ArticleCancers2023
Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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
10 citing papers in PubMed.
- 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
- Review
- Equity and Generalizability of Radiomics in Orbital Disease: Challenges for Ophthalmology, Otolaryngology, and Plastic Surgery.Diagnostics (Basel, Switzerland) · 2026Review
- Machine learning-based treatment outcome prediction in head and neck cancer using integrated noninvasive diagnostics.International journal of computer assisted radiology and surgery · 2026Article
- 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
- Novel pre-spatial data fusion deep learning approach for multimodal volumetric outcome prediction models in radiotherapy.Medical physics · 2025Article
- Artificial intelligence for response prediction and personalisation in radiation oncology.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics analysis provides a promising avenue towards the enabling of personalized radiotherapy. Most frequently, prognostic radiomics models are based on features extracted from medical images that are acquired before treatment. Here, we investigate whether combining data from multiple timepoints during treatment and from multiple imaging modalities can improve the predictive ability of radiomics models. We extracted radiomics features from computed tomography (CT) images acquired before treatment as well as two and three weeks after the start of radiochemotherapy for 55 patients with locally advanced head and neck squamous cell carcinoma (HNSCC). Additionally, we obtained features from FDG-PET images taken before treatment and three weeks after the start of therapy. Cox proportional hazards models were then built based on features of the different image modalities, treatment timepoints, and combinations thereof using two different feature selection methods in a five-fold cross-validation approach. Based on the cross-validation results, feature signatures were derived and their performance was independently validated. Discrimination regarding loco-regional control was assessed by the concordance index (C-index) and log-rank tests were performed to assess risk stratification. The best prognostic performance was obtained for timepoints during treatment for all modalities. Overall, CT was the best discriminating modality with an independent validation C-index of 0.78 for week two and weeks two and three combined. However, none of these models achieved statistically significant patient stratification. Models based on FDG-PET features from week three provided both satisfactory discrimination (C-index = 0.61 and 0.64) and statistically significant stratification (p=0.044 and p<0.001), but produced highly imbalanced risk groups. After independent validation on larger datasets, the value of (multimodal) radiomics models combining several imaging timepoints should be prospectively assessed for personalized treatment strategies.
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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 Socratic graph.