Additional file 1 of Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients
Creators: name: Farina, Benito
name: Guerra, Ana Delia Ramos
name: Bermejo-Peláez, David
name: Miras, Carmelo Palacios
name: Peral, Andrés Alcazar
name: Madueño, Guillermo Gallardo
name: Jaime, Jesús Corral
name: Vilalta-Lacarra, Anna
name: Pérez, Jaime Rubio
name: Muñoz-Barrutia, Arrate
name: Peces-Barba, German R.
name: Maceiras, Luis Seijo
name: Gil-Bazo, Ignacio
name: Gómez, Manuel Dómine
name: Ledesma-Carbayo, María J.
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Cancer

description: Additional file 1: Table S1. Number of patients in the training and independent test set for each model considering the endpoint PFS6. Table S2. Number of patients in the training and independent test set for each model considering the endpoint PFS9. Table S3. CT image acquisition and reconstruction parameters for the two institutions involved in the study: FJD and CUN. Table S4. Results of the feature repeatability against segmentation and feature reproducibility. Table S5. Clinical variables used for the implementation of the clinical models. Table S6. Demographic and clinical characteristics of the patients in the baseline analysis with imaging data. P-values of no significant difference analysis (p-value &gt; 0.05) between the training and test set after two samples T-test for continuous variables, and Chi-square test for categorical variables. SD represents the standard deviation, and Q1 and Q3 represent the first and third quartiles, respectively. Table S7. Demographic and clinical characteristics of the patients in the longitudinal analysis with imaging data. P-values of no significant difference analysis (p-value &gt; 0.05) between the training and test set after two samples T-test for continuous variables, and Chi-square test for categorical variables. SD represents the standard deviation, and Q1 and Q3 represent the first and third quartiles, respectively. Table S8. Number of features selected for each RF model. Longitudinal models had as input the concatenation of features extracted from baseline, 1st and 2nd follow-up data (n time steps = 3). In the case of clinical models, only 12 variables had continuous values. Table S9. Results of the implemented models in the training set for PFS6 and PFS9. The results are presented in terms of the area under the curve ROC curve (AUC) for the 3-fold cross validation. Table S10. Response prediction performance comparison between longitudinal and ensemble models in the independent test set for endpoint PFS6 by evaluating AUC, ACC, SENS, SPEC, PREC and bACC, respectively. For each metric, the 95% confidence interval is shown. The highest value for each metric is highlighted in bold. Table S11. Response prediction performance comparison between longitudinal and ensemble models in the independent test set for endpoint PFS9 by evaluating AUC, ACC, SENS, SPEC, PREC and bACC, respectively. For each metric, the 95% confidence interval is shown and the highest value is highlighted in bold.