chang-gung image texture analysis toolbox (MathWorks Inc)
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MathWorks Inc
chang-gung image texture analysis toolbox
Chang Gung Image Texture Analysis Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/chang-gung+image+texture+analysis+toolbox/pm37880322-81-16-33
Average 90 stars, based on 1 article reviews
Chang Gung Image Texture Analysis Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/chang-gung+image+texture+analysis+toolbox/pm37880322-81-16-33
Average 90 stars, based on 1 article reviews
chang-gung image texture analysis toolbox - by Bioz Stars,
2026-09
90/100 stars
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Positron Emission Tomography:Article Title: Metabolic radiogenomics in lung cancer: associations between FDG PET image features and oncogenic signaling pathway alterations Article Snippet: After creating gradient-based segmentation of the target tumor lesion, we extracted PET image features using the Chang-Gung Image Texture Analysis toolbox (CGITA, https://code.google.com/p/cigita ), an Article Title: Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients. Article Snippet: After performing gradient-based segmentation of the target tumor lesion, we extracted PET image features using the Chang-Gung Image Texture Analysis toolbox (CGITA, https:// code. google. com/p/ cgita), an Article Title: Intratumoral heterogeneity of (18)F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma. Article Snippet: Purpose To assess whether intratumoral heterogeneity measured by F-FDG PET texture analysis has potential as a prognostic imaging biomarker in patients with pancreatic ductal adenocarcinoma (PDAC).. Methods We evaluated a cohort of 137 patients with newly diagnosed PDAC who underwent pretreatment F-FDG PET/CT from January 2008 to December 2010.. First-order (histogram indices) and higher-order (grey-level run length, difference, size zone matrices) textural features of primary tumours were extracted by PET texture analysis. Article Title: Pre-treatment 18 F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer. Article Snippet: Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer H.K.. Ahn , H. Lee , S.G. Kim , S.H.. Hyun c,* Division of Hematology and Oncology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea Department of Nuclear Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea Article Title: Combining the radiomic features and traditional parameters of 18 F-FDG PET with clinical profiles to improve prognostic stratification in patients with esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy and surgery. Article Snippet: Objectives To investigate the role of the traditional and radiomic parameters of 18F-FDG PET for predicting the outcomes of patients with esophageal squamous cell carcinoma (SqCC).. Methods Forty-four patients with primary esophageal SqCC who underwent neoadjuvant chemoradiotherapy (CCRT) followed by esophagectomy (tri-modality treatment) were retrospectively analyzed.. All patients underwent 18F-FDG PET/CT before and after neoadjuvant CCRT. Article Title: Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18 F-FDG Textural Features Machine Learning Approaches with PCA Article Snippet: Quantitative analysis was assessed using the Chang-Gung Image Texture Analysis toolbox ( http://code.google.com/p/cgita ), an Article Title: Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients Article Snippet: After performing gradient-based segmentation of the target tumor lesion, we extracted PET image features using the Chang-Gung Image Texture Analysis toolbox (CGITA, https://code.google.com/p/cgita ), an Software:Article Title: Metabolic radiogenomics in lung cancer: associations between FDG PET image features and oncogenic signaling pathway alterations Article Snippet: After creating gradient-based segmentation of the target tumor lesion, we extracted PET image features using the Chang-Gung Image Texture Analysis toolbox (CGITA, https://code.google.com/p/cigita ), an Article Title: Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients. Article Snippet: After performing gradient-based segmentation of the target tumor lesion, we extracted PET image features using the Chang-Gung Image Texture Analysis toolbox (CGITA, https:// code. google. com/p/ cgita), an Article Title: Intratumoral heterogeneity of (18)F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma. Article Snippet: Purpose To assess whether intratumoral heterogeneity measured by F-FDG PET texture analysis has potential as a prognostic imaging biomarker in patients with pancreatic ductal adenocarcinoma (PDAC).. Methods We evaluated a cohort of 137 patients with newly diagnosed PDAC who underwent pretreatment F-FDG PET/CT from January 2008 to December 