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Detailed supplementary material for a scientific study presenting imaging acquisition protocols, radiomics feature extraction, and development of machine learning models combining clinical and imaging data for lung adenocarcinoma.
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
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Year:
2023
Region / city:
Dublin, Ireland
Subject:
Medical imaging, PET/CT scan
Document type:
Referral form
Author:
St. James’s PET CT Centre
Target audience:
Referring clinicians
Period of validity:
N/A
Date of approval:
N/A
Date of modification:
N/A
Year:
2026
Study type:
Clinical research
Sample size:
23 patients
Medical focus:
Non-tuberculous mycobacterial pulmonary disease (NTM-PD)
Data type:
Laboratory and imaging results
Parameters analyzed:
White cell count, neutrophils, lymphocytes, red blood cells, hemoglobin, platelets, CRP, ESR, T lymphocytes, IL-6, TNF-α, LDH, PET/CT SUV values
Patient grouping:
Immune deficiency, mildly decreased immunity, normal immunity
Lesion distribution:
Localized, disseminated
Severity assessment:
Severe, non-severe NTM-PD
Measurement techniques:
Blood tests, 18FDG PET/CT imaging
Institution:
Not specified
Context:
Clinical dataset comparing immunological states with imaging and laboratory parameters in NTM-PD patients
Observational period:
Not specified
Year:
2025
Region / City:
Weymouth, Massachusetts, USA
Topic:
Healthcare, Diagnostic Imaging
Document Type:
Presentation and Notice of Intent
Organization / Institution:
Shields Health, South Shore Health
Authors:
Lou Masella, Haley Cammarata, Art Mombourquette
Target Audience:
South Shore Hospital PFAC members, local community
Period of Implementation:
December 2025 and ongoing
Date of Presentation:
December 11, 2025
Date of Notice:
December 18, 2025
Project Value:
$1,099,871
Services:
PET/CT imaging, cancer staging, Alzheimer’s evaluation, cardiac applications
Delivery Method:
Combination of indoor and mobile PET/CT units
Patient Cost:
Outpatient fee schedule
Registration Deadline for Public Comments:
January 22, 2026
Year:
2026-2030
Region / City:
Massachusetts, USA
Subject:
Healthcare, Medical Imaging, PET/CT
Document Type:
Guidance and Forecast Document
Organization:
Department of Public Health, Division of Network (DoN)
Author:
Applicant organization (unspecified)
Target Audience:
DoN staff, healthcare administrators
Submission Deadline:
January 30, 2026
Methodology:
Proprietary forecasting tools via Healthcare Advisory Board, assumptions on population changes, treatment standards, and PET/CT market trends
Operational Staffing:
4 FTE Technologists, 1.5 FTE Tech Aids
Data Collection Focus:
Race and ethnicity demographics
Scan Volume Growth:
FY22-FY25 historical trends, FY26-FY30 projections
Service Availability:
7 days/week
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:
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:
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
Version:
GastricHER2Biomarkers 1.0.0.1
Protocol Posting Date:
June 2017
Year:
2017
Region / city:
United States
Topic:
HER2 biomarker testing, gastric cancer, gastroesophageal junction
Document type:
Guideline
Organization / institution:
College of American Pathologists, American Society for Clinical Pathology, American Society of Clinical Oncology
Author:
Angela N. Bartley, MD; Jessi Christ, CTR; Patrick Fitzgibbons, MD; Stanley R. Hamilton, MD; Sanjay Kakar, MD; Manish A. Shah, MD; Laura H. Tang, MD, PhD; Megan L. Troxell MD, PhD
Target audience:
