№ lp_2_3_28183
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Year
Contextual description:
A research paper providing advanced imaging analysis methods for refining risk assessment in high-risk neuroblastoma patients.
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Qualification:
BTEC International Level 3 Extended Diploma in Performing Arts
Unit G:
G17 Using Development Plans to Refine Skills
Note:
Issue Date
Document Type:
Assignment Brief
Target Audience:
Performing Arts Students
Document Type:
Category 1 submission for Authority Required (Written) listing
Medicine:
Eflornithine (Ifinwil®) 250 mg tablet
Active Ingredient:
Eflornithine (as hydrochloride)
Sponsor:
Norgine Pty Ltd
Regulatory Authority:
Therapeutic Goods Administration (TGA)
Advisory Body:
Pharmaceutical Benefits Advisory Committee (PBAC)
Program:
Section 100 – Highly Specialised Drugs Program
Schedule:
General Schedule (Code GE – Schedule 85)
Indication:
High-risk neuroblastoma in patients aged 1 year and older in remission after prior therapy
Population:
Patients ≥1 year with high-risk neuroblastoma meeting INRG, COG, or SIOPEN criteria
Treatment Phase:
Initial and Continuing Treatment
Comparator:
Standard of care (follow-up and monitoring)
Clinical Outcomes:
Event-free survival, overall survival, safety
Dosage Form:
250 mg oral tablet
Treatment Duration:
Up to 27 cycles (2 years) or until disease progression
Orphan Drug Designation:
Granted 7 December 2022
Submission Pathway:
TGA/PBAC Parallel Process
Restriction Type:
Authority Required (Full assessment – written)
Prescriber Type:
Medical Practitioners (paediatric oncologist or haematologist for initiation)
Type:
Original Report
Initiative:
Joint initiative by SIOPEN, COG and GPOH
Subject:
Standardized reporting of neuroblastoma surgery
Authors:
Lucas E. Matthyssens et al.
Affiliated Organizations:
SIOP Europe International Neuroblastoma Study Group (SIOPEN); Children’s Oncology Group (COG); Gesellschaft fuer Paediatrische Onkologie und Haematologie (GPOH)
Institutions:
Multiple pediatric surgical and oncology centers in Belgium, USA, The Netherlands, United Kingdom, Norway, France, Sweden, Italy, Spain and Germany
Corresponding Author:
Dr Lucas E. Matthyssens
Presentation:
50th Congress of the International Society of Pediatric Oncology (SIOP) and the International Society for Pediatric Surgical Oncology (IPSO), November 16, 2018, Kyoto, Japan
Conflicts of Interest:
None declared
Funding:
No funding received
Keywords:
Neuroblastoma; surgery; biopsy; resection; tumor; standardization; reporting; postoperative complication; outcome; Clavien-Dindo Classification
Scope:
International consensus on structured documentation of neuroblastoma-related surgical procedures and outcomes
Target Field:
Pediatric surgical oncology
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
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
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
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