№ lp_2_3_28179
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Supplementary scientific tables and figures presenting statistical methods, radiomics texture features, reproducibility analysis, and comparative results of CT-based ground-glass nodules classified as benign or adenocarcinoma.
Document type:
Supplementary material
Subject:
CT radiomics analysis of ground-glass nodules (GGNs)
Imaging modality:
Computed tomography (CT)
Slice thickness:
3 mm; 1 mm
Study groups:
Benign GGNs (n = 23); Malignant GGNs (n = 92); Adenocarcinoma group (n = 92; n = 54 for 1 mm CT)
Statistical methods:
Propensity score matching; Logistic regression model; LASSO algorithm; 10-fold cross-validation; ROC analysis; Intraclass correlation coefficient (ICC)
Matching ratio:
1:4
C-statistic:
0.7261
Matching method:
Greed matching within specified caliper distances
Distance metric:
0.7
Use of replacement:
With replacement
Feature categories:
Conventional indices; First order features; Second order features (GLCM, GLRLM, NGLDM, GLZLM)
Outcome measures:
AUC value; Rad-score; P-values for texture feature comparison
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The product description is provided for reference. Actual content and formatting may differ slightly.
Year:
2011
Region / City:
London, United Kingdom
Subject:
Gambling regulation and gambling advertising
Document Type:
Research report
Institution:
Responsible Gambling Fund
Authors:
Simon Planzer; Heather Wardle
Field:
Gambling policy and public health
Geographic Scope:
Europe and Great Britain
Research Focus:
Comparative effectiveness of regulatory approaches and the impact of gambling advertising on problem gambling propensity
Policy Context:
Gambling Act 2005 and gambling advertising regulation
Target Audience:
Policy makers, regulators, researchers, and legal experts
Publication Date:
October 2011
Year:
2023
Region / city:
Not specified
Topic:
Medical research, Neoadjuvant therapy
Document type:
Data table
Organization / institution:
Not specified
Author:
Not specified
Target audience:
Medical professionals, researchers
Period of validity:
Not specified
Approval date:
Not specified
Modification date:
Not specified
Journal:
Revista SODEBRAS
Country:
Brazil
Region / City:
Alfenas, Minas Gerais
Subject:
Burnout Syndrome in high school students
Document Type:
Scientific article
Research Type:
Cross-sectional analytical study with quantitative approach
Population:
High school students (1st, 2nd and 3rd year)
Sample Size:
100 students
Institutions Involved:
One public school and one private school
Data Collection Period:
2022
Assessment Instrument:
Maslach Burnout Inventory – Student Survey (MBI-SS)
Variables Analyzed:
emotional exhaustion, disbelief, student efficacy, gender, age, leisure activities, sleep quality, school type, grade
Statistical Methods:
Chi-square test, Kruskal-Wallis test, Dunn multiple comparison test, Principal Component Analysis (PCA)
Software Used:
R v.4.3.0
Keywords:
young people, modern diseases, quality of sleep, stress
Year:
2013
Region / City:
United States
Subject:
Law, Evidence
Document Type:
Academic Text
Organization / Institution:
The John Marshall Law School, CALI eLangdell Press
Author:
Colin Miller
Target Audience:
Legal educators, law students, legal practitioners
Period of validity:
N/A
Date of approval:
N/A
Date of changes:
N/A
Year:
2020
Region / City:
Not specified
Topic:
Deep learning, convolutional neural networks, survival analysis, radiomics
Document type:
Scientific article
Institution / Organization:
Not specified
Author:
He Kaiming, Sanghyun Woo, Zwanenburg A et al.
