Evaluating Dimensionality Reduction for Patient-Reported Outcome-Based Survival Modeling in Patients With Head and Neck Cancer

Authors

Eric Ababio Anyimadu
Yaohua Wang
Amy C Moreno
Clifton David Fuller
Xinhua Zhang
G Elisabeta Marai
Guadalupe M Canahuate

Language

English

Publication Date

10-1-2025

Journal

JCO Clinical Cancer Informatics

DOI

10.1200/CCI-25-00069

PMID

41092280

PMCID

PMC12529990

PubMedCentral® Posted Date

10-15-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Purpose: This study aims to improve survival modeling in head and neck cancer (HNC) by integrating patient-reported outcomes (PROs) using dimensionality reduction techniques. PROs capture symptom severity across the treatment timeline and offer key insights for personalized care. However, their high dimensionality poses challenges such as overfitting and computational complexity. This work focuses on transforming and incorporating PRO data to enhance model performance in HNC.

Materials and methods: We analyzed retrospective data of 923 patients with HNC treated at the University of Texas MD Anderson Cancer Center between 2010 and 2021. Baseline clinical data including demographic, treatment, and disease characteristics were used to build a reference survival model. PRO data, capturing symptom ratings, were integrated using dimensionality reduction techniques: principal component analysis (PCA), autoencoders (AEs), and patient clustering. These reduced representations, combined with clinical data, were input into Cox proportional hazards models to predict overall survival (OS) and progression-free survival (PFS). Model performance was assessed using the concordance index, time-dependent AUC, Brier score for calibration, and hazard ratios for predictor significance.

Results: Cox models incorporating PCA and AE outperformed the clinical-only reference model for both OS and PFS. The PCA-based model achieved the highest C-indices (0.74 for OS and 0.64 for PFS), followed by the AE model (0.73 and 0.63) and the clustering model (0.72 and 0.62). Time-dependent AUCs reinforced these results, with PCA showing the highest average AUC over 36 months. All models were well-calibrated, with low Brier scores. Key predictors included age, disease stage, and tumor subsite.

Conclusion: Dimensionality reduction techniques improve survival prediction in patients with HNC by effectively incorporating PRO data, potentially providing greater insights into more personalized treatment strategies.

Keywords

Humans, Patient Reported Outcome Measures, Head and Neck Neoplasms, Male, Female, Middle Aged, Retrospective Studies, Aged, Adult, Prognosis, Dimensionality Reduction

Published Open-Access

yes

Recommended Citation

Anyimadu, Eric Ababio; Wang, Yaohua; Moreno, Amy C; et al., "Evaluating Dimensionality Reduction for Patient-Reported Outcome-Based Survival Modeling in Patients With Head and Neck Cancer" (2025). Faculty, Staff and Student Publications. 5656.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5656