ArticleScientific reports2025
Survival and data-driven phenotypes in head and neck cancer.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07739771 (Retrospective Evaluation of Severe Oral Mucositis Incidence, Time to Onset and Functional Impact in Quality of Life in Head and Neck Cancer Radiated and Chemo-radiated Patients. Part of the Stop Oral Mucositis Project), which is not on this map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Retrospective Evaluation of Severe Oral Mucositis Incidence, Time to Onset and Functional Impact in Quality of Life in Head and Neck Cancer Radiated and Chemo-radiated Patients. Part of the Stop Oral Mucositis Project (STOPOMP)
Who cites it
10 citing papers in PubMed.
- Extracellular Matrix-MYCAF Signatures Correlate with Resistance to Neoadjuvant aPD-L1 Immune Checkpoint Inhibition with Durvalumab + Metformin in HPV+ HNSCC.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Trial
- Data-driven biopsychosocial profiles to inform early psycho-oncology triage in head and neck cancer : A cross-sectional cluster analysis integrating patient-reported outcomes, medical complexity, and early psychosocial care utilization in an interdisciplinary fast-track diagnostic program.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Article
- Tumor Biomarkers in Head and Neck Squamous Cell Carcinoma: From Etiology and Pathogenesis to Treatment Response-A Scoping Review.Biomedicines · 2026Review
- Metabolic and Clinical-Nutritional Correlation Patterns in Relation to 3-Year Disease-Free Survival in Locally Advanced Head and Neck Squamous Cell Carcinoma: A Preliminary Analysis.International journal of molecular sciences · 2026Article
- Investigation of metformin analogues in targeting signaling molecules of head and neck cancer pathways by computational approaches.Scientific reports · 2026Article
- Validating cancer-specific and generic quality of life (QoL) instruments among patients with head and neck cancers: incorporating the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Head and Neck Module with generic QoL instruments.Psicologia, reflexao e critica : revista semestral do Departamento de Psicologia da UFRGS · 2026Article
- Gallic acid chemoprevention of oral carcinogenesis is associated with HSD11β2 upregulation and immune remodeling.Scientific reports · 2026Article
- Reprogramming the Tumor Microenvironment in Head and Neck Squamous Cell Carcinoma: Therapeutic Targets and Innovations.Oncology research · 2025Review
- The role of the tumor microenvironment in HNSCC resistance and targeted therapy.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Head and neck cancer (HNC) is the seventh most common cancer worldwide, with a 5-year overall survival of 50-60%. Despite known survival factors, unexpected deaths underscore the need to refine pre-treatment survival predictions. This study aimed to identify novel data-driven HNC patient phenotypes, associated features and predict overall survival using deep survival clustering model VaDeSC. We used a retrospective cohort of 1341 HNC patients from Helsinki University Hospital, utilizing their pre-treatment clinical and demographic data from electronic health records. We identified six previously unrecognized HNC phenotypes with distinct survival patterns, highlighting the significance of BMI, sleep apnoea, TNM stage, tumour site, treatment intention, gender, and age-related treatment modalities. VaDeSC demonstrated strong predictive accuracy, clustering performance and generalizability, achieving C-index of 0.895 ± 0.015 on training and 0.782 ± 0.013 on test data. The phenotype purity was 0.102 ± 0.11 on training-validation data and 0.143 ± 0.006 on test data. Our findings demonstrated that clustering pre-treatment clinical survival data offers intuitively interpretable results with accurate individualised predictions. This approach holds significant potential for novel phenotype discovery across various diseases and endpoints.
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