№ files_lp_4_process_3_107058
File format: docx
Character count: 2107
File size: 13 KB
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
Price: 8 / 10 USD
The file will be delivered to the email address provided at checkout within 12 hours.
The file will be delivered to the email address provided at checkout within 12 hours.
Don’t have cryptocurrency yet?
You can still complete your purchase in a few minutes:- Buy Crypto in a trusted app (Coinbase, Kraken, Cash App or any similar service).
- In the app, tap Send.
- Select network, paste our wallet address.
- Send the exact amount shown above.
The final amount may vary slightly depending on the payment method.
The file will be sent to the email address provided at checkout within 24 hours.
The product description is provided for reference. Actual content and formatting may differ slightly.
Note:
Year
Context:
Application form for connecting power generating modules to the electricity distribution network, specifying conditions and requirements for multiple customers sharing power output.
Year:
2026
Region / City:
Chidambaram, Tamil Nadu, India
Subject:
Photovoltaic energy systems, intelligent control, battery management
Document Type:
Research paper
Institution:
Annamalai University, Department of EEE
Authors:
K. Vinoth Bresnav, Dr. S. Singaravelu
Target Audience:
Electrical engineering researchers and practitioners
Methodology:
ANN-based MPPT, bi-directional DC-DC converter, three-level NPC inverter, PI control
Simulation Environment:
MATLAB/Simulink
Key Components:
Standalone PV system, lithium-ion battery, DC link, three-phase AC load
Applications:
Off-grid renewable energy systems, dynamic load management
Performance Metrics:
MPPT efficiency, battery management effectiveness, AC output stability
Keywords:
ANN, MPPT, PV system, Bi-Directional Converter, NPC Inverter, DC Link, PI Controller
Year:
2023
Region / city:
Global
Topic:
Aquatic animal health
Document type:
Assessment report
Organization / institution:
World Organisation for Animal Health (WOAH)
Author:
Aquatic Animal Health Standards Commission
Target audience:
Veterinary and aquatic health professionals, policy makers
Period of validity:
Not specified
Approval date:
Not specified
Date of amendments:
Not specified
Year:
2021
Region:
International
Topic:
Traditional Chinese Medicine, Spleen QI Deficiency
Document Type:
Academic Paper
Institution:
National Center for Complementary and Integrative Health
Author:
Student (name not specified)
Target Audience:
Students and researchers in complementary and integrative medicine
Period Covered:
Historical overview and current therapeutic approaches
Therapies Discussed:
Dietary therapy, Chinese herbs, Pharmaceutical treatment, Pulmonary and circulatory health management, Mental therapy
Symptoms Detailed:
Fatigue, bloating, acid reflux, diarrhea, nausea, dehydration, weak pulse, cold limbs
References:
Medicalnewstoday (2021), NCCIH (2021), Shushe & Zishen (2017), Zhou et al. (2019)
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
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
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