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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Chemotherapy response prediction with diffuser elapser network.

Batuhan Koyuncu1,2, Ahmet Melek3,2, Defne Yilmaz4,2

  • 1Department of Computer Engineering, Bogazici University, Istanbul, 34342, Turkey.

Scientific Reports
|February 1, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model to predict chemotherapy response in solid tumors. The model forecasts tumor microenvironment changes, aiding in personalized treatment scheduling for improved drug delivery.

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Area of Science:

  • Oncology
  • Computational Biology
  • Medical Imaging

Background:

  • Solid tumors exhibit high fluid pressure and poor perfusion, hindering drug delivery.
  • Antiangiogenic therapy can normalize tumor vasculature, improving drug penetration.
  • Optimizing combination therapy scheduling (antiangiogenics + chemotherapy) is crucial for effective cancer treatment.

Purpose of the Study:

  • To develop a deep learning model for predicting patient-specific responses to chemotherapy treatment schedules.
  • To provide a framework for optimizing the timing and dosage of cytotoxic drugs in combination cancer therapy.
  • To simulate tumor microenvironment dynamics under various treatment scenarios.

Main Methods:

  • Utilized an in-silico tumor microenvironment model including tumor layer, vasculature, interstitial fluid pressure, and drug diffusion.
  • Developed a deep learning framework employing multiple convolutional neural network submodels.
  • Predicted future tumor microenvironment maps and tumor cell density.

Main Results:

  • The model achieved high accuracy in predicting tumor microenvironment maps seven days ahead, with an average structural similarity score of 0.973 and peak signal-to-noise ratio of 35.41.
  • Predicted tumor cell density at day 7 showed a mean absolute percentage error of [Formula: see text].
  • Demonstrated the capability of deep learning to simultaneously capture tumor growth and drug response.

Conclusions:

  • The developed deep learning model shows promise for predicting chemotherapy response and optimizing treatment strategies.
  • This approach can facilitate personalized medicine by enabling patient-specific treatment schedule decisions.
  • Further research can build upon this proof-of-concept for enhanced anticancer drug delivery and efficacy.