Pathologists, oncologists, clinical laboratories
Period of validity:
Indefinite
Date of approval:
June 2017
Date of changes:
N/A
Year:
2016
Region / city:
Seoul, Korea
Subject:
Medical research, Oncology
Document type:
Supplementary Methods
Organization / institution:
Seoul National University, Jackson Laboratory for Genomic Medicine, MD Anderson Cancer Center
Authors:
Yun-Suhk Suh, Deukchae Na, Ju-Seog Lee, Jeesoo Chae, EuiHyun Kim, Giyong Jang, Jieun Lee, Jimin Min, Chan-Young Ock, Seong-Ho Kong, Joshy George, Chengsheng Zhang, Hyuk-Joon Lee, Jong-Il Kim, Seong-Jin Kim, Woo Ho Kim, Charles Lee, Han-Kwang Yang
Target audience:
Researchers, medical professionals, academicians
Date of approval:
2016
Date of modifications:
None
Year:
2023
Country:
China
Ethics Approval Number:
2023KYLL014
Scientific Field:
Genetic epidemiology
Research Topic:
Causal relationship between chronic obstructive pulmonary disease and lung adenocarcinoma
Study Design:
Two-sample Mendelian randomization study
Document Type:
Reporting checklist
Exposure:
Chronic obstructive pulmonary disease (COPD)
Outcome:
Lung adenocarcinoma (LUAD)
Data Sources:
GWAS dataset ebi-a-GCST90018807; GWAS dataset ebi-a-GCST004744; TCGA; GEO datasets GSE116959 and GSE76925
Sample Size:
57 LUAD patients, 11 healthy controls, 111 COPD cases, 40 smoking controls
Key Biomarkers:
FCRLA, GREM1, MMP9
Statistical Methods:
Inverse variance weighting (IVW), MR Egger, MR-PRESSO, LASSO regression, false discovery rate correction
Software:
R version 4.2.1; TwoSampleMR package; ggplot2
Diagnostic Criteria:
COPD defined by spirometry (FEV1/FVC < 0.7); LUAD confirmed by histopathology
Genetic Variant Selection Criteria:
Genome-wide significance threshold p < 5 × 10⁻⁸; LD pruning r² < 0.01
Core Mendelian Randomization Assumptions:
Relevance, independence, exclusion restriction
Year:
2024
Region / city:
Global
Subject:
Radiotherapy, Cancer Research
Document type:
Research article
Institution:
Not specified
Author:
Not specified
Target audience:
Researchers, Oncology professionals
Period of validity:
N/A
Approval date:
Not specified
Modification date:
Not specified
Year:
2026
Field:
Oncology, Genomics
Document type:
Research article supplementary material
Authors:
Yanding Zhao, Frederick S. Varn, Guoshuai Cai, Feifei Xiao, Christopher I. Amos, Chao Cheng
Institution:
Not specified
Target audience:
Medical researchers, oncologists, geneticists
Study population:
Patients with early stage lung adenocarcinoma
Data sources:
TCGA LUAD, TCGA LUSC, GSE3141, GSE42127, GSE68465, GSE13213, GSE4573, GSE8894, GSE14814
Methods:
PDS calculation, Mann-Whitney U test, Cox regression, log-rank tests
Variables analyzed:
P53 mutation type, smoking status, overall survival, recurrence risk
Supplementary content:
Figures S1–S5 with statistical analyses
Supplementary purpose:
Correlation of gene signature with recurrence and survival outcomes
Year:
2026
Type of document:
Supplemental Table
Field:
Genetics / Oncology
Source:
Research Study
Authors:
Not specified
Target audience:
Researchers and clinicians in oncology and genetics
Dataset:
Hypothetical gene panels
Number of genes analyzed:
20, 50, 100, 200
Metrics:
Percent of cases with >2 mutations, Median number of mutations per case
Reference:
Supplemental Method 3
Year:
2005–2019
Region / City:
China, multicenter
Topic:
Cervical adenocarcinoma treatment outcomes
Document type:
Retrospective cohort study
Institution:
Nanfang Hospital, Southern Medical University; Shanxi Cancer Hospital, Shanxi Medical University
Authors:
