ArticlePloS one2019
Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients.Cancer medicine · 2023Pooled it
- Determining Individualized Prognosis of Head and Neck Squamous Cell Carcinoma Patients Treated with Radiotherapy Using a Prediction Model Based on Body Composition Features: Introducing a Probability Calculator.Journal of imaging informatics in medicine · 2026Article
- Selection Criteria for De-Escalated Chemoradiotherapy for HPV-Related Oropharyngeal Cancer Based on Prognostic Biomarkers or Early Tumor Response to Therapy: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Benchmarking feature projection methods in radiomics.Scientific reports · 2025Article
- Development and application of a novel tumor habitat analysis technique based on dynamical modeling.Medical physics · 2025Article
- Measured intrapatient radiomic variability as a predictor of treatment response in multi-metastatic soft tissue sarcoma patients.Scientific reports · 2025Article
- Predictive value of tumoral and peritumoral radiomic features in neoadjuvant chemotherapy response for breast cancer: a retrospective study.La Radiologia medica · 2025Article
- Prognostic modeling in head and neck cancer: deep learning or handcrafted radiomics?BJR artificial intelligence · 2025Review
- Prognosis Prediction in Head and Neck Squamous Cell Carcinoma by Radiomics and Clinical Information.Biomedicines · 2024Article
- Prognostic Value of Radiomic Analysis Using Pre- and Post-TreatmentJournal of personalized medicine · 2024Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics.Diagnostics (Basel, Switzerland) · 2023Article
- Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas.Oral oncology · 2023Article
- MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures.Biomarker research · 2023Article
- The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation.Seminars in radiation oncology · 2023Review
- Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics.Cancer research communications · 2023Article
- External validation of anEuropean journal of nuclear medicine and molecular imaging · 2023Article
- Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization.Physics and imaging in radiation oncology · 2023Article
- Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction.Cancers · 2023Article
- Head and neck cancer predictive risk estimator to determine control and therapeutic outcomes of radiotherapy (HNC-PREDICTOR): development, international multi-institutional validation, and web implementation of clinic-ready model-based risk stratification for head and neck cancer.European journal of cancer (Oxford, England : 1990) · 2023Article
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18 authors.
Funding
Abstract
Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models. We retrospectively reviewed 726 CT and 686 PET images from head and neck cancer patients, who were divided into training or independent testing cohorts. For each patient, radiomics features with different preprocessing were calculated and two clinical variables-HPV status and tumor volume-were also included. A Cox proportional hazards model was built on the training data by using bootstrapped Lasso regression to predict overall survival. The effect of controlled imaging protocols on model performance was evaluated by subsetting the original training and independent testing cohorts to include only patients whose images were obtained using the same imaging protocol and vendor. Tumor volume, HPV status, and two radiomics covariates were selected for the CT model, resulting in an AUC of 0.72. However, volume alone produced a higher AUC, whereas adding radiomics features reduced the AUC. HPV status and one radiomics feature were selected as covariates for the PET model, resulting in an AUC of 0.59, but neither covariate was significantly associated with survival. Limiting the training and independent testing to patients with the same imaging protocol reduced the AUC for CT patients to 0.55, and no covariates were selected for PET patients. Radiomics features were not consistently associated with survival in CT or PET images of head and neck patients, even within patients with the same imaging protocol.
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