Journal: Translational Lung Cancer Research

Article Title: 3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma

doi: 10.21037/tlcr-20-122

Figure Lengend Snippet: Representative CT images with tumor segmentation by the radiomics analysis platform. (I) Axial views show a lobulated lung tumor for image segmentation. (II) Axial view, (III) coronal view, and (IV) sagittal view showed segmented lung tumors indicated by a yellow overlay. (V) Three-dimensional views of the segmented tumors. (A) (I-V) A 61-year-old male non-smoker, with EGFR wild-type lung adenocarcinoma. CT mediastinal window showed a solid mass in the upper lobe of the left lung with lobulation sign and a maximum diameter of about 20 mm. (B) (I-V) A 58-year-old male smoker, with lung adenocarcinoma of EGFR exon-19 deletion. CT mediastinal window showed a solid mass in the upper lobe of the right lung with a maximum diameter of about 35 mm, rough margin, and lobulated sign. (C) (I-V) A 48-year-old female non-smoker, with lung adenocarcinoma of EGFR exon-21 L858R mutation. CT mediastinal window showed a solid mass in the lower lobe of the left lung with a maximum diameter of about 36 mm, burrs, and lobulation. EGFR, epidermal growth factor receptor.

Article Snippet: Tumor segmentation and radiomic feature extraction We used a radiomics analysis package (Radiomics 1.0.9a, Siemens Healthineers) on a research platform (SyngoVia VB10b, Research Frontier, Siemens Healthineers) to three-dimensionally segment the tumor on CT images as a volume of interest and extracted radiomic features ( 21 ).

Techniques: Mutagenesis

Journal: Translational Lung Cancer Research

Article Title: 3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma

doi: 10.21037/tlcr-20-122

Figure Lengend Snippet: Results of the logistic regression models to detect exon-21 L858R mutation in the test cohort

Article Snippet: Tumor segmentation and radiomic feature extraction We used a radiomics analysis package (Radiomics 1.0.9a, Siemens Healthineers) on a research platform (SyngoVia VB10b, Research Frontier, Siemens Healthineers) to three-dimensionally segment the tumor on CT images as a volume of interest and extracted radiomic features ( 21 ).

Techniques: Mutagenesis

Journal: Frontiers in Oncology

Article Title: Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study

doi: 10.3389/fonc.2022.856605

Figure Lengend Snippet: Comparison between radiomics model and senior radiologists in training set.

Article Snippet: The radiomics parameters were extracted using a MATLAB (R2020a) package of radiomics analysis from Github (github.com/mvallieres/radiomics), and the statistical modeling was performed by R software (version 3.6.2).

Techniques: Comparison

Journal: Frontiers in Oncology

Article Title: Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study

doi: 10.3389/fonc.2022.856605

Figure Lengend Snippet: Comparison between radiomics model and senior radiologists in test set.

Article Snippet: The radiomics parameters were extracted using a MATLAB (R2020a) package of radiomics analysis from Github (github.com/mvallieres/radiomics), and the statistical modeling was performed by R software (version 3.6.2).

Techniques: Comparison

Journal: Frontiers in Oncology

Article Title: Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study

doi: 10.3389/fonc.2022.856605

Figure Lengend Snippet: Area under the curve between the radiomics model and the senior radiologists in the test set.

Article Snippet: The radiomics parameters were extracted using a MATLAB (R2020a) package of radiomics analysis from Github (github.com/mvallieres/radiomics), and the statistical modeling was performed by R software (version 3.6.2).

Techniques:

Journal: Frontiers in Oncology

Article Title: Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study

doi: 10.3389/fonc.2022.856605

Figure Lengend Snippet: Receiver operating characteristic between the radiomics model and radiologists in the whole set.

Article Snippet: The radiomics parameters were extracted using a MATLAB (R2020a) package of radiomics analysis from Github (github.com/mvallieres/radiomics), and the statistical modeling was performed by R software (version 3.6.2).

Techniques:

Journal: Nature Communications

Article Title: Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma

doi: 10.1038/s41467-019-11007-0

Figure Lengend Snippet: Image post-processing radiomic workflow. a relative cerebral blood volume (rCBV) and K trans maps of perfusion MRI are acquired. b Segmentation of the region of interest using 3D slicer software. c Radiomic feature extraction from the whole tumor volume. d Statistical analysis: radiomic and clinical features are analyzed to determince their diagnostic and predictive values

Article Snippet: Radiomic analysis was performed using our in-house developed software implemented in MATLAB (version 2017b; MathWorks Inc; Natick, MA).

Techniques: Software, Extraction, Diagnostic Assay

Journal: Molecules

Article Title: Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

doi: 10.3390/molecules27134169

Figure Lengend Snippet: Summary of the most usually recognized ADME analysis tools used in the computational drug design process.

Article Snippet: 10. , Metabase , It is a low-cost radio analytical LIMs in ADME/PK research based on Excel , https://www.metabase.com/ , [ ] .

Techniques: