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Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes

Authors:

Guadalupe M Canahuate,

Lisanne V Van Dijk,

Abdallah S. R. Mohamed,

Clifton David Fuller,

Georgeta-Elisabeta MaraiAuthors Info & Claims

IDEAS '21: Proceedings of the 25th International Database Engineering & Applications Symposium

Pages 273 - 279

https://doi.org/10.1145/3472163.3472177

Published: 07 September 2021 Publication History

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Abstract

Patient-Reported Outcome (PRO) surveys are used to monitor patients’ symptoms during and after cancer treatment. Late symptoms refer to those experienced after treatment. While most patients experience severe symptoms during treatment, these usually subside in the late stage. However, for some patients, late toxicities persist negatively affecting the patient’s quality of life (QoL). In the case of head and neck cancer patients, PRO surveys are recorded every week during the patient’s visit to the clinic and at different follow-up times after the treatment has concluded. In this paper, we model the PRO data as a time-series and apply Long-Short Term Memory (LSTM) neural networks for predicting symptom severity in the late stage. The PRO data used in this project corresponds to MD Anderson Symptom Inventory (MDASI) questionnaires collected from head and neck cancer patients treated at the MD Anderson Cancer Center. We show that the LSTM model is effective in predicting symptom ratings under the RMSE and NRMSE metrics. Our experiments show that the LSTM model also outperforms other machine learning models and time-series prediction models for these data.

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References

[1]

Ratnadip Adhikari and R. K. Agrawal. 2013. An Introductory Study on Time Series Modeling and Forecasting. CoRR abs/1302.6613(2013). arxiv:1302.6613http://arxiv.org/abs/1302.6613

[2]

A. Aktas, D. Walsh, and L. Rybicki. 2010. Symptom clusters: myth or reality?Palliative medicine 24(4) (2010), 373––385. https://doi.org/10.1177/0269216310367842

[3]

Stef Buuren and Catharina Groothuis-Oudshoorn. 2011. MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software 45 (12 2011). https://doi.org/10.18637/jss.v045.i03

[4]

Kaitlin M. Christopherson, Alokananda Ghosh, Abdallah Sherif Radwan Mohamed, 2019. Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles. Clin. Transl. Rad. Onc. 18 (Sept. 2019), 16–22. https://doi.org/10.1016/j.ctro.2019.06.005

[5]

C. S. Cleeland, T. R. Mendoza, X. S. Wang, 2000. Assessing symptom distress in cancer patients: the M.D. Anderson Symptom Inventory. Cancer 89(7)(2000), 1634––1646. https://doi.org/10.1002/1097-0142(20001001)89:7<1634::aid-cncr29>3.0.co;2-v

[6]

S. J. Coons, S. Eremenco, J. J. Lundy, 2015. Capturing Patient-Reported Outcome (PRO) Data Electronically: The Past, Present, and Promise of ePRO Measurement in Clinical Trials. The patient 8(4)(2015). https://doi.org/10.1007/s40271-014-0090-z

[7]

S. T. Dong, D. S. Costa, P. N. Butow, 2016. Symptom Clusters in Advanced Cancer Patients: An Empirical Comparison of Statistical Methods and the Impact on Quality of Life. Journal of pain and symptom management 51(1) (2016), 88––98. https://doi.org/10.1016/j.jpainsymman.2015.07.013

[8]

Hesham Elhalawani, Abdallah SR Mohamed, Aubrey L White, 2017. Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges. Scientific data 4(2017), 170077.

[9]

Steven Elsworth and Stefan Güttel. 2020. Time Series Forecasting Using LSTM Networks: A Symbolic Approach. arxiv:2003.05672 [cs.LG]

[10]

S. A. Eraj, M. K. Jomaa, C. D. Rock, 2017. Long-term patient reported outcomes following radiation therapy for oropharyngeal cancer: cross-sectional assessment of a prospective symptom survey in patients ≥ 65 years old. Rad. onc. 12(1)(2017). https://doi.org/10.1186/s13014-017-0878-9

[11]

G. Fan, L. Filipczak, and E. Chow. 2007. Symptom clusters in cancer patients: a review of the literature. Current oncology (Toronto, Ont.) 14(5) (2007), 173––179. https://doi.org/10.3747/co.2007.145

[12]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-term Memory. Neural computation 9 (12 1997), 1735–80. https://doi.org/10.1162/neco.1997.9.8.1735

Digital Library

[13]

Rudolph Emil Kalman. 1960. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME–Journal of Basic Engineering 82, Series D(1960), 35–45.

