№ lp_1_2_58472
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A detailed procedural and pathologic guide for the examination of gastrointestinal stromal tumors (GISTs) in the stomach.
Year:
2005
Region / City:
Philadelphia, PA
Topic:
Gastrointestinal Stromal Tumor (GIST), Pathology
Document Type:
Medical Procedural Guide
Organization / Institution:
Elsevier Saunders, Elsevier Churchill Livingstone
Author:
Kumar V, Abbas A, Fausto N, Lester SC
Target Audience:
Medical professionals, Pathologists
Period of Validity:
Not specified
Approval Date:
Not specified
Date of Changes:
Not specified
Price: 8 / 10 USD
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The product description is provided for reference. Actual content and formatting may differ slightly.
Year:
2022
Region / City:
Not specified
Topic:
Cancer research, genomics, histology
Document Type:
Supplementary information
Organization / Institution:
The Jackson Laboratory for Genomic Medicine, Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), and others
Author:
Brian S White, Xing Yi Woo, Soner Koc, Todd Sheridan, Steven B Neuhauser, et al.
Target Audience:
Researchers, clinicians, and institutions in genomics and cancer research
Period of validity:
Not specified
Approval Date:
Not specified
Date of Changes:
Not specified
Contextual description:
Supplementary information for a genomic and histological image repository enabling deep learning analysis in cancer research.
Year:
2023
Region / city:
Wuhan, China
Topic:
Medical Imaging, Artificial Intelligence, Gastrointestinal Stromal Tumors
Document Type:
Research Article
Organization / Institution:
Renmin Hospital of Wuhan University
Authors:
Chenxia Zhang, Wei Tan, Xun Li, Xiao Tao, Bing Xiao, Wei Zhou, Honggang Yu
Target Audience:
Medical professionals, researchers in gastroenterology and artificial intelligence
Period of Application:
Not specified
Date of Approval:
Not specified
Date of Changes:
Not specified
Keywords:
Gastrointestinal Stromal Tumors, Endoscopic Ultrasound, Artificial Intelligence, Risk Stratification, Deep Learning
Methodology:
Image Classification, Image Segmentation, Deep Convolutional Neural Network (DCNN), ResNet, Unet++
Evaluation Metrics:
Accuracy, Intersection over Union (IoU), Precision, Recall, F1 score
Conclusion:
The use of AI-based risk stratification and image analysis improves GIST diagnosis accuracy
References:
Li J, Zhu Y, Dong Z, et al., Yao L, Zhang J, Liu J, et al., Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J
Year:
2021
Organisation:
International Collaboration on Cancer Reporting
Document type:
Histopathology reporting guide
Subject:
Gastrointestinal stromal tumour (GIST)
Scope:
Pathology reporting of resection specimens for GIST
Exclusions:
Metastatic GIST specimens
Edition:
1st edition
Applicable staging system:
Pathological TNM staging
Evidence framework:
NHMRC levels of evidence
Intended users:
Pathologists reporting GIST resection specimens
Related publications:
Gastrointestinal Stromal Tumour (GIST) Histopathology Reporting Guide – Biopsy Specimens; Soft Tissue Sarcoma Histopathology Reporting Guide – Resection Specimens
Year:
2026
Region / city:
Not specified
Subject:
Angiogenesis, wound healing, tumor stroma
Document type:
Supplemental scientific figures and tables
Institution / organization:
Not specified
Author:
Not specified
Model organism:
Mouse
Experimental intervention:
Ad-VEGF-A164 injection, anti-VEGFR2 (DC101) and anti-VEGF (G6) treatment
Sample size:
10 ears per treatment per time point (unless otherwise noted)
Data sources:
The Cancer Genome Atlas (TCGA), Lei et al Singapore cohort, ACRG gastric cohort
Analysis methods:
Differential gene expression, hypergeometric enrichment, heatmap clustering, RPPA protein profiling, Kaplan-Meier survival analysis
Gene signatures:
Signature 1, Signature 2, Signature 3
Key measurements:
mRNA expression of VE-Cadherin (CDH5), smooth muscle actin (Acta2), protein analytes
Time points:
Day 5, Day 20, Day 60
Histological classification:
Lauren, EMT, MSI, P53N, P53P
Relevant pathways:
VEGF signaling, vascular and stromal development, inflammatory response, endothelial genes
Year:
2022
Region / City:
Gwangju
Theme:
International student support, cultural exchange, education
Document Type:
News article
Organization:
GIST (Gwangju Institute of Science and Technology)
Author:
N/A
Target Audience:
International undergraduate students, prospective students, faculty
Period of Activity:
Four months (September to December)
Approval Date:
December 27, 2022
Date of Changes:
N/A
Year:
2022
Date:
April 26
Time:
1:30 pm
Location:
GIST (Gwangju Institute of Science and Technology) Administration Building, Gwangju
Organizations:
GIST (Gwangju Institute of Science and Technology); Korea Air Industry Promotion Association
Document Type:
Memorandum of Understanding (MoU) / Business Agreement
Signatories:
Kiseon Kim; Bo-gon Kim
Participants:
Young-jip Kim; Chang-Duk Jun; Chang-geum Song; Hwa-shin Lee; In-seong Jang; Kyung-hoon Kwak
Subject:
Development of the air industry and nurturing of professional workforce
Purpose:
Technological innovation, local industry development, job creation, commercialization of public technologies
Scope of Cooperation:
Information exchange; support for local companies; technology guidance and consulting; discovery, analysis, transfer, and education of promising technologies; mutual development and cooperation
Year:
2021
Organisation:
International Collaboration on Cancer Reporting
Document type:
Histopathology reporting guide
Subject:
Gastrointestinal stromal tumour (GIST)
Scope:
Pathology reporting of resection specimens for GIST
Exclusions:
Metastatic GIST specimens
Edition:
1st edition
Applicable staging system:
Pathological TNM staging
Evidence framework:
NHMRC levels of evidence
Intended users:
Pathologists reporting GIST resection specimens
Related publications:
Gastrointestinal Stromal Tumour (GIST) Histopathology Reporting Guide – Biopsy Specimens; Soft Tissue Sarcoma Histopathology Reporting Guide – Resection Specimens
Source section:
Table 3
Disease:
Gastric gastrointestinal stromal tumor (GIST)
Biomarker:
DNM3OS expression
Variables analyzed:
Gender; Age; Tumor size; Mitotic count; NIH risk classification; Mutational status; Prior TKI treatment
Sample size:
251 patients
Low expression group:
n=143
High expression group:
n=108
Mutation analysis subset:
n=130
Statistical methods:
P value comparison; Fisher exact test
Genetic markers:
KIT exon 9/11; PDGFRA exon 18; WT
Clinical parameters:
Tumor size (cm); Mitotic count per 50 HPFs; NIH risk classification categories
Therapeutic variable:
Prior tyrosine kinase inhibitor (TKI) treatment
Field of study:
Oncology; Molecular pathology