scan. The results show that the proposed method has superiority in the presence of inconsistency; moreover, the proposed method gives nearly similar results to the regular Bowsher MAPEM in case of consistency. In conclusion, we show that using a suitable constant weighting factor in Bowsher MAPEM, one can operatively reduce the image noise while preserving the image quality parameters where the emission tomography images are either consistent or inconsistent with the prior anatomical map.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Neural entrainment, the synchronization of brain oscillations to the frequency of an external stimuli, is a key mechanism that shapes perceptual and cognitive processes. <jats:italic>Objective.</jats:italic> Using simulations, we investigated the dynamics of neural entrainment, particularly the period following the end of the stimulation, since the persistence (reverberation) of neural entrainment may condition future sensory representations based on predictions about stimulus rhythmicity. <jats:italic>Methods.</jats:italic> Neural entrainment was assessed using a modified Jansen-Rit neural mass model (NMM) of coupled cortical columns, in which the spectral features of the output resembled that of the electroencephalogram (EEG). We evaluated spectro-temporal features of entrainment as a function of the stimulation frequency, the resonant frequency of the neural populations comprising the NMM, and the coupling strength between cortical columns. Furthermore, we tested if the entrainment persistence depended on the phase of the EEG-like oscillation at the time the stimulus ended. <jats:italic>Main Results.</jats:italic> The entrainment of the column that received the stimulation was maximum when the frequency of the entrainer was within a narrow range around the resonant frequency of the column. When this occurred, entrainment persisted for several cycles after the stimulus terminated, and the propagation of the entrainment to other columns was facilitated. Propagation also depended on the resonant frequency of the second column, and the coupling strength between columns. The duration of the persistence of the entrainment depended on the phase of the neural oscillation at the time the entrainer terminated, such that falling phases (from <jats:italic>π</jats:italic>/2 to 3<jats:italic>π</jats:italic>/2 in a sine function) led to longer persistence than rising phases (from 0 to <jats:italic>π</jats:italic>/2 and 3<jats:italic>π</jats:italic>/2 to 2<jats:italic>π</jats:italic>). <jats:italic>Significance.</jats:italic> The study bridges between models of neural oscillations and empirical electrophysiology, providing insights to the mechanisms underlying neural entrainment and the use of rhythmic sensory stimulation for neuroenhancement.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>In a cone beam CT system, a bowtie filter brings in additional scatter signals with respect to object induced scatter signals, which can degrade image quality and sometimes result in artifacts. This work aims to improve the image quality of CT scans by analyzing the contribution of bowtie filter induced scatter signals and removing them from projection data. Air calibration is a very useful preprocessing step to eliminate the response variations of detector pixels. Bowtie filter induced scattered x-ray signals of air scans are recorded in air calibration tables and therefore considered as a part of primary signals. However, scattered X-rays behave differently in scanned objects compared to primary x-rays. The difference should be corrected to eliminate the impact of bowtie filter induced scatter signals. A kernel based correction algorithm based on air scan data, named bowtie filter scatter correction algorithm, is applied to estimate and to eliminate the bowtie filter induced scatter signals in object scans. The scatter signals of air scans can be measured with air scans or retrieved from air calibration tables of a CT system, and can be used as input of the correction algorithm to estimate the change of scatter signals caused by the scanned objects in the scan field. Based on the assumption that the scatter signals in the projection data scanned with narrow collimation can be neglected, the difference signals between narrow and broad collimations can be used to estimate bowtie filter induced scatter signals for air scans with the correction of extra-focal radiations (EFRs). The calculated bowtie filter induced scatter signals have been compared with the results of Monte Carlo simulations, and the parameters of correction algorithm have been determined by fitting the measured scatter signal curves of phantom scans with calculated curves. Projection data have been reconstructed using Filtered BackProjection (FBP) method with and without bowtie filter correction to check whether the image quality is improved. Scatter signals can be well approximated with the bowtie filter scatter correction algorithm together with an existing object scatter correction algorithm. After removing the bowtie filter induced scatter signals, the dark bands in reconstructed images in the regions near the edges of scanned objects can be mostly eliminated. The difference signals of air scan data between narrow and broad collimations can be used to estimate the bowtie filter induced scatter for air scans. The proposed bowtie filter scatter correction algorithm using air scan data can be applied to estimate and to remove most of the bowtie filter induced scatter signals in object scans.