[14]

M. Kamal, M. P. Barrow, J. S. Lewin, 2019. Modeling symptom drivers of oral intake in long-term head and neck cancer survivors. Supportive care in cancer 27(4) (2019), 1405–1415. https://doi.org/10.1007/s00520-018-4434-4

[15]

Shruti Kaushik, Abhinav Choudhury, Pankaj Kumar Sheron, 2020. AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures. Frontiers in Big Data 3(2020), 4. https://doi.org/10.3389/fdata.2020.00004

[16]

Zachary C. Lipton, David C. Kale, Charles Elkan, 2017. Learning to Diagnose with LSTM Recurrent Neural Networks. arxiv:1511.03677 [cs.LG]

[17]

Timothy Luciani, Andrew Wentzel, Baher Elgohari, 2020. A spatial neighborhood methodology for computing and analyzing lymph node carcinoma similarity in precision medicine. J. Biomed. Informatics 5(2020), 100067.

[18]

G. Maragatham and S Devi. 2019. LSTM Model for Prediction of Heart Failure in Big Data. J Med Syst 111(2019). https://doi.org/10.1007/s10916-019-1243-3

[19]

G. E. Marai, C. Ma, A. T. Burks, 2019. Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots. IEEE Trans. Vis. Comp. Graphics 25(4) (2019), 1732––1745. https://doi.org/10.1109/TVCG.2018.2817557

[20]

Steffen Moritz and Thomas Bartz-Beielstein. 2017. ImputeTS: Time Series Missing Value Imputation in R. The R Journal 9, 1 (2017), 207–218. https://doi.org/10.32614/RJ-2017-009

[21]

Steffen Moritz, Alexis Sardá, Thomas Bartz-Beielstein, 2015. Comparison of different Methods for Univariate Time Series Imputation in R. arxiv:1510.03924 [stat.AP]

[22]

D. I. Rosenthal, T. R. Mendoza, M. S. Chambers, 2007. Measuring head and neck cancer symptom burden: the development and validation of the M. D. Anderson symptom inventory, head and neck module. Head & neck 29(10)(2007), 923––931. https://doi.org/10.1002/hed.20602

[23]

D. I. Rosenthal, T. R. Mendoza, C. D. Fuller, 2014. Patterns of symptom burden during radiotherapy or concurrent chemoradiotherapy for head and neck. Cancer 120(13)(2014), 1975–1984. https://doi.org/10.1002/cncr.28672

[24]

T. Sheu, D. Vock, A. Mohamed, 2017. Conditional Survival Analysis of Patients With Locally Advanced Laryngeal Cancer: Construction of a Dynamic Risk Model and Clinical Nomogram. Scientific Reports (2017). https://doi.org/10.1038/srep43928

[25]

H. M. Skerman, P. M. Yates, and D. Battistutta. 2009. Multivariate methods to identify cancer-related symptom clusters. Res. Nursing & Health 32(3) (2009), 345––360. https://doi.org/10.1002/nur.20323

[26]

J. Tosado, L. Zdilar, H. Elhalawani, 2020. Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction. Scientific reports 10(1)(2020). https://doi.org/10.1038/s41598-020-60140-0

[27]

A. Wentzel, P. Hanula, T. Luciani, 2020. Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration. IEEE Trans. Vis. and Comp. Graphics 26, 1 (Jan. 2020), 949–959. https://doi.org/10.1109/TVCG.2019.2934546

[28]

Andrew Wentzel, Peter Hanula, Lisanne V van Dijk, 2020. Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy. Radiotherapy and Oncology 148 (2020), 245–251.

