Predicting Survival in Patients with Advanced NSCLC Treated with Atezolizumab Using Pre‐ and on‐Treatment Prognostic Biomarkers
Résumé
Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. We introduce a novel kinetics‐machine learning (kML) model that integrates baseline markers, tumor kinetics, and four on‐treatment simple blood markers (albumin, C‐reactive protein, lactate dehydrogenase, and neutrophils). Developed for immune‐checkpoint inhibition (ICI) in non‐small cell lung cancer on three phase II trials (533 patients), kML was validated on the two arms of a phase III trial (ICI and chemotherapy, 377 and 354 patients). It outperformed the current state‐of‐the‐art for individual predictions with a test set C‐index of 0.790, 12‐months survival accuracy of 78.7% and hazard ratio of 25.2 (95% CI: 10.4–61.3, P < 0.0001) to identify long‐term survivors. Critically, kML predicted the success of the phase III trial using only 25 weeks of on‐study data (predicted HR = 0.814 (0.64–0.994) vs. final study HR = 0.778 (0.65–0.931)). Modeling on‐treatment blood markers combined with predictive machine learning constitutes a valuable approach to support personalized medicine and drug development. The code is publicly available at https://gitlab.inria.fr/benzekry/nlml_onco .
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Benzekry_Jamois_2024_Clinical Pharmacology & Therapeutics_Predicting Survival in Patients with Advanced NSCLC Treated with Atezolizumab Using Pre‐ and on‐Treatment Prognostic Biomarkers_1.pdf (3.36 Mo)
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