Browsing by Author "Fuentes, David"

Now showing 1 - 6 of 6
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    A molecular dynamics approach towards evaluating osmotic and thermal stress in the extracellular environment
    (Taylor & Francis, 2018) Fuentes, David; Muñoz, Nina M.; Guo, Chunxiao; Polak, Urzsula; Minhaj, Adeeb A.; Allen, William J.; Gustin, Michael C.; Cressman, Erik N.K.
    OBJECTIVE: A molecular dynamics approach to understanding fundamental mechanisms of combined thermal and osmotic stress induced by thermochemical ablation (TCA) is presented. METHODS: Structural models of fibronectin and fibronectin bound to its integrin receptor provide idealized models for the effects of thermal and osmotic stress in the extracellular matrix. Fibronectin binding to integrin is known to facilitate cell survival. The extracellular environment produced by TCA at the lesion boundary was modelled at 37 °C and 43 °C with added sodium chloride (NaCl) concentrations (0, 40, 80, 160, and 320 mM). Atomistic simulations of solvated proteins were performed using the GROMOS96 force field and TIP3P water model. Computational results were compared with the results of viability studies of human hepatocellular carcinoma (HCC) cell lines HepG2 and Hep3B under matching thermal and osmotic experimental conditions. RESULTS: Cell viability was inversely correlated with hyperthermal and hyperosmotic stresses. Added NaCl concentrations were correlated with a root mean square fluctuation increase of the fibronectin arginylglycylaspartic acid (RGD) binding domain. Computed interaction coefficients demonstrate preferential hydration of the protein model and are correlated with salt-induced strengthening of hydrophobic interactions. Under the combined hyperthermal and hyperosmotic stress conditions (43 °C and 320 mM added NaCl), the free energy change required for fibronectin binding to integrin was less favorable than that for binding under control conditions (37 °C and 0 mM added NaCl). CONCLUSION: Results quantify multiple measures of structural changes as a function of temperature increase and addition of NaCl to the solution. Correlations between cell viability and stability measures suggest that protein aggregates, non-functional proteins, and less favorable cell attachment conditions have a role in TCA-induced cell stress.
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    Artificial intelligence uncertainty quantification in radiotherapy applications − A scoping review
    (Elsevier, 2024) Wahid, Kareem A.; Kaffey, Zaphanlene Y.; Farris, David P.; Humbert-Vidan, Laia; Moreno, Amy C.; Rasmussen, Mathis; Ren, Jintao; Naser, Mohamed A.; Netherton, Tucker J.; Korreman, Stine; Balakrishnan, Guha; Fuller, Clifton D.; Fuentes, David; Dohopolski, Michael J.
    Background/purpose The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions. Methods We followed the PRISMA-ScR scoping review reporting guidelines. We utilized the population (human cancer patients), concept (utilization of AI UQ), context (radiotherapy applications) framework to structure our search and screening process. We conducted a systematic search spanning seven databases, supplemented by manual curation, up to January 2024. Our search yielded a total of 8980 articles for initial review. Manuscript screening and data extraction was performed in Covidence. Data extraction categories included general study characteristics, RT characteristics, AI characteristics, and UQ characteristics. Results We identified 56 articles published from 2015 to 2024. 10 domains of RT applications were represented; most studies evaluated auto-contouring (50 %), followed by image-synthesis (13 %), and multiple applications simultaneously (11 %). 12 disease sites were represented, with head and neck cancer being the most common disease site independent of application space (32 %). Imaging data was used in 91 % of studies, while only 13 % incorporated RT dose information. Most studies focused on failure detection as the main application of UQ (60 %), with Monte Carlo dropout being the most commonly implemented UQ method (32 %) followed by ensembling (16 %). 55 % of studies did not share code or datasets. Conclusion Our review revealed a lack of diversity in UQ for RT applications beyond auto-contouring. Moreover, we identified a clear need to study additional UQ methods, such as conformal prediction. Our results may incentivize the development of guidelines for reporting and implementation of UQ in RT.
