ArticleHeliyon2024
Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiomic and dosiomic machine learning models for predicting radiation-induced oral mucositis in head and neck cancer: a systematic review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Pooled it
- Prediction models of severe radiation-induced oral mucositis: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- The prevalence of oral mucositis after radiotherapy in patients with Head and Neck Cancer and its associated factors: a meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Pooled it
- Longitudinal bilateral parotid delta-radiomic kinetics and variance signatures for prediction of late xerostomia following radiotherapy.BMC medical imaging · 2026Article
- Construction of a predictive model for radiation proctitis after radical radiotherapy for cervical cancer based on MRI radiomics.Abdominal radiology (New York) · 2026Article
- Evaluation of mid-treatmentQuantitative imaging in medicine and surgery · 2026Article
- Hybrid I-ResNeT-ViT and Cost-Sensitive InceptionV3 Models for Tumour Severity and Malignancy Classification Using Medical Imaging.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- SHAP-based interpretable machine learning with longitudinal delta-radiomics across seven weeks of treatment for xerostomia prediction in head-and-neck cancer.Radiation oncology (London, England) · 2026Article
- Article
- Interventions to Chemotherapy and Radiotherapy Induced Oral Mucositis: a Systematic Review and Meta-analysis.Acta stomatologica Croatica · 2025Article
- Machine learning-based prediction of severe oral mucositis from head and neck cancer radiation therapy.Radiation oncology journal · 2025Article
- Artificial intelligence in head and neck cancer: a bibliometric and visualization analysis (1995-2025).Discover oncology · 2025Article
- Beyond Standard Radiotherapy: an AI-Driven Framework for Personalized Radiotherapy with a Four-Step Classification in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients.Current oncology reports · 2025Review
- Integrating dose-volume histogram parameters and radiomics-based machine learning to identify carbon ion radiotherapy-induced acute oral mucositis in patients with head and neck cancer.Physics and imaging in radiation oncology · 2025Article
- A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion.BMC medical imaging · 2025Article
- Machine Learning-Assisted Analysis of the Oral Cancer Immune Microenvironment: From Single-Cell Level to Prognostic Model Construction.Journal of cellular and molecular medicine · 2025Article
- Development and validation of a CT based radiomics nomogram for preoperative prediction of ISUP/WHO grading in renal clear cell carcinoma.Abdominal radiology (New York) · 2025Article
- Potentiality of Curcumin Against Radio-Chemotherapy Induced Oral Mucositis: A Review.Indian journal of surgical oncology · 2025Review
- A multi-center, multi-organ, multi-omic prediction model for treatment-induced severe oral mucositis in nasopharyngeal carcinoma.La Radiologia medica · 2025Article
- Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To establish the early prediction models of radiation-induced oral mucositis (RIOM) based on baseline CT-based radiomic features (RFs), dosimetric data, and clinical features by machine learning models for head and neck cancer (HNC) patients. Methods: In this single-center prospective study, 49 HNCs treated with curative intensity modulated radiotherapy (IMRT) were enrolled. Baseline CT images ( Results: Among extracted features, the top 10 RFs, the top 5 dose-volume features, and the top 5 clinical features were selected using mRMR method. The model exploiting the integrated features (10-radiomics + 5-dosimetric + 5-clinical) achieved the best prediction with AUC, accuracy, sensitivity, precision, and F1-measure values of 91.7 %, 90.0 %, 83.0 % 100.0 %, and 91.0 %, respectively. The model developed using baseline CT RFs alone provided the best performance compared to dose-volume features or clinical features alone, with an AUC of 87.0 %. Conclusion: Our results suggest that the integration of baseline CT radiomic features with dosimetric and clinical features showed promising potential to improve the performance of machine learning models in early prediction of RIOM. The ultimate goal is to personalize radiotherapy for HNC patients.
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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.