Additional file 1 of Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma
Creators: name: Martens, Roland M.
name: Koopman, Thomas
name: Noij, Daniel P.
name: Pfaehler, Elisabeth
name: Übelhör, Caroline
name: Sharma, Sughandi
name: Vergeer, Marije R.
name: Leemans, C. René
name: Hoekstra, Otto S.
name: Yaqub, Maqsood
name: Zwezerijnen, Gerben J.
name: Heymans, Martijn W.
name: Peeters, Carel F. W.
name: de Bree, Remco
name: de Graaf, Pim
name: Castelijns, Jonas A.
name: Boellaard, Ronald
Publisher: name: figshare
Year: 2020
Subjects: subject: Space Science
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Biological Sciences not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Cancer
subject: Infectious Diseases
subject: FOS: Health sciences

description: Additional file 1: Supplement 1. All 8 radiomics factors, consisting of a spectrum of the extracted radiomics features. The number of each feature reflects the importance weight in that factor in which it is present. Supplement 2. Correlations of clinical parameters, 18F-FDG-PET-parameters and trained radiomic factors in the validation cohort. Supplement 3: The correlations between radiomics factors (Spearman’s Rho), with the significant correlated factors (bold) after Bonferroni’s correction (P&lt; 0,00078125). Factor 1 was significantly correlated with factor 8. Factor 2 was significantly correlated with factor 7. Supplement 4. Multivariable cox regression analysis in the training set performing clinical, PET and/or radiomics parameters separately to predict recurrence, metastasis and overall survival. Multivariable cox regression analysis performing combined clinical, PET and/or radiomics parameters to predict recurrence, metastasis and overall survival. Supplement 6. The comparison of the predictive accuracy between the combined clinical + PET parameters and combined clinical + radiomics models versus the combination of clinical, + PET + radiomics predicting recurrence, distant metastasis and death. The prediction of recurrence was significantly more accurate using the combination of clinical + PET + radiomic factors than the combination of clinical + PET parameters, and it showed a borderline significant trend compared with clinical + radiomics factors. The prediction of metastasis was found significant more accurate combining clinical + PET + radiomics compared to clinical + PET and clinical+ radiomics factors. The prediction of overall survival was found not significant different for any prediction model. Supplement 7a. The risk stratification was constructed in the training set, using the combined prediction model for locoregional recurrence, metastasis and death (Figure 3). 7b. The risk stratification using the combined prediction model for locoregional recurrence, metastasis and death (Figure 3). 7c. The risk stratification using the combined prediction model for locoregional recurrence, metastasis and death (Figure 3). Supplement 8a. The risk stratification was validated in the validation set, using the combined prediction model for locoregional recurrence, metastasis and death (Figure 3). 8b. The risk stratification using the combined prediction model for locoregional recurrence, metastasis and death (Figure 3). 8c. The risk stratification using the combined prediction model for locoregional recurrence, metastasis and death (Figure 3). Supplement 9. TRIPOD Checklist: Prediction Model Development. Supplement 10. Output example of the RaCat tool