№ files_lp_4_process_2_58441
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Supplementary material for a research study examining the use of cycle threshold values in Xpert MTB/RIF® as a potential marker for assessing TB disease severity and its implications for treatment decisions.
Year: 2021
Region / City: N/A
Subject: Tuberculosis, Diagnostic Methods, Treatment Stratification
Document Type: Research Supplement
Institution: N/A
Authors: Daniel J. Grint, Jasvir Dhillon, Philip D. Butcher, Jack Adams, Tulika Munshi, Adam Witney, Kate Gould, Ken Laing, Christopher Cousins, Sean Wasserman, Katherine Fielding, Tom Harrison, Amina Jindani
Target Audience: Healthcare professionals, researchers, clinicians
Period of Effectiveness: N/A
Date of Approval: N/A
Date of Changes: N/A
Price: 8 / 10 USD
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Development of imaging features to refine risk stratification in high-risk neuroblastoma
Note: Year
Contextual description: A research paper providing advanced imaging analysis methods for refining risk assessment in high-risk neuroblastoma patients.
AutoFRS: An Externally Validated, Annotation-Free Approach to Computational Preoperative Complication Risk Stratification in Pancreatic Surgery – Supplementary Material
Year: 2025
Type of document: Supplementary digital content
Topic: Computational preoperative risk stratification in pancreatic surgery
Methods: Radiomics, feature extraction, hierarchical clustering, machine learning models
Patient cohorts: Dresden (Development), Heidelberg (External Test)
Imaging modality: CT
Software/Tools: TotalSegmentator v1.5.6, MIRP v2.0.0
Number of extracted features: 654
Cluster analysis: Hierarchical agglomerative clustering
Comparison: AutoFRS vs traditional FRS risk stratification
Content sections: Patient selection, CT imaging characteristics, Totalsegmentator configuration, MIRP feature extraction, Feature clustering, Radiomics and clinical signatures, Evaluation of models, Web application, Glossary
Supplementary materialRisk Stratification System of Gastrointestinal Stromal Tumors under Endoscopic Ultrasound Elastography Based on Artificial Intelligence
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
Risk Stratification in Febrile Neutropenic Episodes in Adolescent / Young Adult Patients with Cancer
Year: 2023
Region / city: United Kingdom, Canada, Switzerland, The Netherlands, Germany
Subject: Risk Stratification in Medical Research
Document Type: Research Article
Organization / Institution: University of York, Leeds Teaching Hospitals, University of Leicester, The Hospital for Sick Children, University of Bern, University Medical Center Groningen, Johann Wolfgang Goethe University
Author(s): Robert S. Phillips, Kaljit Bhuller, Lillian Sung, Roland A. Ammann, Wim J. E. Tissing, Thomas Lehrnbecher, Lesley A Stewart
Target Audience: Medical Researchers, Oncologists, Pediatricians, Healthcare Professionals
Period of Validity: Not specified
Approval Date: Not specified
Date of Changes: Not specified
Supplementary Digital Content Table 2. Stratification of Age Adjusted-Incidence Rate by Histology
Year: 2020
Region / City: United States
Topic: Oncology, Hematology
Document Type: Research data
Organization / Institution: N/A
Author: N/A
Target Audience: Researchers, medical professionals
Period of Validity: N/A
Approval Date: N/A
Date of Changes: N/A
Topics: materialXpert MTBRIF stratification