Artificial Neural Networks for Prediction of Response to Chemoradiation in HT29 Xenografts

Kakar, Manish; Seierstad, Therese; Roe, Kathrine; Olsen, Dag Rune

doi:10.1016/j.ijrobp.2009.05.036

Title: Artificial Neural Networks for Prediction of Response to Chemoradiation in HT29 Xenografts

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Abstract

Purpose: To evaluate the feasibility of using neural networks for predicting treatment response by using longitudinal measurements of apparent diffusion coefficient (ADC) obtained from diffusion-weighted magnetic resonance imaging (DWMRI). Methods and Materials: Mice bearing HT29 xenografts were allocated to six treatment groups receiving different combinations of daily chemotherapy and/or radiation therapy for 2 weeks. T{sub 2}-weighted and DWMR images were acquired before treatment, twice during fractionated chemoradiation (at days 4 and 11), and four times after treatment ended (at days 18, 25, 32, and 46). A tumor doubling growth delay (T{sub delay}) value was found for individual xenografts. ADC values and treatment groups (1-6) were used as input to a back propagation neural network (BPNN) to predict T{sub delay}. Results: When treatment group and ADC values from days 0, 4, 11, 18, 25, 32, and 46 were used as inputs to the BPNN, a strong correlation between measured and predicted T{sub delay} values was found (R = 0.731, p < 0.01). When ADC values from days 0, 4, and 11, and the treatment group were used as inputs, the correlation between predicted and measured T{sub delay} was 0.693 (p < 0.01). Conclusions: BPNN was successfully used to predict T{sub delay}more »« less

Authors:

Kakar, Manish ^[1]; Seierstad, Therese ^[1]; Roe, Kathrine ^[1]; Olsen, Dag Rune ^[1]

Department of Radiation Biology, Institute for Cancer Research, Oslo University Hospital, Oslo (Norway)

Publication Date:: Thu Oct 01 00:00:00 EDT 2009

OSTI Identifier:: 21282054

Resource Type:: Journal Article

Journal Name:: International Journal of Radiation Oncology, Biology and Physics

Additional Journal Information:: Journal Volume: 75; Journal Issue: 2; Other Information: DOI: 10.1016/j.ijrobp.2009.05.036; PII: S0360-3016(09)00820-7; Copyright (c) 2009 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0360-3016

Country of Publication:: United States

Language:: English

Subject:: 62 RADIOLOGY AND NUCLEAR MEDICINE; CHEMOTHERAPY; FORECASTING; MICE; NEOPLASMS; NEURAL NETWORKS; NMR IMAGING; RADIOTHERAPY

Citation Formats


                    Kakar, Manish, Seierstad, Therese, Buskerud University College, Department of Health Sciences, Drammen, Roe, Kathrine, Olsen, Dag Rune, and Department of Physics, University of Oslo, Oslo. Artificial Neural Networks for Prediction of Response to Chemoradiation in HT29 Xenografts.  United States: N. p., 2009. 
        Web.  doi:10.1016/j.ijrobp.2009.05.036.

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                    Kakar, Manish, Seierstad, Therese, Buskerud University College, Department of Health Sciences, Drammen, Roe, Kathrine, Olsen, Dag Rune, & Department of Physics, University of Oslo, Oslo. Artificial Neural Networks for Prediction of Response to Chemoradiation in HT29 Xenografts.  United States.  https://doi.org/10.1016/j.ijrobp.2009.05.036

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                    Kakar, Manish, Seierstad, Therese, Buskerud University College, Department of Health Sciences, Drammen, Roe, Kathrine, Olsen, Dag Rune, and Department of Physics, University of Oslo, Oslo. 2009.  
        "Artificial Neural Networks for Prediction of Response to Chemoradiation in HT29 Xenografts".  United States.  https://doi.org/10.1016/j.ijrobp.2009.05.036.

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@article{osti_21282054,

  title        = {Artificial Neural Networks for Prediction of Response to Chemoradiation in HT29 Xenografts},

  author       = {Kakar, Manish and Seierstad, Therese and Buskerud University College, Department of Health Sciences, Drammen and Roe, Kathrine and Olsen, Dag Rune and Department of Physics, University of Oslo, Oslo},

  abstractNote = {Purpose: To evaluate the feasibility of using neural networks for predicting treatment response by using longitudinal measurements of apparent diffusion coefficient (ADC) obtained from diffusion-weighted magnetic resonance imaging (DWMRI). Methods and Materials: Mice bearing HT29 xenografts were allocated to six treatment groups receiving different combinations of daily chemotherapy and/or radiation therapy for 2 weeks. T{sub 2}-weighted and DWMR images were acquired before treatment, twice during fractionated chemoradiation (at days 4 and 11), and four times after treatment ended (at days 18, 25, 32, and 46). A tumor doubling growth delay (T{sub delay}) value was found for individual xenografts. ADC values and treatment groups (1-6) were used as input to a back propagation neural network (BPNN) to predict T{sub delay}. Results: When treatment group and ADC values from days 0, 4, 11, 18, 25, 32, and 46 were used as inputs to the BPNN, a strong correlation between measured and predicted T{sub delay} values was found (R = 0.731, p < 0.01). When ADC values from days 0, 4, and 11, and the treatment group were used as inputs, the correlation between predicted and measured T{sub delay} was 0.693 (p < 0.01). Conclusions: BPNN was successfully used to predict T{sub delay} from tumor ADC values obtained from HT29 xenografts undergoing fractionated chemoradiation therapy.},

  doi          = {10.1016/j.ijrobp.2009.05.036},

  url          = {https://www.osti.gov/biblio/21282054},
  journal      = {International Journal of Radiation Oncology, Biology and Physics},

  issn         = {0360-3016},

  number       = 2,

  volume       = 75,

  place        = {United States},

  year         = {Thu Oct 01 00:00:00 EDT 2009},

  month        = {Thu Oct 01 00:00:00 EDT 2009}

}

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