Development of a clinical prediction model for overall survival in locally advanced head and neck squamous cell carcinoma
Abstract
To develop and validate a machine-learning–based prediction model for overall survival (OS) in locally advanced head and neck squamous cell carcinoma (LA-HNSCC) that integrates both surgery-based and chemoradiotherapy (CRT) cohorts within a single framework. A total of 1,005 LA-HNSCC patients receiving curative-intent surgery-based treatment or CRT were retrospectively assembled from a domestic multicenter cohort ( n = 678) and publicly available datasets from The Cancer Imaging Archive ( n = 327). Candidate predictors were pre-specified from routinely available clinical variables. Elastic net regularization with 10-fold cross-validation was applied for variable selection and weighting, followed by a multivariable Cox proportional hazards model. Elastic net selected 12 variables, of which p16 status, performance status, nodal stage, serum albumin, T stage, and age were most influential. The model showed good discrimination (C-index 0.73), with time-dependent AUCs of 0.78–0.73 across 1–5 years and good calibration at 1 and 3 years. Decision curve analysis confirmed clinical utility across relevant threshold probabilities in both cohorts. By relying on prognostic factors common to both surgery-based treatment and CRT, this interpretable model demonstrated stable performance and may support prognostic risk stratification applicable across treatment modalities in LA-HNSCC.
Related
- Development and internal validation of a pretreatment magnetic resonance imaging-based radiomics prediction model for 1-year overall survival in treatment-naïve patients with hepatocellular carcinoma receiving transarterial chemoembolization combined with targeted therapy and immune checkpoint inhibitors.
- Long-term survival with immune checkpoint inhibitors combined with bevacizumab in advanced clear cell renal cell carcinoma : A case report
- Development and validation of a metabolic syndrome-based nomogram to predict 1-year metastasis after radiotherapy in locally advanced nasopharyngeal carcinoma
- Preoperative prediction of microvascular invasion in hepatocellular carcinoma using routine clinical variables: development and temporal validation of an interpretable random forest model in a single-center retrospective cohort study.
- CBX4 and NOX4 Expression Are Associated With Chemoradiotherapy Response and Overall Survival in Locally Advanced Cervical Squamous Cell Carcinoma.
BibTeX
@article{koide2026development,
title = {{Development of a clinical prediction model for overall survival in locally advanced head and neck squamous cell carcinoma}},
author = {Yutaro Koide and Shunichi Ishihara and Kohei Wakabayashi and Souichiro Abe and Nobuhiro Hanai and Taijiro Ozawa and Makoto Ito and Hidetoshi Shimizu and Takahiro Aoyama and Masamune Noguchi and Hiroyuki Tachibana and Takeshi Kodaira},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-74101-6},
url = {https://www.nature.com/articles/s41598-026-74101-6},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Development of a clinical prediction model for overall survival in locally advanced head and neck squamous cell carcinoma
AU - Yutaro Koide
AU - Shunichi Ishihara
AU - Kohei Wakabayashi
AU - Souichiro Abe
AU - Nobuhiro Hanai
AU - Taijiro Ozawa
AU - Makoto Ito
AU - Hidetoshi Shimizu
AU - Takahiro Aoyama
AU - Masamune Noguchi
AU - Hiroyuki Tachibana
AU - Takeshi Kodaira
PY - 2026
DA - 2026/10/22
JO - Scientific Reports
DO - 10.1038/s41598-026-74101-6
UR - https://www.nature.com/articles/s41598-026-74101-6
AB - To develop and validate a machine-learning–based prediction model for overall survival (OS) in locally advanced head and neck squamous cell carcinoma (LA-HNSCC) that integrates both surgery-based and chemoradiotherapy (CRT) cohorts within a single framework. A total of 1,005 LA-HNSCC patients receiving curative-intent surgery-based treatment or CRT were retrospectively assembled from a domestic multicenter cohort ( n = 678) and publicly available datasets from The Cancer Imaging Archive ( n = 327). Candidate predictors were pre-specified from routinely available clinical variables. Elastic net regularization with 10-fold cross-validation was applied for variable selection and weighting, followed by a multivariable Cox proportional hazards model. Elastic net selected 12 variables, of which p16 status, performance status, nodal stage, serum albumin, T stage, and age were most influential. The model showed good discrimination (C-index 0.73), with time-dependent AUCs of 0.78–0.73 across 1–5 years and good calibration at 1 and 3 years. Decision curve analysis confirmed clinical utility across relevant threshold probabilities in both cohorts. By relying on prognostic factors common to both surgery-based treatment and CRT, this interpretable model demonstrated stable performance and may support prognostic risk stratification applicable across treatment modalities in LA-HNSCC.
SN - 2045-2322
ER -