Genentech researchers introduced tumor dynamic neural ODE (TDNODE) as a pharmacology-based neural network to improve tumor dynamic modeling in oncology drug development. Overcoming the limitations of existing models, TDNODE enables unbiased predictions from truncated data. Its encoder-decoder architecture expresses an underlying dynamic law with generalized homogeneity, representing kinetic velocity metrics with inverse time as unit. The generated metrics accurately predict overall patient survival, showing the utility of TDNODE in principled oncology disease modeling and improving personalized therapy decision making.
TDNODE’s encoder-decoder architecture expresses a homogeneous dynamic law over time, generating metrics for accurate predictions of patients’ overall survival. The proposed formalism enables the integration of multimodal dynamic dataset principles into oncological disease modeling. The study specifies the dimensions for the output of the initial condition encoder and the GRU hidden layers. The implementation uses torchdiffeq, PyTorch, Pandas, Numpy, Scipy, Lifelines, Shap and Matplotlib for solving, development and analysis.
The study explores the dynamics of tumor growth using mathematical models, emphasizing the historical success of such models in describing experimental data. While nonlinear mixed-effects modeling is common in pharmacometrics, machine learning has been underutilized to derive metrics. The TDNODE framework integrates neural ODEs and ML, aiming to mine large oncology data sets for accurate predictions and better understanding. The study aims to predict future patient outcomes early, enabling personalized therapy and advancing drug development through interpretable machine learning models.
TDNODE is a system that uses two encoders and one decoder based on an ODE solver. It employs a recurrent neural network to determine initial conditions and an attention-based LSTM to evaluate tumor kinetic parameters. Using numerical integration, the decoder represents the ODE system as a neural network and predicts tumor size over time. The Reducer component condenses the state vector to compare it to the tumor size.
The TDNODE model overcomes existing limitations by making unbiased predictions from truncated data and generating kinetic rate metrics for highly accurate overall survival predictions. TDNODE integrated multimodal dynamic datasets into oncology disease modeling, demonstrating its versatility and providing a principled approach to combining diverse data types. Continuous predictions of longitudinal tumor size were generated for training and test sets, employing an ADAM optimization approach for 150 epochs with specific hyperparameters, achieving accurate predictions through careful configuration of the L2 weight decay, learning rate , the ODE tolerance, the batch size and the observation window.
By using kinetic rate metrics, TDNODE can provide highly accurate predictions of survival rates even when working with incomplete or truncated data sets. This advanced approach overcomes the limitations of traditional survival analysis methods, which often must be able to accurately account for incomplete or missing data. With TDNODE’s cutting-edge technology, researchers and healthcare professionals can gain a more detailed understanding of patient outcomes, leading to better-informed treatment decisions and better clinical outcomes.
Other avenues of research for TDNODE include exploring the incorporation of dosing or pharmacokinetic factors and improving the completeness of the model. Validation on diverse data sets will evaluate the generalizability of TDNODE to predict future tumor sizes. Investigating the potential of TDNODE in personalized therapy is a promising direction, leveraging its capacity for model discovery from longitudinal tumor data to support individualized treatment decisions. Exploring TDNODE in disease modeling beyond oncology could offer insights into its applicability and effectiveness in various medical contexts.
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Hello, my name is Adnan Hassan. I’m a consulting intern at Marktechpost and soon to be a management trainee at American Express. I am currently pursuing a double degree from the Indian Institute of technology, Kharagpur. I am passionate about technology and I want to create new products that make a difference.
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