# Mechanistic Learning for Survival Prediction in NSCLC Using Routine Blood Biomarkers and Tumor Kinetics

Source: [Mechanistic Learning for Survival Prediction in NSCLC Using Routine Blood Biomarkers and Tumor Kinetics](<https://www.sota2.com/research/paper/696dba5de25ad2fa9d1ecaf1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-20T05:00:04.106Z

Authors: Ruben Taieb, Ren\\'e Bruno, Pascal Chanu, Jin Yan Jin, S\\'ebastien Benzekry

Published: 2026-01-16T10:07:05.000Z

## Abstract

Background Predicting overall survival (OS) in non-small cell lung cancer (NSCLC) is essential for clinical decision-making and drug development. While tumor and blood test markers kinetics are intrinsically linked, their joint dynamics and relationship to OS remain unknown. Methods We developed a mechanistic model capturing the interplay between tumor (T) burden and three key blood markers kinetics: albumin (A), lactate dehydrogenase (L), and neutrophils (N), through coupled differential equations (termed TALN-k). This model was enhanced with a machine learning framework (TALN-kML) for OS prediction. The model was trained and validated on clinical trial data from NSCLC patients treated with atezolizumab in monotherapy (N = 862 patients) or combination therapy (N = 1,115). Model parameters were estimated using nonlinear mixed-effects modelling, and survival predictions were assessed using individual and trial level metrics. Results TALN-k successfully described individual and population-level marker kinetics, revealing complex interactions between tumor and blood markers, and improving corrected BIC and log-likelihood metrics by a significant margin of previous empirical state-of-the-art models. Feature selection methods also highlighted valuable predictive parameters, indicatives of good or poor prognosis. The TALN-kML model outperformed empirical, uncoupled models, achieving improved C-index (0.74 $\\pm$ 0.02 vs 0.72 $\\pm$ 0.03), 12-months AUC (0.83 $\\pm$ 0.004 vs 0.79 $\\pm$ 0.05), and accuracy (0.77 $\\pm$ 0.03 vs 0.76 $\\pm$ 0.05) in OS prediction. Conclusion Our mechanistic learning approach allows for an interpretable model, which improves on longitudinal data description and on survival prediction in NSCLC by jointly integrating tumor and blood markers kinetics. This methodology offers a promising avenue for both personalized treatment strategies and drug development optimization.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2601.11148>)

[PDF](<https://arxiv.org/pdf/2601.11148.pdf>)