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>This study investigates the equivalence or compatibility between U-Net and visual segmentations of fibroglandular tissue regions by mammography experts for calculating the breast density and mean glandular dose (MGD). A total of 703 mediolateral oblique-view mammograms were used for segmentation. Two region types were set as the ground truth (determined visually): (1) one type included only the region where fibroglandular tissue was identifiable (called the ‘dense region’); (2) the other type included the region where the fibroglandular tissue may have existed in the past, provided that apparent adipose-only parts, such as the retromammary space, are excluded (the ‘diffuse region’). U-Net was trained to segment the fibroglandular tissue region with an adaptive moment estimation optimiser, five-fold cross-validated with 400 training and 100 validation mammograms, and tested with 203 mammograms. The breast density and MGD were calculated using the van Engeland and Dance formulas, respectively, and compared between U-Net and the ground truth with the Dice similarity coefficient and Bland–Altman analysis. Dice similarity coefficients between U-Net and the ground truth were 0.895 and 0.939 for the dense and diffuse regions, respectively. In the Bland–Altman analysis, no proportional or fixed errors were discovered in either the dense or diffuse region for breast density, whereas a slight proportional error was discovered in both regions for the MGD (the slopes of the regression lines were −0.0299 and −0.0443 for the dense and diffuse regions, respectively). Consequently, the U-Net and ground truth were deemed equivalent (interchangeable) for breast density and compatible (interchangeable following four simple arithmetic operations) for MGD. U-Net-based segmentation of the fibroglandular tissue region was satisfactory for both regions, providing reliable segmentation for breast density and MGD calculations. U-Net will be useful in developing a reliable individualised screening-mammography programme, instead of relying on the visual judgement of mammography experts.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective</jats:italic>. To evaluate the impact of image reconstruction algorithm selection, as well as imaging mode and the reconstruction interval, on image quality metrics for megavoltage computed tomography (MVCT) image acquisition for use in image-guided (IGRT) and adaptive radiotherapy (ART) on a next-generation helical tomotherapy system. <jats:italic>Approach</jats:italic>. A CT image quality phantom was scanned across all available acquisition modes for filtered back projection (FBP) and both iterative reconstruction (IR) algorithms available on the system. Image quality metrics including noise, uniformity, contrast, spatial resolution, and mean CT number were compared. Analysis of DICOM data was performed using ImageJ software and Python code. ANOVA single factor and Tukey’s honestly significant difference post-hoc tests were utilized for statistical analysis. <jats:italic>Main Results</jats:italic>. Application of both IR algorithms noticeably improved noise and image contrast when compared to the FBP algorithm available on all previous-generation helical tomotherapy systems. Use of the FBP algorithm improved image uniformity and spatial resolution in the axial plane, though values for the IR algorithms were well within tolerances recommended for IGRT and/or MVCT-based ART implementation by the American Association of Physicists in Medicine (AAPM). Additionally, longitudinal resolution showed little dependence on the reconstruction algorithm, while a negligible variation in mean CT number was observed regardless of the reconstruction algorithm or acquisition parameters. Statistical analysis confirmed the significance of these results. <jats:italic>Significance</jats:italic>. An overall improvement in image quality for metrics most important to IGRT and ART—mainly image noise and contrast—was evident in the application of IR when compared to FBP. Furthermore, since other imaging parameters remain identical regardless of the reconstruction algorithm, this improved image quality does not come at the expense of additional patient dose or an increased scan acquisition time for otherwise identical parameters. These improvements are expected to enhance fidelity in IGRT and ART implementation.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>This paper presents a method to solve a linear regression problem subject to group lasso and ridge penalisation when the model has a Kronecker structure. This model was developed to solve the inverse problem of electrocardiography using sparse signal representation over a redundant dictionary or frame. The optimisation algorithm was performed using the block coordinate descent and proximal gradient descent methods. The explicit computation of the underlying Kronecker structure in the regression was avoided, reducing space and temporal complexity. We developed an algorithm that supports the use of arbitrary dictionaries to obtain solutions and allows a flexible group distribution.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Increased life expectancy has led to an increase in the use of bone substitutes in numerous nations, with over two million bone-grafting surgeries performed worldwide each year. A bone defect can be caused by trauma, infections, and tissue resections which can self-heal due to the osteoconductive nature of the native extracellular matrix components. However, natural self-healing is time-consuming, and new bone regeneration is slow, especially for large bone defects. It also remains a clinical challenge for surgeons to have a suitable bone substitute. To date, there are numerous potential treatments for bone grafting, including gold-standard autografts, allograft implantation, xenografts, or bone graft substitutes. Tricalcium phosphate (TCP) and hydroxyapatite (HA) are the most extensively used and studied bone substitutes due to their similar chemical composition to bone. The scaffolds should be tested <jats:italic>in vivo</jats:italic> and <jats:italic>in vitro </jats:italic>using suitable animal models to ensure that the biomaterials work effectively as implants. Hence, this article aims to familiarize readers with the most frequently used animal models for biomaterials testing and highlight the available literature for in vivo studies using small and large animal models. This review summarizes the bio ceramic materials, particularly HA and β-TCP scaffolds, for bone defects in small and large animal models. Besides, the design considerations for the pre-clinical animal model selection for bone defect implants are emphasized and presented.</jats:p>