2010.. First-order (histogram indices) and higher-order (grey-level run length, difference, size zone matrices) textural features of primary tumours were extracted by PET texture analysis. Article Title: Pre-treatment 18 F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer. Article Snippet: Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer H.K.. Ahn , H. Lee , S.G. Kim , S.H.. Hyun c,* Division of Hematology and Oncology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea Department of Nuclear Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea Article Title: Combining the radiomic features and traditional parameters of 18 F-FDG PET with clinical profiles to improve prognostic stratification in patients with esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy and surgery. Article Snippet: Objectives To investigate the role of the traditional and radiomic parameters of 18F-FDG PET for predicting the outcomes of patients with esophageal squamous cell carcinoma (SqCC).. Methods Forty-four patients with primary esophageal SqCC who underwent neoadjuvant chemoradiotherapy (CCRT) followed by esophagectomy (tri-modality treatment) were retrospectively analyzed.. All patients underwent 18F-FDG PET/CT before and after neoadjuvant CCRT. Article Title: Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18 F-FDG Textural Features Machine Learning Approaches with PCA Article Snippet: Quantitative analysis was assessed using the Chang-Gung Image Texture Analysis toolbox ( http://code.google.com/p/cgita ), an Article Title: Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients Article Snippet: After performing gradient-based segmentation of the target tumor lesion, we extracted PET image features using the Chang-Gung Image Texture Analysis toolbox (CGITA, https://code.google.com/p/cgita ), an Standard Deviation:Article Title: Metabolic radiogenomics in lung cancer: associations between FDG PET image features and oncogenic signaling pathway alterations Article Snippet: After creating gradient-based segmentation of the target tumor lesion, we extracted PET image features using the Chang-Gung Image Texture Analysis toolbox (CGITA, https://code.google.com/p/cigita ), an Article Title: Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients. Article Snippet: After performing gradient-based segmentation of the target tumor lesion, we extracted PET image features using the Chang-Gung Image Texture Analysis toolbox (CGITA, https:// code. google. com/p/ cgita), an Article Title: Intratumoral heterogeneity of (18)F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma. Article Snippet: Purpose To assess whether intratumoral heterogeneity measured by F-FDG PET texture analysis has potential as a prognostic imaging biomarker in patients with pancreatic ductal adenocarcinoma (PDAC).. Methods We evaluated a cohort of 137 patients with newly diagnosed PDAC who underwent pretreatment F-FDG PET/CT from January 2008 to December 2010.. First-order (histogram indices) and higher-order (grey-level run length, difference, size zone matrices) textural features of primary tumours were extracted by PET texture analysis. Article Title: Pre-treatment 18 F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer. Article Snippet: Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer H.K.. Ahn , H. Lee , S.G. Kim , S.H.. Hyun c,* Division of Hematology and Oncology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea Department of Nuclear Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea Article Title: Combining the radiomic features and traditional parameters of 18 F-FDG PET with clinical profiles to improve prognostic stratification in patients with esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy and surgery. Article Snippet: Objectives To investigate the role of the traditional and radiomic parameters of 18F-FDG PET for predicting the outcomes of patients with esophageal squamous cell carcinoma (SqCC).. Methods Forty-four patients with primary esophageal SqCC who underwent neoadjuvant chemoradiotherapy (CCRT) followed by esophagectomy (tri-modality treatment) were retrospectively analyzed.. All patients underwent 18F-FDG PET/CT before and after neoadjuvant CCRT. Article Title: Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18 F-FDG Textural Features Machine Learning Approaches with PCA Article Snippet: Quantitative analysis was assessed using the Chang-Gung Image Texture Analysis toolbox ( http://code.google.com/p/cgita ), an Article Title: Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients Article Snippet: After performing gradient-based segmentation of the target tumor lesion, we extracted PET image features using the Chang-Gung Image Texture Analysis toolbox (CGITA, https://code.google.com/p/cgita ), an |