ArticleFrontiers in medicine2023
Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Emerging applications of artificial intelligence for risk stratification in head and neck cancer: a scoping review.Frontiers in oncology · 2026Pooled it
- Clinical predictive models for recurrence and survival in treated laryngeal and hypopharyngeal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Radiomics Using Fused PET and CT Data for Prognostic and Diagnostic Modeling in Head and Neck Cancer: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- [Contrast-enhanced CT-based habitat radiomics for analyzing the predictive capability for oral squamous cell carcinoma].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2026Article
- Deep learning radiomics based on multimodal MRI for preoperative prediction of N stage in tongue squamous cell carcinoma: a multicenter study.BMC oral health · 2026Article
- Artificial Intelligence for Preoperative Prediction of Lymph Node Metastasis and Depth of Invasion in Oral Tongue Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Multiparametric MRI radiomics model incorporating ADC improves differentiation of benign and malignant sinonasal tumors.Neuroradiology · 2026Article
- Direct integration of deep learning-based GTV auto-segmentation into a clinical radiotherapy planning system.Physics and imaging in radiation oncology · 2026Article
- Application of multimodal fusion technology in early recurrence prediction and pathological analysis of hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- Article
- Artificial intelligence in head and neck cancer: a bibliometric and visualization analysis (1995-2025).Discover oncology · 2025Article
- Article
- CNN-based prediction using early post-radiotherapy MRI as a proxy for toxicity in the murine head and neck.Acta oncologica (Stockholm, Sweden) · 2025Article
- Feature level quantitative ultrasound and CT information fusion to predict the outcome of head & neck cancer radiotherapy treatment: Enhanced principal component analysis.Medical physics · 2025Article
- Computed tomography-based deep learning and multi-instance learning for predicting microvascular invasion and prognosis in hepatocellular carcinoma.World journal of gastroenterology · 2025Article
- Use of Radiomics in Characterizing Tumor Hypoxia.International journal of molecular sciences · 2025Review
- Article
- Novel pre-spatial data fusion deep learning approach for multimodal volumetric outcome prediction models in radiotherapy.Medical physics · 2025Article
- Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study.BMC medical imaging · 2025Article
- High-risk habitat radiomics model based on ultrasound images for predicting lateral neck lymph node metastasis in differentiated thyroid cancer.BMC medical imaging · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Radiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI. Purpose: The purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment Materials and methods: FDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS; Results: CNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours. Conclusion: High performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.
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