ArticlePloS one2020
Computed tomography-derived radiomic signature of head and neck squamous cell carcinoma (peri)tumoral tissue for the prediction of locoregional recurrence and distant metastasis after concurrent chemo-radiotherapy.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT06854107 (Intratumoral and Peritumoral Habitat Radiomics of MRI Predict Pathologic Complete Response to Neoadjuvant Chemoimmunotherapy in Oral Squamous Cell Carcinoma), which is not on this map. Cited by 26 papers, 2 of them syntheses that pooled it.
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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.
Intratumoral and Peritumoral Habitat Radiomics of MRI Predict Pathologic Complete Response to Neoadjuvant Chemoimmunotherapy in Oral Squamous Cell Carcinoma
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- The application of radiomics in laryngeal cancer.The British journal of radiology · 2021Pooled it
- Radiomics as a spatial context for treatment decision-making in head and neck cancer.British journal of cancer · 2026Review
- Review
- Spatial Radiomic Graphs for Outcome Prediction in Radiation Therapy-treated Head and Neck Squamous Cell Carcinoma Using Pretreatment CT.Radiology. Imaging cancer · 2025Article
- Prognostic modeling in head and neck cancer: deep learning or handcrafted radiomics?BJR artificial intelligence · 2025Review
- The value of multi-phase CT based intratumor and peritumoral radiomics models for evaluating capsular characteristics of parotid pleomorphic adenoma.Frontiers in medicine · 2025Article
- Radiomics analysis for the prediction of locoregional recurrence of locally advanced oropharyngeal cancer and hypopharyngeal cancer.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 · 2024Article
- The effect of data resampling methods in radiomics.Scientific reports · 2024Article
- Article
- External validation of anEuropean journal of nuclear medicine and molecular imaging · 2023Article
- Article
- Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression.Journal of neuro-oncology · 2022Article
- Prediction of Incomplete Response of Primary Tumour Based on Clinical and Radiomics Features in Inoperable Head and Neck Cancers after Definitive Treatment.Journal of personalized medicine · 2022Article
- Evaluation of the dependence of radiomic features on the machine learning model.Insights into imaging · 2022Article
- Predicting cancer outcomes with radiomics and artificial intelligence in radiology.Nature reviews. Clinical oncology · 2022Review
- Locoregional Recurrence Prediction Using a Deep Neural Network of Radiological and Radiotherapy Images.Journal of personalized medicine · 2022Article
- Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.Frontiers in oncology · 2022Review
- Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: the additive benefit of CT and intra-treatment cone-beam computed tomography features.Quantitative imaging in medicine and surgery · 2021Article
- Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics.Insights into imaging · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionIn this study, we investigate the role of radiomics for prediction of overall survival (OS), locoregional recurrence (LRR) and distant metastases (DM) in stage III and IV HNSCC patients treated by chemoradiotherapy. We hypothesize that radiomic analysis of (peri-)tumoral tissue may detect invasion of surrounding tissues indicating a higher chance of locoregional recurrence and distant metastasis.
methodsTwo comprehensive data sources were used: the Dutch Cancer Society Database (Alp 7072, DESIGN) and "Big Data To Decide" (BD2Decide). The gross tumor volumes (GTV) were delineated on contrast-enhanced CT. Radiomic features were extracted using the RadiomiX Discovery Toolbox (OncoRadiomics, Liege, Belgium). Clinical patient features such as age, gender, performance status etc. were collected. Two machine learning methods were chosen for their ability to handle censored data: Cox proportional hazards regression and random survival forest (RSF). Multivariable clinical and radiomic Cox/ RSF models were generated based on significance in univariable cox regression/ RSF analyses on the held out data in the training dataset. Features were selected according to a decreasing hazard ratio for Cox and relative importance for RSF.
resultsA total of 444 patients with radiotherapy planning CT-scans were included in this study: 301 head and neck squamous cell carcinoma (HNSCC) patients in the training cohort (DESIGN) and 143 patients in the validation cohort (BD2DECIDE). We found that the highest performing model was a clinical model that was able to predict distant metastasis in oropharyngeal cancer cases with an external validation C-index of 0.74 and 0.65 with the RSF and Cox models respectively. Peritumoral radiomics based prediction models performed poorly in the external validation, with C-index values ranging from 0.32 to 0.61 utilizing both feature selection and model generation methods.
conclusionOur results suggest that radiomic features from the peritumoral regions are not useful for the prediction of time to OS, LR and DM.
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