[29]

Luka Zdilar, David M Vock, G Elisabeta Marai, 2018. Evaluating the effect of right-censored end point transformation for radiomic feature selection of data from patients with oropharyngeal cancer. JCO clinical cancer informatics 2 (2018), 1–19.

[30]

Zizhao Zhang, Yuanpu Xie, Fuyong Xing, 2017. MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network. CoRR abs/1707.02485(2017). arxiv:1707.02485http://arxiv.org/abs/1707.02485

Cited By

Zeinali NYoun NAlbashayreh AFan WGilbertson White S(2024)Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic ReviewJMIR Cancer10.2196/5232210(e52322)Online publication date: 19-Mar-2024
https://doi.org/10.2196/52322
Anyimadu EFuller CZhang XMarai GCanahuate G(2024)Collaborative Filtering for the Imputation of Patient Reported OutcomesDatabase and Expert Systems Applications10.1007/978-3-031-68309-1_20(231-248)Online publication date: 18-Aug-2024
https://doi.org/10.1007/978-3-031-68309-1_20
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Published In

cover image ACM Other conferences

IDEAS '21: Proceedings of the 25th International Database Engineering & Applications Symposium

July 2021

308 pages

ISBN:9781450389914

DOI:10.1145/3472163

Editors:
Bipin C. Desai
Concordia University, Canada
,
Bipin C. Desai
Concordia University, Canada
,
Jeffrey Ullman
Stanford University, USA
,
Richard McClatchey
Univ. Of West England, U.K
,
Motomichi Toyoma
Keio University, Japan

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2021

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IDEAS 2021

IDEAS 2021: 25th International Database Engineering & Applications Symposium

July 14 - 16, 2021

QC, Montreal, Canada

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Overall Acceptance Rate 74 of 210 submissions, 35%

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Cited By

Zeinali NYoun NAlbashayreh AFan WGilbertson White S(2024)Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic ReviewJMIR Cancer10.2196/5232210(e52322)Online publication date: 19-Mar-2024
https://doi.org/10.2196/52322
Anyimadu EFuller CZhang XMarai GCanahuate G(2024)Collaborative Filtering for the Imputation of Patient Reported OutcomesDatabase and Expert Systems Applications10.1007/978-3-031-68309-1_20(231-248)Online publication date: 18-Aug-2024
https://doi.org/10.1007/978-3-031-68309-1_20
Wójcik ZDimitrova VWarrington LVelikova GAbsolom K(2024)Patient-Centric Approach for Utilising Machine Learning to Predict Health-Related Quality of Life Changes During ChemotherapyArtificial Intelligence in Medicine10.1007/978-3-031-66538-7_12(101-116)Online publication date: 25-Jul-2024
https://doi.org/10.1007/978-3-031-66538-7_12
Zhou FHu SWan XLu ZWu J(2023)Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing HomesElectronics10.3390/electronics1215320612:15(3206)Online publication date: 25-Jul-2023
https://doi.org/10.3390/electronics12153206
Floricel CWentzel AMohamed AFuller CCanahuate GMarai G(2023)Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule MiningIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332693930:1(1227-1237)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3326939
Or Rashid MShahriyar SShamrat FMahbub TTasnim ZAhmed M(2023)A Convolutional Neural Network Based Classification Approach for Breast Cancer Detection2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)10.1109/ICOEI56765.2023.10125783(761-768)Online publication date: 11-Apr-2023
https://doi.org/10.1109/ICOEI56765.2023.10125783
Wang YDijk LMohamed ANaser MFuller CZhang XMarai GCanahuate G(2023)Improving Prediction of Late Symptoms using LSTM and Patient-reported Outcomes for Head and Neck Cancer Patients2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI57859.2023.00047(292-300)Online publication date: 26-Jun-2023
https://doi.org/10.1109/ICHI57859.2023.00047
Cheng ESteinhardt RBen Miled Z(2022)Predicting Childhood Obesity Using Machine Learning: Practical ConsiderationsBioMedInformatics10.3390/biomedinformatics20100122:1(184-203)Online publication date: 8-Mar-2022
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