Journal: Sports

Article Title: Advancements in Performance Monitoring: A Systematic Review of Sensor Technologies in Rowing and Canoeing Biomechanics

doi: 10.3390/sports12090254

Figure Lengend Snippet: Summary table of storage and transmission system, data visualization, and analysis software.

Article Snippet: [ ] , 100 Hz , Mobile phone, tablet, and PC , Not real time , Radio Modems , MATLAB (version R2011a) (The MathWorks Inc., Natick, MA, USA).

Techniques: Transmission Assay, Software, Sampling

Journal: Sports

Article Title: Advancements in Performance Monitoring: A Systematic Review of Sensor Technologies in Rowing and Canoeing Biomechanics

doi: 10.3390/sports12090254

Figure Lengend Snippet: Summary table of storage and transmission system, data visualization, and analysis software.

Article Snippet: [ ] , 256 Hz , PC , Not real time , Radio Modems , MATLAB (version R2010a by The MathWorks Inc., Natick, MA, USA) IBM SPSS (version 12.0, IBM Inc., Chicago, IL, USA).

Techniques: Transmission Assay, Software, Sampling

Journal: Frontiers in Oncology

Article Title: Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

doi: 10.3389/fonc.2020.01416

Figure Lengend Snippet: Distribution of radiomics scores according to the PM status. (A–C) The values of the radiomics scores (Rad-scores) of each patient and the median values with interquartile range of Rad-scores. PM, peritoneal metastasis status.

Article Snippet: Based on the above procedure, we used an available radiomics analysis package ( https://github.com/mvallieres/radiomics/ ) in Matlab R2016a (The MathWorks Inc.) to extract and calculated the radiomics features of these images.

Techniques:

Journal: Frontiers in Oncology

Article Title: Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

doi: 10.3389/fonc.2020.01416

Figure Lengend Snippet: Development of radiomics nomogram in training cohort. The radiomics nomogram incorporating the radiomics signature and cT stage and cN stages was developed in the training cohort.

Article Snippet: Based on the above procedure, we used an available radiomics analysis package ( https://github.com/mvallieres/radiomics/ ) in Matlab R2016a (The MathWorks Inc.) to extract and calculated the radiomics features of these images.

Techniques:

Journal: Frontiers in Oncology

Article Title: Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

doi: 10.3389/fonc.2020.01416

Figure Lengend Snippet: Calibration curves of radiomics nomogram of peritoneal metastasis (PM) status prediction in training and internal and external validation cohorts. The calibration curves described the calibration in agreement between predicted and observed outcome. The 45-degree reference line means a perfect calibration with the outcome by ideal model. The solid line is the performance of the nomogram, without correction for overfit. The dotted line is the bootstrap-corrected performance of the nomogram, with a scatter estimate for future accuracy. (A–C) Calibration curves in the training cohort, internal and external validation cohorts.

Article Snippet: Based on the above procedure, we used an available radiomics analysis package ( https://github.com/mvallieres/radiomics/ ) in Matlab R2016a (The MathWorks Inc.) to extract and calculated the radiomics features of these images.

Techniques: Biomarker Discovery

Journal: Frontiers in Oncology

Article Title: Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

doi: 10.3389/fonc.2020.01416

Figure Lengend Snippet: Decision curve analysis of the radiomics nomogram, Rad-score, and clinical T and N stage in each cohort. (A–C) Calibration curves in the training cohort, internal and external validation cohorts.

Article Snippet: Based on the above procedure, we used an available radiomics analysis package ( https://github.com/mvallieres/radiomics/ ) in Matlab R2016a (The MathWorks Inc.) to extract and calculated the radiomics features of these images.

Techniques: Biomarker Discovery

Journal: Scientific Reports

Article Title: Combining Multiple Magnetic Resonance Imaging Sequences Provides Independent Reproducible Radiomics Features

doi: 10.1038/s41598-018-37984-8

Figure Lengend Snippet: Radiomics features extracted using our in-house software.

Article Snippet: All the post-acquisition steps were performed using an in-house software (Matlab R2013b [The Mathworks, Natick, MA, USA]) adapted from the Vallieres radiomics toolbox .

Techniques: Software, Standard Deviation

Journal: Scientific Reports

Article Title: Combining Multiple Magnetic Resonance Imaging Sequences Provides Independent Reproducible Radiomics Features

doi: 10.1038/s41598-018-37984-8

Figure Lengend Snippet: Radiomics features extracted using our in-house software.

Article Snippet: All the post-acquisition steps were performed using an in-house software (Matlab R2013b [The Mathworks, Natick, MA, USA]) adapted from the Vallieres radiomics toolbox .

Techniques: Software, Standard Deviation

Journal: Healthcare

Article Title: Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review

doi: 10.3390/healthcare10081511

Figure Lengend Snippet: Applications of radiomics in pancreatic CT images.

Article Snippet: Xie , 2019 , In-house algorithm (MATLAB R2017a) , Differential diagnosis (MCN vs. SCN) , 57 (31 MCNs, 26 SCNs) , SW , Radiologist , AP, PVP, DP , Radiomics model: AUC 0.989, Acc 94.7%, Sen 93.6%, Spe 96.2% Combined model (radiomics + radiological features): AUC 0.994, Acc 98.2%, Sen 96.8%, Spe 100%.

Techniques: Software, Biomarker Discovery, Histopathology, Imaging, Standard Deviation, Functional Assay

Journal: Healthcare

Article Title: Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review

doi: 10.3390/healthcare10081511

Figure Lengend Snippet: Applications of radiomics in pancreatic MRI images.

Article Snippet: Xie , 2019 , In-house algorithm (MATLAB R2017a) , Differential diagnosis (MCN vs. SCN) , 57 (31 MCNs, 26 SCNs) , SW , Radiologist , AP, PVP, DP , Radiomics model: AUC 0.989, Acc 94.7%, Sen 93.6%, Spe 96.2% Combined model (radiomics + radiological features): AUC 0.994, Acc 98.2%, Sen 96.8%, Spe 100%.

Techniques: Biomarker Discovery, Histopathology, Software, Methylated DNA Immunoprecipitation