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

arXiv: https://arxiv.org/abs/1703.08516 · HTML: https://mlsanity.com/paper/1703.08516/

## Key facts

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

## Built on by (most cited first, 30 of 31)

- [Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images](https://mlsanity.com/paper/2201.04138/) (2022)
- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma](https://mlsanity.com/paper/2312.09576/) (2023)
- [DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT](https://mlsanity.com/paper/2109.07711/) (2021)
- [Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer](https://mlsanity.com/paper/2211.05409/) (2022)
- [Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers](https://mlsanity.com/paper/2209.11268/) (2022)
- [A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data](https://mlsanity.com/paper/2101.11935/) (2021)
- [Lymph Node Graph Neural Networks for Cancer Metastasis Prediction](https://mlsanity.com/paper/2106.01711/) (2021)
- [AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics](https://mlsanity.com/paper/2110.10332/) (2021)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases](https://mlsanity.com/paper/2501.11221/) (2025)
- [Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning](https://mlsanity.com/paper/2104.11416/) (2021)
- [Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling](https://mlsanity.com/paper/2203.04964/) (2022)
- [Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features](https://mlsanity.com/paper/2203.06314/) (2022)
- [Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images](https://mlsanity.com/paper/2211.10138/) (2022)
- [Feature Gradient Flow for Interpreting Deep Neural Networks in Head and Neck Cancer Prediction](https://mlsanity.com/paper/2307.13061/) (2023)
- [Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization](https://mlsanity.com/paper/2312.01689/) (2023)
- [Prediction of recurrence free survival of head and neck cancer using PET/CT radiomics and clinical information](https://mlsanity.com/paper/2402.18417/) (2024)
- [Translation-based Video-to-Video Synthesis](https://mlsanity.com/paper/2404.04283/) (2024)
- [Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)](https://mlsanity.com/paper/2405.01756/) (2024)
- [Single-round Self-supervised Distributed Learning using Vision Transformer](https://mlsanity.com/paper/2301.02064/) (2023)
- [Graph data modelling for outcome prediction in oropharyngeal cancer patients](https://mlsanity.com/paper/2310.02931/) (2023)
- [Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Data](https://mlsanity.com/paper/2402.08999/) (2024)
- [All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processing](https://mlsanity.com/paper/2403.06145/) (2024)
- [Unraveling Radiomics Complexity: Strategies for Optimal Simplicity in Predictive Modeling](https://mlsanity.com/paper/2407.04888/) (2024)
- [Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation](https://mlsanity.com/paper/2506.12106/) (2025)
- [MasHeNe: A Benchmark for Head and Neck CT Mass Segmentation using Window-Enhanced Mamba with Frequency-Domain Integration](https://mlsanity.com/paper/2512.01563/) (2025)
- [SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma](https://mlsanity.com/paper/2601.20575/) (2026)
- [AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer](https://mlsanity.com/paper/2604.09280/) (2026)
- [HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT](https://mlsanity.com/paper/2607.26498/) (2026)

## 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.