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Multiprocess dynamic modeling of tumor evolution with bayesian tumor-specific predictions.

Achilleas Achilleos1, Charalambos Loizides, Marios Hadjiandreou

  • 1KIOS Research Center for Intelligent Systems and Networks, Department of Electrical and Computer Engineering, University of Cyprus, P.O. Box 20537, Kallipoleos 75, Nicosia, Cyprus.

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We developed a new probabilistic model for predicting tumor growth. This stochastic approach uses all patient data for more accurate, individualized forecasts, outperforming deterministic methods.

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Area of Science:

  • Computational biology
  • Mathematical oncology
  • Biostatistics

Background:

  • Conventional tumor growth models often use deterministic methods, providing limited individualization.
  • Accurate tumor evolution forecasting is crucial for effective cancer treatment strategies.

Purpose of the Study:

  • To introduce a novel sequential probabilistic mixture model for individualized tumor growth forecasting.
  • To enhance tumor growth prediction by incorporating all available tumor-specific data over time.
  • To move beyond point estimates towards using full posterior and predictive distributions for inference.

Main Methods:

  • Developed a sequential probabilistic mixture model, a multi-process dynamic linear model.
  • Utilized prior population information, updated sequentially with individual tumor observations.
  • Employed stochastic modeling to approximate the multi-scale tumor growth process.
  • Validated the model using simulation studies and experimental data from mice.

Main Results:

  • The proposed probabilistic model demonstrated superiority over single-process deterministic alternatives in simulation studies.
  • The model effectively integrates prior knowledge with sequential, individualized tumor data.
  • Inference using full posterior and predictive distributions provides a more comprehensive understanding of tumor dynamics.

Conclusions:

  • The sequential probabilistic mixture model offers a robust framework for individualized tumor growth forecasting.
  • This approach enhances prediction accuracy by leveraging all available patient-specific data.
  • The methodology holds potential for developing personalized adaptive cancer treatment strategies.