The S. A. Repertory, The S. S. A. Repertory, The S. A. B. Repertory, The T. T. B. Repertory

Kenley, Huldah Jane

doi:10.2307/3384627

Cited by 2 publications

(2 citation statements)

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“…MaCRUISE integrates the processes of cortical reconstruction and multi-atlas segmentation to produce reliable and consistent cortical surface parcellations in anatomical agreement with brain segmentations (Huo et al, 2016a(Huo et al, ,b, 2018. In the MaCRUISE pipeline, skull and dura-stripped images are subject to both multi-atlas segmentation of 132 regions (Klein et al, 2010;Landman, 2012, 2013) and TOpology-preserving Anatomical Segmentation (TOADS) fuzzy membership segmentation (Bazin and Pham, 2008). MaCRUISE then fuses the rigid multi-atlas and TOADS segmentations, resulting in a full cerebrum segmentation comprised of a gray matter and white matter component.…”

Section: Image Processingmentioning

confidence: 99%

Sex and age effects on gray matter volume trajectories in young children with prenatal alcohol exposure

Long,

Kar,

Forkert

et al. 2024

Front. Hum. Neurosci.

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Prenatal alcohol exposure (PAE) occurs in ~11% of North American pregnancies and is the most common known cause of neurodevelopmental disabilities such as fetal alcohol spectrum disorder (FASD; ~2–5% prevalence). PAE has been consistently associated with smaller gray matter volumes in children, adolescents, and adults. A small number of longitudinal studies show altered gray matter development trajectories in late childhood/early adolescence, but patterns in early childhood and potential sex differences have not been characterized in young children. Using longitudinal T1-weighted MRI, the present study characterized gray matter volume development in young children with PAE (N = 42, 84 scans, ages 3–8 years) compared to unexposed children (N = 127, 450 scans, ages 2–8.5 years). Overall, we observed altered global and regional gray matter development trajectories in the PAE group, wherein they had attenuated age-related increases and more volume decreases relative to unexposed children. Moreover, we found more pronounced sex differences in children with PAE; females with PAE having the smallest gray matter volumes and the least age-related changes of all groups. This pattern of altered development may indicate reduced brain plasticity and/or accelerated maturation and may underlie the cognitive/behavioral difficulties often experienced by children with PAE. In conjunction with previous research on older children, adolescents, and adults with PAE, our results suggest that gray matter volume differences associated with PAE vary by age and may become more apparent in older children.

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Section: Image Processingmentioning

confidence: 99%

Sex and age effects on gray matter volume trajectories in young children with prenatal alcohol exposure

Long,

Kar,

Forkert

et al. 2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…With the use of Convolutional Neural Network (CNN), histopathology images have proven to be good predictors of malignancy status, important molecular biomarkers of various clinical and research relevance, as well as other cellular and extracellular processes (Mungenast et al, 2021). While many deep learning models have achieved relatively high metrics in detecting tumors within histopathology images of metastatic lymph node samples (Chuang et al, 2021;Wen et al, 2021;Huang et al, 2022), the difficult problem of predicting DM from primary samples remains a challenge, and most of the past attempts on this and similar tasks have struggled with relatively average model performance (Zhao, 2020;Brinker et al, 2021;Kiehl et al, 2021;Schiele et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Multimodal AI for prediction of distant metastasis in carcinoma patients

Olatunji

Cui

2023

Front. Bioinform.

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Metastasis of cancer is directly related to death in almost all cases, however a lot is yet to be understood about this process. Despite advancements in the available radiological investigation techniques, not all cases of Distant Metastasis (DM) are diagnosed at initial clinical presentation. Also, there are currently no standard biomarkers of metastasis. Early, accurate diagnosis of DM is however crucial for clinical decision making, and planning of appropriate management strategies. Previous works have achieved little success in attempts to predict DM from either clinical, genomic, radiology, or histopathology data. In this work we attempt a multimodal approach to predict the presence of DM in cancer patients by combining gene expression data, clinical data and histopathology images. We tested a novel combination of Random Forest (RF) algorithm with an optimization technique for gene selection, and investigated if gene expression pattern in the primary tissues of three cancer types (Bladder Carcinoma, Pancreatic Adenocarcinoma, and Head and Neck Squamous Carcinoma) with DM are similar or different. Gene expression biomarkers of DM identified by our proposed method outperformed Differentially Expressed Genes (DEGs) identified by the DESeq2 software package in the task of predicting presence or absence of DM. Genes involved in DM tend to be more cancer type specific rather than general across all cancers. Our results also indicate that multimodal data is more predictive of metastasis than either of the three unimodal data tested, and genomic data provides the highest contribution by a wide margin. The results re-emphasize the importance for availability of sufficient image data when a weakly supervised training technique is used. Code is made available at: https://github.com/rit-cui-lab/Multimodal-AI-for-Prediction-of-Distant-Metastasis-in-Carcinoma-Patients.

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The S. A. Repertory, The S. S. A. Repertory, The S. A. B. Repertory, The T. T. B. Repertory

Cited by 2 publications

References 0 publications

Sex and age effects on gray matter volume trajectories in young children with prenatal alcohol exposure

Sex and age effects on gray matter volume trajectories in young children with prenatal alcohol exposure

Multimodal AI for prediction of distant metastasis in carcinoma patients

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