Zhaohong Yin, Lixin Sun, Zhenwei Gao, Hongwei Zhao, Chunlin Chen, Ping Liu
Target audience:
Oncology researchers and clinicians
Study period:
2005–2019
Ethics approval:
Ethics Committee of Nanfang Hospital of Southern Medical University, NFEC-2017-135
Clinical trial registration:
CHiCTR1800017778, 14/08/2018
Treatment groups:
Radical hysterectomy (RH), Radiochemotherapy (R-CT)
Primary outcomes:
5-year overall survival (OS), 5-year disease-free survival (DFS)
Sample size:
236 patients (RH: 203, R-CT: 33)
Follow-up:
Available survival outcome data for all included patients
Data source:
Four C database of cervical cancer clinical records in mainland China
Inclusion criteria:
Age ≥18, biopsy-confirmed cervical adenocarcinoma, FIGO 2018 stage IIIC1, specific treatment regimens per group
Exclusion criteria:
Special cervical cancer types, non-compliance with inclusion criteria
Year:
2026
Research Area:
Molecular biology, Cancer research
Document Type:
Supplemental figures and legends
Institution:
Unspecified research laboratory
Authors:
Unspecified
Cell Lines:
AGS, KATO-III, GT5, 293T
Experimental Methods:
Luciferase reporter assay, quantitative real-time PCR, immunofluorescence, co-immunoprecipitation, yeast two-hybrid analysis, in vitro invasion and migration assays, in vivo tumorigenicity assay
Molecular Targets:
PPARδ, YAP1, SOX9, CTGF
Compounds Tested:
GW501516 (PPARδ agonist), GSK3837/GSK3738 (PPARδ antagonists)
Time Points:
24 h, 48 h, 10–14 days
Controls:
Vector-only transfection, DMSO treatment, wild-type and mutant promoters, normal IgG
Year:
2023
Study Type:
Clinical trial, Phase 2
Document Type:
Supplementary data
Authors:
Samuel J. Klempner et al.
Intervention:
Zolbetuximab monotherapy, Zolbetuximab + mFOLFOX6, Zolbetuximab + Pembrolizumab
Population:
Patients with advanced or metastatic claudin 18.2–positive gastric or gastroesophageal junction adenocarcinoma
Sample Size:
54
Countries:
France, Italy, Japan, Korea, Taiwan, United States
Cohorts:
1A, 2, 3A
Age Range:
32–79 years
Median Age:
60–65 years
Sex Distribution:
Male 33, Female 21
Ethnicity:
Asian 43%, White 43%, Black 2%, Hispanic/Latino 6%
PK Sampling:
Zolbetuximab, mFOLFOX, Pembrolizumab
HRQOL Assessment:
Multiple cycles per cohort
Follow-up:
30-day safety, 90-day follow-up
Geographic Focus:
Eastern Asia, Central/Eastern Europe, United States, France
End Points:
Pharmacokinetics, health-related quality of life, patient demographics
Data Sources:
Clinical study data, GLOBOCAN 2020, literature references
Supplementary Materials:
Methods, Tables, Figures
Year:
2016–2023
Institution:
West China Hospital, Sichuan University
Department:
Thoracic Surgery
Location:
Chengdu, China
Study Type:
Retrospective cohort study
Patient Population:
Siewert type II adenocarcinoma of the esophagogastric junction
Surgical Approaches:
McKeown esophagectomy, Left Thoracotomy
Intervention:
Complete mediastinal and abdominal lymphadenectomy (CMAD) versus middle/lower mediastinal and abdominal lymphadenectomy (MLMAD)
Sample Size:
221 patients
Outcome Measures:
Overall survival, disease-free survival, lymph node metastasis patterns, surgical complications
Keywords:
Minimally invasive esophagectomy, Propensity score matching, McKeown
Corresponding Author:
Qi-Xin Shang, MD, PhD, West China Hospital, Chengdu, China, [email protected]
Data Source:
Hospital surgical records and pathology reports
Contextual Description:
Retrospective clinical study analyzing survival outcomes and lymph node metastasis patterns in Siewert II adenocarcinoma patients undergoing different extents of mediastinal lymphadenectomy.