Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction
Year: 2020
Type: article
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.
Source: Scientific Reports
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,870
Funders: National Science Foundation, U.S. Department of Health and Human Services, Elekta, Andrew Sabin Family Foundation, Egyptian Cultural and Educational Bureau, Feinberg Foundation, National Institutes of Health, Directorate for Mathematical and Physical Sciences, University of Texas MD Anderson Cancer Center, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institute of Dental and Craniofacial Research, Division of Mathematical Sciences
Awards: G111615301, G1336552892, G1442369977, G1527481403, G1607744604, G1858909031, G190039562, G203557288, G2164129353, G2326049341, G2747889112, M D Anderson Cancer Center Head & Neck SPORE, G2816209679, G293715275, G3325949621, G3518590350, G3670257500, G380820829, G4292215687, SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment, G458618165, G4660434062, Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Establish Objective Clinical Outcome Measures for Mandibular Osteoradionecrosis, G5275117371, QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data , SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment, G5499366301, G5598715163, QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data , SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment, G5721054265, G6131072705, G6582163824, G6651941027, G6742822672, G7087022923, CCSG supplement: HOPE/CARE, G7400934742, imaging Radiation-Associated Dysphagia (iRAD), QuBBD: Collaborative Research: SMART -- Spatial-Nonspatial Multidimensional Adaptive Radiotherapy Treatment, G816801620, G8294977537, G8401789219, G842568704, G9000003559, G9000012603, G9000019672, G9000025114, G9000034222, G9000072279, G9000091839, G9000101031, G9000117382, G9000146054, G9000297587, G941367620, G9625747
OpenAlex ID: W3010135971