Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction

Joel Tosado; Luka Zdilar; Hesham Elhalawani; Baher Elgohari; David M Vock; G Elisabeta Marai; Clifton Fuller; Abdallah S R Mohamed; Guadalupe Canahuate

doi:10.1038/s41598-020-60140-0

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Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction

Journal article

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Peer reviewed

Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction

Joel Tosado, Luka Zdilar, Hesham Elhalawani, Baher Elgohari, David M Vock, G Elisabeta Marai, Clifton Fuller, Abdallah S R Mohamed and Guadalupe Canahuate

Scientific reports, Vol.10(1), pp.3811-3811

03/02/2020

DOI: 10.1038/s41598-020-60140-0

PMCID: PMC7051972

PMID: 32123193

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Abstract

Clustering is the task of identifying groups of similar subjects according to certain criteria. The AJCC staging system can be thought as a clustering mechanism that groups patients based on their disease stage. This grouping drives prognosis and influences treatment. The goal of this work is to evaluate the efficacy of machine learning algorithms to cluster the patients into discriminative groups to improve prognosis for overall survival (OS) and relapse free survival (RFS) outcomes. We apply clustering over a retrospectively collected data from 644 head and neck cancer patients including both clinical and radiomic features. In order to incorporate outcome information into the clustering process and deal with the large proportion of censored samples, the feature space was scaled using the regression coefficients fitted using a proxy dependent variable, martingale residuals, instead of follow-up time. Two clusters were identified and evaluated using cross validation. The Kaplan Meier (KM) curves between the two clusters differ significantly for OS and RFS (p-value < 0.0001). Moreover, there was a relative predictive improvement when using the cluster label in addition to the clinical features compared to using only clinical features where AUC increased by 5.7% and 13.0% for OS and RFS, respectively.

Cluster Analysis

Computational Biology

Female

Head and Neck Neoplasms - diagnosis

Head and Neck Neoplasms - pathology

Humans

Male

Middle Aged

Neoplasm Staging

Prognosis

Supervised Machine Learning

Details

Title: Subtitle: Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction
Creators: Joel Tosado - University of Iowa
Luka Zdilar - University of Iowa
Hesham Elhalawani - University of Texas MD Anderson Cancer Center
Baher Elgohari - University of Texas MD Anderson Cancer Center
David M Vock - University of Minnesota
G Elisabeta Marai - University of Illinois Chicago
Clifton Fuller - University of Texas MD Anderson Cancer Center
Abdallah S R Mohamed - University of Texas MD Anderson Cancer Center
Guadalupe Canahuate - University of Iowa
Resource Type: Journal article
Publication Details: Scientific reports, Vol.10(1), pp.3811-3811
DOI: 10.1038/s41598-020-60140-0
PMID: 32123193
PMCID: PMC7051972
NLM abbreviation: Sci Rep
ISSN: 2045-2322
eISSN: 2045-2322
Grant note: R01CA214825 / U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) P30CA016672 / U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) R25 EB025787 / NIBIB NIH HHS R01CA225190 / National Science Foundation (NSF)
Language: English
Date published: 03/02/2020
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984197450702771

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