Prediction of individual survival and trial outcome for anti-PDL1 treatment in non-small cell lung cancer using blood markers-based kinetics-machine learning
Benzekry, S.; Karslsen, M.; Bigarre, C.; El Kaoutari, A.; Gomes, B.; Stern, M.; Neubert, A.; Bruno, R.; Mercier, F.; Vatakuti, S.; Curle, P.; Jamois, C.
Show abstract
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, CRP, lactate dehydrogenase and neutrophils). Developed for immune-checkpoint inhibition (ICI) in non-small cell lung cancer on three phase 2 trials (533 patients), kML was validated on the two arms of a phase 3 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, a 12-months survival accuracy of 78.7% and a 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 3 trial using only 25 weeks of on-study data (predicted HR = 0.814 (0.64 - 0.994) versus final study HR = 0.778 (0.65 - 0.931)). Our model constitutes a valuable approach to support personalized medicine and drug development.
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