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Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE.

Mark Laurie^1,2, James Lu³

¹Modeling & Simulation/Clinical Pharmacology, Genentech, South San Francisco, CA, USA.

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|November 18, 2023

Summary

We developed a new AI model, Tumor Dynamic Neural-ODE (TDNODE), for oncology drug development. This model improves prediction accuracy from patient tumor data, aiding personalized cancer therapy decisions.

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Area of Science:

Oncology
Computational Biology
Artificial Intelligence

Background:

Tumor dynamic modeling is crucial for oncology drug development but requires enhanced predictivity and personalization.
Existing models face limitations in handling incomplete or truncated patient data.

Purpose of the Study:

To introduce Tumor Dynamic Neural-ODE (TDNODE), a novel pharmacology-informed neural network for discovering models from longitudinal tumor size data.
To address the challenge of making unbiased predictions from truncated tumor growth data.

Main Methods:

Utilized an encoder-decoder neural network architecture designed for time-homogeneous dynamical systems.
Developed a modeling formalism where encoder outputs are interpretable as kinetic rate metrics.
Applied the TDNODE model to longitudinal tumor size data for model discovery.

Main Results:

TDNODE demonstrated the ability to make unbiased predictions from truncated tumor data, a key limitation of current models.
The derived kinetic rate metrics accurately predicted patients' overall survival (OS).
The formalism supports the integration of multimodal dynamical datasets in oncology.

Conclusions:

TDNODE offers a powerful, principled approach for oncology disease modeling using longitudinal tumor data.
The model enhances predictive capabilities, paving the way for more personalized cancer therapies.
Interpretable kinetic metrics derived from TDNODE can significantly improve clinical decision-making in oncology.