ArticleScientific reports2024
Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Artificial intelligence in otolaryngology: current applications, limitations, and future perspectives.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Review
- Clinical Applications of Multimodal Artificial Intelligence in Otolaryngology: A State-of-the-Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Review
- Performance of Machine Learning Models for Prognosis Prediction in Oral Cavity Squamous Cell Carcinoma: A Systematic Review.Cancers · 2026Review
- Temporal Trends in Cause-Specific Cardiovascular Disease Mortality Following Cancer Diagnosis.Cancer medicine · 2026Article
- Human versus artificial intelligence in oral pathology diagnosis: a comparative study of ChatGPT, Grok, and MANUS.Scientific reports · 2026Article
- A High Magnifications Histopathology Image Dataset for Oral Squamous Cell Carcinoma Diagnosis and Prognosis.Scientific data · 2026Article
- Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.BMJ oncology · 2026Review
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- AI in Cancer Prognosis: A Systematic Review of Multimodal Models Combining Pathology Images and High-Throughput Omics.Cancer informatics · 2026Article
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- A Machine Learning Model Based on Clinical Factors to Predict the Efficacy of First-Line Immunochemotherapy for Patients With Advanced Gastric Cancer: Retrospective Study.JMIR medical informatics · 2025Article
- Deep learning-based artificial intelligence models predict survival in patients with oral cavity squamous cell carcinoma.Scientific reports · 2025Article
- Genetic Artificial Intelligence in Gastrointestinal Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning model and hemoglobin to red cell distribution width ratio evaluates all-cause mortality in pulmonary embolism.Scientific reports · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma.NPJ digital medicine · 2025Article
- Pathogenomic fingerprinting to identify associations between tumor morphology and epigenetic states.European journal of cancer (Oxford, England : 1990) · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- Dual-stream cross-modal fusion alignment network for survival analysis.Briefings in bioinformatics · 2025Article
- Prognostic biomarkers in oral squamous cell carcinoma: current evidence and future directions.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we aimed to develop a novel prognostic algorithm for oral squamous cell carcinoma (OSCC) using a combination of pathogenomics and AI-based techniques. We collected comprehensive clinical, genomic, and pathology data from a cohort of OSCC patients in the TCGA dataset and used machine learning and deep learning algorithms to identify relevant features that are predictive of survival outcomes. Our analyses included 406 OSCC patients. Initial analyses involved gene expression analyses, principal component analyses, gene enrichment analyses, and feature importance analyses. These insights were foundational for subsequent model development. Furthermore, we applied five machine learning/deep learning algorithms (Random Survival Forest, Gradient Boosting Survival Analysis, Cox PH, Fast Survival SVM, and DeepSurv) for survival prediction. Our initial analyses revealed relevant gene expression variations and biological pathways, laying the groundwork for robust feature selection in model building. The results showed that the multimodal model outperformed the unimodal models across all methods, with c-index values of 0.722 for RSF, 0.633 for GBSA, 0.625 for FastSVM, 0.633 for CoxPH, and 0.515 for DeepSurv. When considering only important features, the multimodal model continued to outperform the unimodal models, with c-index values of 0.834 for RSF, 0.747 for GBSA, 0.718 for FastSVM, 0.742 for CoxPH, and 0.635 for DeepSurv. Our results demonstrate the potential of pathogenomics and AI-based techniques in improving the accuracy of prognostic prediction in OSCC, which may ultimately aid in the development of personalized treatment strategies for patients with this devastating disease.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.