345 research outputs found

    Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers

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    For Head and Neck Cancers (HNC) patient management, automatic gross tumor volume (GTV) segmentation and accurate pre-treatment cancer recurrence prediction are of great importance to assist physicians in designing personalized management plans, which have the potential to improve the treatment outcome and quality of life for HNC patients. In this paper, we developed an automated primary tumor (GTVp) and lymph nodes (GTVn) segmentation method based on combined pre-treatment positron emission tomography/computed tomography (PET/CT) scans of HNC patients. We extracted radiomics features from the segmented tumor volume and constructed a multi-modality tumor recurrence-free survival (RFS) prediction model, which fused the prediction results from separate CT radiomics, PET radiomics, and clinical models. We performed 5-fold cross-validation to train and evaluate our methods on the MICCAI 2022 HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR) dataset. The ensemble prediction results on the testing cohort achieved Dice scores of 0.77 and 0.73 for GTVp and GTVn segmentation, respectively, and a C-index value of 0.67 for RFS prediction. The code is publicly available (https://github.com/wangkaiwan/HECKTOR-2022-AIRT). Our team's name is AIRT.Comment: MICCAI 2022, HECKTOR Challenge Submissio

    Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning

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    Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace. This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation. By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 95%). Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety

    Correlated Mutation in the Evolution of Catalysis in Uracil DNA Glycosylase Superfamily

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    Enzymes in Uracil DNA glycosylase (UDG) superfamily are essential for the removal of uracil. Family 4 UDGa is a robust uracil DNA glycosylase that only acts on double-stranded and single-stranded uracil-containing DNA. Based on mutational, kinetic and modeling analyses, a catalytic mechanism involving leaving group stabilization by H155 in motif 2 and water coordination by N89 in motif 3 is proposed. Mutual Information analysis identifies a complexed correlated mutation network including a strong correlation in the EG doublet in motif 1 of family 4 UDGa and in the QD doublet in motif 1 of family 1 UNG. Conversion of EG doublet in family 4 Thermus thermophilus UDGa to QD doublet increases the catalytic efficiency by over one hundred-fold and seventeen-fold over the E41Q and G42D single mutation, respectively, rectifying the strong correlation in the doublet. Molecular dynamics simulations suggest that the correlated mutations in the doublet in motif 1 position the catalytic H155 in motif 2 to stabilize the leaving uracilate anion. The integrated approach has important implications in studying enzyme evolution and protein structure and function

    Concurrent probing of electron-lattice dephasing induced by photoexcitation in 1T-TaSeTe using ultrafast electron diffraction

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    It has been technically challenging to concurrently probe the electrons and the lattices in materials during non-equilibrium processes, allowing their correlations to be determined. Here, in a single set of ultrafast electron diffraction patterns taken on the charge-density-wave (CDW) material 1T-TaSeTe, we discover a temporal shift in the diffraction intensity measurements as a function of scattering angle. With the help of dynamic models and theoretical calculations, we show that the ultrafast electrons probe both the valence-electron and lattice dynamic processes, resulting in the temporal shift measurements. Our results demonstrate unambiguously that the CDW is not merely a result of the periodic lattice deformation ever-present in 1T-TaSeTe but has significant electronic origin. This method demonstrates a novel approach for studying many quantum effects that arise from electron-lattice dephasing in molecules and crystals for next-generation devices.Comment: 13 pages and 4 figures in main tex
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