Target audience:
Researchers in machine learning, medical imaging, and survival analysis
Period of validity:
Not specified
Approval date:
Not specified
Date of changes:
Not specified
Document type:
Supplementary materials
Subject:
Radiomics feature extraction and machine learning model evaluation
Imaging modality:
Computed tomography (CT)
Preprocessing methods:
Intensity normalization, gray-level discretization, Gaussian transform, wavelet transform, voxel resampling
Voxel size:
1×1×1 mm³
Feature selection method:
mRMR algorithm with 1000-fold bootstrap resampling
Dimensionality reduction:
LASSO regression analysis
Model interpretation:
SHAP analysis
Machine learning algorithms:
LR, SVM, RF, ExtraTrees, LightGBM, MLP
Evaluation metrics:
AUC, ACC, SEN, SPE, PPV, NPV
Validation cohorts:
Training set, internal validation, external validation
Variables included:
Intra-radiomics features, Peri-radiomics features, Intra-Peri-radiomics features, body composition indices (VFI, SFI, SMI, IMFI, SMD, VSR, VMR)
Tables:
S1–S4
Figures:
S1–S11
Pagination:
Pages 2–14
Year:
2026
Region:
Multicenter
Subject:
Medical imaging, radiomics
Document type:
Supplementary table
Institution:
Imaging Biomarker Standardization Initiative (IBSI)
Software used:
ITK-SNAP 4.0.1, nnU-Net v2, Python 3.9, Pyradiomics 3.1.0
Imaging modality:
Computed tomography (CT)
Patient preparation:
Fasting state
Contrast agent:
Iodinated
Segmentation method:
Manual modification and automatic segmentation
Radiomics features:
First Order Statistics, shape features, GLCM, GLDM, GLRLM, GLSZM, NGTDM
Data format:
DICOM converted to NIfTI
ROI delineation:
Liver and spleen
Experts involved:
2 radiologists with cross-validation by 1 senior radiologist
Acquisition phases:
Arterial, venous, delayed
Year:
2026
Region:
International (data from China and Western medical centers)
Subject:
Lung adenocarcinoma, radiomics, medical imaging, PET/CT
Document type:
Research supplementary material
Institution:
Philips Healthcare, Siemens Healthineers, GE Medical Systems
Authors:
Not explicitly listed in the text
Patient cohort:
Clinical IA stage lung adenocarcinoma patients
Imaging modalities:
CT, PET/CT
Radiomics features extracted:
1,709 features including shape, first-order, GLCM, GLRLM, GLSZM, GLDM
Model types:
CT-signs model, CT/PET radiomics models, Hybrid models with late and early fusion
Outcome measures:
Invasive adenocarcinoma vs AIS/MIA, high-risk vs low-risk histopathology, EGFR mutation prediction
Data processing tools:
SimpleITK, Python, Pyradiomics, H2O.ai auto-ML
Voxel size for resampling:
2×2×2 mm
Date of examination:
Not specified
Blood glucose threshold:
<6.6 mmol/L
Radiopharmaceutical:
[18F]FDG, purity >95%
Scan parameters:
CT 120 kV/80 mA, PET 3 min per bed, voxel sizes and matrix dimensions specified
Year:
2021-2023
Country:
China
Region / City:
Changsha, Hunan
Topic:
Hepatocellular carcinoma, microvascular invasion prediction
Document type:
Research article
Institution:
Hunan Provincial People’s Hospital and The First Affiliated Hospital of Hunan Normal University
Authors:
Chuanlin Yu, QianBiao Gu, Peng Liu, YaQiong He
Corresponding author:
YaQiong He
Methodology:
Retrospective analysis, dual-energy CT, radiomics, logistic regression
Sample size:
145 patients
Cohorts:
Training cohort 101 cases, validation cohort 44 cases
Clinical features analyzed:
Gender, tumor size, laboratory tests
Imaging features analyzed:
DECT quantitative parameters, portal venous phase, virtual monoenergetic images
Outcome measures:
Microvascular invasion (MVI) prediction
Key findings:
Combined clinical-radiomics model shows highest predictive performance for MVI
Application:
Preoperative prediction in hepatocellular carcinoma patients
Type of source:
Scientific research article
Year:
2021
Region / City:
Guangzhou and Chengdu, China
Topic:
Hepatocellular carcinoma, transarterial chemoembolization, radiomics
Document Type:
Original research article
Institution:
Nanfang Hospital, Southern Medical University; Affiliated Hospital of Chengdu University
Authors:
Xiang-Ke Niu, Xiao-Feng He
Target Audience:
Medical researchers, interventional radiologists, oncologists
Study Period:
March 2009 – March 2016
Received Date:
November 2, 2020
Revised Date:
December 7, 2020
Accepted Date:
December 16, 2020
Published Online:
January 14, 2021
Funding:
Health and Family Planning Commission of Sichuan Province, China, No. 17PJ430 and No. 18PJ150
Methodology:
Retrospective study, CT-based radiomics analysis, nomogram construction, validation with external cohort
Sample Size:
Training dataset n = 137; Validation dataset n = 81
Key Findings:
CT-based radiomics nomogram predicts TACE refractoriness and stratifies patients into high- and low-risk groups with different survival outcomes
Keywords:
Hepatocellular carcinoma, Transarterial chemoembolization, Refractoriness, Radiomics, Nomogram, Computed tomography