Additional file 1: Supplementary Figure 1. Representative lymph node metastases. Supplementary Figure 2. Training and cross-validation with LASSO. Supplementary Figure 3. Performance comparison of radiomics in the unsure group (likely benign and likely malignant) to expert radiologists and the effect of encountering the prediction model. Supplementary Figure 4. Performance comparison of radiomics in the unsure group (likely benign and likely malignant) to expert radiologists plotting the 95% confidence interval AUC. Supplementary Table 1. Performance comparison of radiomics and the two expert radiologists. Supplementary Table 2. The effect of encountering radiomics in the unsure group (likely benign and likely malignant) classified by the e...
Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for...
Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement s...
The aim of this study was to compare two different PET/CT tomographs for the evaluation of the role ...
Background: We evaluated the role of radiomics applied to contrast-enhanced computed tomography (CT)...
PurposeTo investigate the ability of a PET/CT-based radiomics nomogram to predict occult lymph node ...
Contains fulltext : 191294.pdf (publisher's version ) (Open Access)BACKGROUND: Lym...
Background: Lymph node stage prior to treatment is strongly related to disease progression and poor ...
Background: To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures a...
Additional file 1. Supplement A1: 18-Month Results. Supplement A2: Training Cohort Ethics Board Amen...
BackgroundThe use of traditional techniques to evaluate breast cancer is restricted by the subjectiv...
Lung cancer is the leading cause of cancer-related death in the United States and worldwide. Early ...
Context: Cancer Radiomics is an emerging field in medical imaging and refers to the process of conve...
Contains fulltext : 232784.pdf (Publisher’s version ) (Open Access)BACKGROUND: Rad...
Contains fulltext : 153334.pdf (publisher's version ) (Open Access)Radiomics extra...
Purpose: to assess the likelihood of local recurrence of lung malignancies following stereotactic ab...
Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for...
Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement s...
The aim of this study was to compare two different PET/CT tomographs for the evaluation of the role ...
Background: We evaluated the role of radiomics applied to contrast-enhanced computed tomography (CT)...
PurposeTo investigate the ability of a PET/CT-based radiomics nomogram to predict occult lymph node ...
Contains fulltext : 191294.pdf (publisher's version ) (Open Access)BACKGROUND: Lym...
Background: Lymph node stage prior to treatment is strongly related to disease progression and poor ...
Background: To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures a...
Additional file 1. Supplement A1: 18-Month Results. Supplement A2: Training Cohort Ethics Board Amen...
BackgroundThe use of traditional techniques to evaluate breast cancer is restricted by the subjectiv...
Lung cancer is the leading cause of cancer-related death in the United States and worldwide. Early ...
Context: Cancer Radiomics is an emerging field in medical imaging and refers to the process of conve...
Contains fulltext : 232784.pdf (Publisher’s version ) (Open Access)BACKGROUND: Rad...
Contains fulltext : 153334.pdf (publisher's version ) (Open Access)Radiomics extra...
Purpose: to assess the likelihood of local recurrence of lung malignancies following stereotactic ab...
Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for...
Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement s...
The aim of this study was to compare two different PET/CT tomographs for the evaluation of the role ...