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    Coupled Flow and Transport in an Organ and its Vasculature
    (2024-08-08) Tzolova, Bilyana; Riviere, Beatrice; Fuentes, David
    In contrast to many other types of cancer, the incidence of liver cancer, specifically hepatocellular carcinoma (HCC), is on the rise. For most patients, surgical intervention is not a viable option, leaving them reliant on chemotherapy treatments, particularly transarterial chemoembolization (TACE), for relief. Our study aims to understand how these treatments function within the liver and their impact on tumor growth. Building upon existing research, we model the flow and transport of chemotherapy drugs and embolic agents in the liver using the miscible displacement equations. Utilizing CT images from liver cancer patients, we extract a 1D centerline of the hepatic vascular structures that deliver blood to the tumors, and then construct a 3D mesh from the liver segmentations. We employ the singularity subtraction technique to create a finite element model for the flow of blood in the liver, specifically focusing on areas affected by the TACE treatment. We extend the singularity subtraction technique to the time-dependent advection-diffusion equation to model the concentration of chemotherapy drugs in the liver and tumors. We first solve the time-dependent non-conservative advection-diffusion equation using the finite element method. To address instabilities arising when the model is advection dominated, we then utilize the discontinuous Galerkin method to solve the time-dependent conservative advection-diffusion equation. We couple the models for blood flow following the injection of an embolic agent with the transport of chemotherapy to develop a comprehensive model based on the miscible displacement equations in the liver. We then apply the simulation to data from MD Anderson patients diagnosed with hepatocellular carcinoma who have undergone transarterial chemoembolization treatment. This final model enables us to provide insights into the evolving dynamics of TACE within the liver.
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    Interactive Brain Tumor Segmentation
    (2023-06-08) Balsells, Cito; Riviere, Beatrice; Fuentes, David
    Machine learning based image segmentation relies on having access to a large dataset of labeled scans. Challenges arise when a sufficient training dataset is not available. To build a labeled dataset, one can manually create segmentations either by hand or assisted by a semi-automatic interactive tool. Here, interaction is given by the user in form of foreground and background clicks on the image. This thesis evaluates semi-automatic interactive image segmentation models applied to 3D brain tumor segmentation from MRI scans under two conditions. The first condition involves training models on the entire dataset. This provides a baseline for the best expected performance for each model. The second condition involves training models on portions of the dataset. This is done in an effort to model a dataset being gradually created from a small dataset. In both conditions, we find that the Wilcoxon signed rank test indicates significant results when comparing some of our interactive models with their fully-automatic counterpart. However, we ultimately deem the difference to be clinically irrelevant.
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    Machine Learning Methods for Vessel Segmentation in Organs
    (2022-04-19) Tzolova, Bilyana; Riviere, Beatrice; Fuentes, David
    The vascular system plays a crucial role in diagnostics, treatment, and surgical planning in a wide array of diseases. Recently, there has been a growing interest in automating the manual vessel segmentation process to save time. We aim to efficiently and effectively segment the vascular system in the liver organ using deep learning techniques in order to improve on current manual methods. We propose a 3D DenseNet using PocketNet paradigm with binary and ternary classifications that has less parameters to train than the state of the art methods. We explore the impact of various preprocessing techniques on the accuracy of the neural network. We are able to reduce training times and increase accuracy per training parameter in medical imaging segmentation of the liver vessels. Finally, we assess the accuracy of our model predictions using the dice score coefficient. We find that successful preprocessing filters and neural network parameters are necessary for consistently high dice scores.
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    PocketNet: A Smaller Neural Network For Medical Image Analysis
    (2023-04-24) Celaya, Adrian; Riviere, Beatrice; Fuentes, David
    Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNet paradigm to reduce the size of deep learning models by throttling the growth of the number of channels in convolutional neural networks. We demonstrate that, for a range of segmentation and classification tasks, PocketNet architectures produce results comparable to that of conventional neural networks while reducing the number of parameters by multiple orders of magnitude, using up to 90% less GPU memory, and speeding up training times by up to 40%, thereby allowing such models to be trained and deployed in resource-constrained settings.