Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar ...categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic features may reveal distinct pLGG imaging subtypes.
Multi-parametric MR images (T1 pre- and post-contrast, T2, and T2 FLAIR) from 157 patients with pLGGs were collected and 881 quantitative radiomic features were extracted from tumorous region. Clustering was performed using K-means after applying principal component analysis (PCA) for feature dimensionality reduction. Molecular and demographic data was obtained from the PedCBioportal and compared between imaging subtypes.
K-means identified three distinct imaging-based subtypes. Subtypes differed in mutational frequencies of BRAF (p < 0.05) as well as the gene expression of BRAF (p<0.05). It was also found that age (p < 0.05), tumor location (p < 0.01), and tumor histology (p < 0.0001) differed significantly between the imaging subtypes.
In this exploratory work, it was found that clustering of pLGGs based on radiomic features identifies distinct, imaging-based subtypes that correlate with important molecular markers and demographic details. This finding supports the notion that incorporation of radiomic data could augment our ability to better characterize pLGGs.
Dissolved organic matter released from biochar (biochar-derived DOM, BDOM) could dominate the environmental behavior and fate of trace metals by forming BDOM-metal complexes. Here general, ...heterospectral as well as moving-window (MW) two-dimensional correlation spectroscopy (2DCOS) analyses of synchronous fluorescence and Fourier transform infrared spectra were employed to explore the heterogeneous binding characteristics between sludge BDOM and Cu(II). The results revealed that Cu-BDOM binding first occurred in the fulvic-like (368–380 nm), then humic-like (428 nm) fluorescent fractions, followed by infrared groups of phenolic hydroxyl groups, carboxylate, COH of polysaccharide groups, CC of aromatic carbon, CH of aliphatics and COC of aliphatic ethers. The binding affinity of the hydrophilic groups was stronger than that of hydrophobic groups in BDOM towards Cu(II). Fluorescence components in BDOM played a decisive role in the binding of Cu(II) with trace concentration (1 μM), while infrared functional groups made a substantial contribution in the complexation of Cu(II) with higher concentration (10–100 μM). The concentration of final configuration transformation point (11.7 μmol/mg in this study) by MW2DCOS analysis was suggested as an actual binding threshold that was helpful for evaluating their environmental behaviors.
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•Hydrophilic sites were binded towards Cu(II) prior to hydrophobic sites.•Oxygen-containing functional groups preferred to bind with Cu(II) than aromatics.•Fluorescence response was prior to vibration changes of infrared functional groups.•Final configuration transformation point was suggested as actual binding threshold.•Moving-window 2DCOS provides a new insight in understanding dynamic binding process.
Groundwater pH is one of the most important geochemical parameters in controlling the interfacial reactions of zero-valent iron (ZVI) with water and contaminants. Ball milled, microscale ZVI (mZVIbm) ...efficiently dechlorinated TCE at initial stage (<24 h) at pH 6–7 but got passivated at later stage due to pH rise caused by iron corrosion. At pH > 9, mZVIbm almost completely lost its reactivity. In contrast, ball milled, sulfidated microscale ZVI (S-mZVIbm) didn't experience any reactivity loss during the whole reaction stage across pH 6–10 and could efficiently dechlorinate TCE at pH 10 with a reaction rate of 0.03 h−1. Increasing pH from 6 to 9 also enhanced electron utilization efficiency from 0.95% to 5.3%, and from 3.2% to 22%, for mZVIbm and S-mZVIbm, respectively. SEM images of the reacted particles showed that the corrosion product layer on S-mZVIbm had a puffy/porous structure while that on mZVIbm was dense, which may account for the mitigated passivation of S-mZVIbm under alkaline pHs. Density functional theory calculations show that covered S atoms on the Fe(100) surface weaken the interactions of H2O molecules with Fe surfaces, which renders the sulfidated Fe surface inefficient for H2O dissociation and resistant to surface passivation. The observation from this study provides important implication that natural sulfidation of ZVI may largely contribute to the long-term (>10 years) efficiency of TCE decontamination by permeable reactive barriers with pore water pH above 9.
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•Sulfidated ZVI effectively dechlorinated TCE at alkaline pHs but ZVI couldn't.•Increasing pH enhanced electron efficiency of both particles.•Sulfidation produced a puffy structure of corrosion products to mitigate passivation.•S atoms weaken the interaction of H2O with Fe surface to cause the puffy structure.
