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Artificial neural network model to predict the future trajectory of anatomical motion during cancer treatment
Meier, Kylie
Meier, Kylie
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2022-04-15
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Meier, Kylie. 2022.
Artificial neural network model to predict the future trajectory of anatomical motion during cancer treatment -- In Proceedings: 21st Annual Undergraduate Research and Creative Activity Forum. Wichita, KS: Wichita State University, p. 29
Abstract
Organ motion is a major challenge in the radiation treatment of lung and
abdominal cancers, which, if unaccounted for, may lead to the underdosing of cancer
cells or overdosing of normal tissue, potentially causing treatment failure or normaltissue
toxicity. Recent developments in the field of radiation therapy have allowed for a
real-time depiction of organ motion through MRI imaging to guide the radiation delivery
process. The goal of this research is to develop and test artificial neural network (ANN)
models to predict the future trajectory of anatomical motion during treatment. We employ
image-processing tools to extract a one-dimensional signal from the sequence of acquired
MRI images to describe the anatomical motion. We develop and train ANN models to
predict the signal amplitude at future time points using the Neural Net Time Series
toolbox in MATLAB. Using different prediction horizons, we apply the proposed ANN
model retrospectively to de-identified cancer patients and compare the prediction
accuracy against other autoregressive models for time-series prediction. The obtained
results show that the ANN model outperforms linear autoregressive model.
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Tie for second place winner of a oral presentation for Natural Sciences and Engineering at the 21st Undergraduate Research and Creative Activity Forum (URCAF) held at the Rhatigan Student Center, Wichita State University, April 15, 2022.
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Wichita State University
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URCAF
v.21
v.21