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Supplementary Tables S1-S11 from Utilizing Machine Learning to Identify Multimodal Signatures for Patients Who Would Benefit from the Addition of Tremelimumab to Durvalumab and Chemotherapy (TRIDENT) Ferdinandos Skoulidis (16041866) Salma K. Jabbour (25153919) Edward B. Garon (15033509) Puneeth Iyengar (25153922) Giorgio Scagliotti (25153925) Loïc Ferrer (25153928) Guillaume Etchepare (25153931) Olivier Gallinato (25153934) Jérôme Faure (25153937) Paul Bernard (25153940) Thierry Colin (25153943) Philippe Menu (25153946) Yian Lin (25153949) Ling Cai (25153952) Ammar Ahmed Chaudhry (25153955) Amanda Remorino (25153958) Ross Stewart (14851015) Luisa Luciani-Silverman (25153961) Katy Miller (25153964) David Dellamonica (25153967) Jolyon Faria (25153970) Yiduo Zhang (25153973) Cancer Cancer Detection and Diagnosis Immuno-oncology Biomarkers Imaging biomarkers Clinical Imaging Immunology Antibodies/immunoconjugates Immunotherapy Checkpoint blockade Lung Cancer <p>Supplementary Table 1. Time-to-event models’ hyperparameter space for Bayesian optimization. Supplementary Table 2. Baseline characteristics for patients included in analysis set 2. Supplementary Table 3. Baseline characteristics for patients included in analysis set 3. Supplementary Table 4. Baseline characteristics for patients included in analysis set 4. Supplementary Table 5. Baseline characteristics for patients included in analysis set 5. Supplementary Table 6. Baseline characteristics for patients included in analysis set 6. Supplementary Table 7. Distribution of missing values for all clinical features. Supplementary Table 8. Representativeness of study participants Supplementary Table 9. Integrated AUC between 0 and 24 months for all numerical features (analysis set 1) Supplementary Table 10. Wald test P-values for all categorical features, ranked according to P-values in the tremelimumab plus durvalumab and chemotherapy arm (analysis set 1) Supplementary Table 11. Group statistics for the 50% of patients with higher/lower treatment benefit (from addition of tremelimumab to durvalumab and chemotherapy) identified by the best performing model (Model 7) with deep dive in STK11, KRAS, and KEAP1 mutations.</p> 2026-10-01T00:00:00Z Text Journal contribution 10.1158/1078-0432.34040690 https://figshare.com/articles/journal_contribution/Supplementary_Tables_S1-S11_from_Utilizing_Machine_Learning_to_Identify_Multimodal_Signatures_for_Patients_Who_Would_Benefit_from_the_Addition_of_Tremelimumab_to_Durvalumab_and_Chemotherapy_TRIDENT_/34040690 CC BY