Abstract BACKGROUND Skull-stripping, the process of extracting brain tissue from MR images, is an important step for tumor segmentation and downstream imaging-based analytics such as AI-powered ...radiomic feature extraction. Existing skull-stripping models, designed for pediatric or adult patients, show limitations in accurately segmenting tumors in sellar/suprasellar regions. This limitation hinders their reliable application across different histologies of pediatric brain tumors. We propose a deep learning approach for fully automated skull-stripping, compatible with both single- or multi-parametric MRI sequences. METHODS We developed 3D nnU-Net models trained on preprocessed MRI sequences (including pre- and post-contrast T1w, T2w, and FLAIR) from 336 patients with brain tumors across multiple tumor histologies such as low-grade, high-grade and brainstem gliomas, medulloblastoma, ependymoma, etc., aged between 3 months and 20 years (median age, 8.5 years). The training utilized manually generated brain masks, including the sellar/suprasellar region, from 153 patients and employed 5-fold cross-validation to split the data into inner training-validation sets. The models were then tested on a withheld set of 183 subjects. Additionally, we trained a single-parametric model on individual images, resulting in 612 training and 732 testing cases. Model performance was evaluated using the Dice similarity metric for segmenting both the entire brain and slices specifically containing the sella turcica. RESULTS The multi-parametric and single-parametric models achieved mean±sd Dice scores of 0.981±0.008 (median=0.983) and 0.979±0.009 (median=0.981), respectively. For the sellar/suprasellar slices, the scores were 0.983±0.009 (median=0.986) and 0.981±0.012 (median=0.984), respectively. These results indicate a high precision in segmenting not only the entire brain volume, but also the sellar/suprasellar region. CONCLUSION Our proposed deep learning-based skull-stripping approach, leveraging both multi-parametric and single-parametric MRI inputs, demonstrates excellent accuracy. These models, made publicly available, have potential for improving auto-processing pipelines in pediatric brain tumors.
Abstract
Characterization of pediatric low-grade glioma (pLGG) remains a significant challenge in the field of neuro-oncology, with a need for more effective precision diagnostics. Multi-modal ...analysis, which incorporates data from varied sources, has the potential to provide a comprehensive understanding of the underlying biology of pediatric brain tumors. Despite its potential, the utilization of true multi-modality clustering remains limited in pediatric brain tumor research. In this study, we aimed to address this gap by using a clustering model that incorporated genomics, radiomics, and clinical variables (age, sex, and tumor location) to group patients into distinct clusters. 103 patients with pLGGs were included. Mutations data was derived from whole genome sequencing obtained through the PedCBioportal. Radiomic data was obtained from MR imaging through the Children’s Brain Tumor Network and included features from pre- and post-contrast T1, T2, FLAIR, and ADC sequences. Categorical variables included sex (male vs female), genetic mutation status for 10 selected genes (BRAF, FGFR1, TSC1, TSC2, NF1, MYB, EGFR, ALK, IDH1, and RB1) that are known to play a role in the pathogenesis of pLGG (mutated vs non-mutated), and tumor location (9 categories). Continuous variables included age (days) and radiomic features. All numerical variables were normalized and reduced into principal components that captured 90% of the variance in the data prior to clustering. Various clustering iterations were performed incorporating combinations of radiomic, genomic, and clinical data. Our models identified two distinct clusters and the PFS differences between clusters approached statistical significance with the integration of information from all modalities when compared to any combination of subsets of data, highlighting the complementary value of these modalities in providing a comprehensive characterization of pLGG. This study provides preliminary evidence for the utility of multi-modality data clustering in improving our understanding of pLGG and supports further investigation into this approach.
Abstract
Background
Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment ...planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans.
Methods
Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients (n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training (n = 151), validation (n = 43), and withheld internal test (n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts.
Results
Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets.
Conclusions
Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.
By ESR spin elimination and photocleavage assay, the mechanisms of one-electron oxidation damage of oligonucleotides by excited triplet state of riboflavin （Rb） have been elucidated. The results ...demonstrate that Rb, an endogenous photosensitizer, is capable of cleav- ing single-stranded telomeric overhang and the template region of telomerase RNA under UVA irradiation, resulting in blocking of reverse transcription of telomeric DNA which leads to the apoptosis of cancer cells ultimately. ~ 2007 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.
Brain tumors are the most common solid tumors and the leading cause of cancer-related death among children. Tumor segmentation is essential in surgical and treatment planning, and response assessment ...and monitoring. However, manual segmentation is time-consuming and has high inter-operator variability, underscoring the need for more efficient methods. We compared two deep learning-based 3D segmentation models, DeepMedic and nnU-Net, after training with pediatric-specific multi-institutional brain tumor data using based on multi-parametric MRI scans.Multi-parametric preoperative MRI scans of 339 pediatric patients (n=293 internal and n=46 external cohorts) with a variety of tumor subtypes, were preprocessed and manually segmented into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). After training, performance of the two models on internal and external test sets was evaluated using Dice scores, sensitivity, and Hausdorff distance with reference to ground truth manual segmentations. Dice score for nnU-Net internal test sets was (mean +/- SD (median)) 0.9+/-0.07 (0.94) for WT, 0.77+/-0.29 for ET, 0.66+/-0.32 for NET, 0.71+/-0.33 for CC, and 0.71+/-0.40 for ED, respectively. For DeepMedic the Dice scores were 0.82+/-0.16 for WT, 0.66+/-0.32 for ET, 0.48+/-0.27, for NET, 0.48+/-0.36 for CC, and 0.19+/-0.33 for ED, respectively. Dice scores were significantly higher for nnU-Net (p<=0.01). External validation of the trained nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high generalization capability in segmentation of whole tumor and tumor core with Dice scores of 0.87+/-0.13 (0.91) and 0.83+/-0.18 (0.89), respectively. Pediatric-specific data trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.
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