mlsanityData to 14 Oct 2026

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

arXiv 1703.08516 ↗First posted 24 Mar 2017cs.CVcited by 31

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Abstract (arXiv)

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Mar 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 6; 2023: 6; 2024: 6; 2025: 3; 2026: 4.

31 papers.

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  1. 2022Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Imagescited by 30
  2. 2021A Systematic Collection of Medical Image Datasets for Deep Learningcited by 29
  3. 2023SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinomacited by 29
  4. 2021DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CTcited by 11
  5. 2022Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancercited by 6
  6. 2022Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancerscited by 4
  7. 2021A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Datacited by 3
  8. 2021Lymph Node Graph Neural Networks for Cancer Metastasis Predictioncited by 3
  9. 2021AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomicscited by 2
  10. 2023Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotationcited by 2
  11. 2025Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastasescited by 2
  12. 2021Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learningcited by 1
19 more
  1. 2022Metastatic Cancer Outcome Prediction with Injective Multiple Instance Poolingcited by 1
  2. 2022Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Featurescited by 1
  3. 2022Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT imagescited by 1
  4. 2023Feature Gradient Flow for Interpreting Deep Neural Networks in Head and Neck Cancer Predictioncited by 1
  5. 2023Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularizationcited by 1
  6. 2024Prediction of recurrence free survival of head and neck cancer using PET/CT radiomics and clinical informationcited by 1
  7. 2024Translation-based Video-to-Video Synthesiscited by 1
  8. 2024Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)cited by 1
  9. 2023Single-round Self-supervised Distributed Learning using Vision Transformercited by 0
  10. 2023Graph data modelling for outcome prediction in oropharyngeal cancer patientscited by 0
  11. 2024Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Datacited by 0
  12. 2024All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processingcited by 0
  13. 2024Unraveling Radiomics Complexity: Strategies for Optimal Simplicity in Predictive Modelingcited by 0
  14. 2025Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentationcited by 0
  15. 2025MasHeNe: A Benchmark for Head and Neck CT Mass Segmentation using Window-Enhanced Mamba with Frequency-Domain Integrationcited by 0
  16. 2026SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinomacited by 0
  17. 2026AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancercited by 0
  18. 2026HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CTcited by 0
  19. 2026Empirical investigation of 3D CT Foundation Models and Unsupervised Adaptation for Head and Neck Cancer Recurrence Predictioncited by 0