Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy

Andrew Wentzel; Peter Hanula; Lisanne V van Dijk; Baher Elgohari; Abdallah S.R Mohamed; Carlos E Cardenas; Clifton D Fuller; David M Vock; Guadalupe Canahuate; G. Elisabeta Marai

doi:10.1016/j.radonc.2020.05.023

Back

Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy

Journal article

Peer reviewed

Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy

Andrew Wentzel, Peter Hanula, Lisanne V van Dijk, Baher Elgohari, Abdallah S.R Mohamed, Carlos E Cardenas, Clifton D Fuller, David M Vock, Guadalupe Canahuate and G. Elisabeta Marai

Radiotherapy and oncology, Vol.148, pp.245-251

07/2020

DOI: 10.1016/j.radonc.2020.05.023

PMCID: PMC7390671

PMID: 32422303

View Online

Abstract

Purpose Using a 200 Head and Neck cancer (HNC) patient cohort, we employ patient similarity based on tumor location, volume, and proximity to organs at risk to predict radiation-associated dysphagia (RAD) in a new patient receiving intensity modulated radiation therapy (IMRT). Material and methods All patients were treated using curative-intent IMRT. Anatomical features were extracted from contrast-enhanced tomography scans acquired pre-treatment. Patient similarity was computed using a topological similarity measure, which allowed for the prediction of normal tissues’ mean doses. We performed feature selection and clustering, and used the resulting groups of patients to forecast RAD. We used Logistic Regression (LG) cross-validation to assess the potential toxicity risk of these groupings. Results Out of 200 patients, 34 patients were recorded as having RAD. Patient clusters were significantly correlated with RAD (p < .0001). The area under the receiver-operator curve (AUC) using pre-established, baseline features gave a predictive accuracy of 0.79, while the addition of our cluster labels improved accuracy to 0.84. Conclusion Our results show that spatial information available pre-treatment can be used to robustly identify groups of RAD high-risk patients. We identify feature sets that considerably improve toxicity risk prediction beyond what is possible using baseline features. Our results also suggest that similarity-based predicted mean doses to organs can be used as valid predictors of risk to organs.

Medical Informatics

Data Interpretation

Dysphagia

Head and Neck cancer

Intensity-modulated radiotherapy

Oropharynx cancer

Radiation therapy

Statistical

Tumor location

Details

Title: Subtitle: Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy
Creators: Andrew Wentzel - University of Illinois Chicago
Peter Hanula - University of Illinois Chicago
Lisanne V van Dijk - The University of Texas MD Anderson Cancer Center
Baher Elgohari - Mansoura University
Abdallah S.R Mohamed - The University of Texas MD Anderson Cancer Center
Carlos E Cardenas - The University of Texas MD Anderson Cancer Center
Clifton D Fuller - The University of Texas MD Anderson Cancer Center
David M Vock - University of Minnesota
Guadalupe Canahuate - University of Iowa
G. Elisabeta Marai - University of Illinois Chicago
Resource Type: Journal article
Publication Details: Radiotherapy and oncology, Vol.148, pp.245-251
DOI: 10.1016/j.radonc.2020.05.023
PMID: 32422303
PMCID: PMC7390671
NLM abbreviation: Radiother Oncol
ISSN: 0167-8140
eISSN: 1879-0887
Grant note: DOI: 10.13039/100000001, name: National Science Foundation; DOI: 10.13039/100000002, name: National Institutes of Health
Language: English
Date published: 07/2020
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984197532902771

Metrics

16 Record Views

24 Times Cited - Web of Science

See more details