ArticleHead and neck tumor segmentation and outcome prediction : second challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings. Head and Neck Tumor Segmentation Challenge (2nd : 2021 ...2022
Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data.
Article in Head and neck tumor segmentation and outcome prediction : second challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings. Head and Neck Tumor Segmentation Challenge (2nd : 2021 ..., 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 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
- Clinically Interpretable Survival Risk Stratification in Head and Neck Cancer Using Bayesian Networks and Markov Blankets.International journal of radiation oncology, biology, physics · 2026Article
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- Evaluating Dimensionality Reduction for Patient-Reported Outcome-Based Survival Modeling in Patients With Head and Neck Cancer.JCO clinical cancer informatics · 2025Article
- Interpretable survival network for progression risk analysis of multimodality imaging biomarkers in poor-prognosis head and neck cancers.Scientific reports · 2024Article
- Adaptive segmentation-to-survival learning for survival prediction from multi-modality medical images.NPJ precision oncology · 2024Article
- Article
- Multi-Modal Ensemble Deep Learning in Head and Neck Cancer HPV Sub-Typing.Bioengineering (Basel, Switzerland) · 2023Article
- Role of PET/CT in Oropharyngeal Cancers.Cancers · 2023Review
- Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction.Cancers · 2023Article
- Artificial Intelligence for Radiation Oncology Applications Using Public Datasets.Seminars in radiation oncology · 2022Review
- Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy.Frontiers in oncology · 2022Article
- Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma.Head and neck tumor segmentation and outcome prediction : second challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings. Head and Neck Tumor Segmentation Challenge (2nd : 2021 ... · 2022Article
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8 authors.
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Abstract
Determining progression-free survival (PFS) for head and neck squamous cell carcinoma (HNSCC) patients is a challenging but pertinent task that could help stratify patients for improved overall outcomes. PET/CT images provide a rich source of anatomical and metabolic data for potential clinical biomarkers that would inform treatment decisions and could help improve PFS. In this study, we participate in the 2021 HECKTOR Challenge to predict PFS in a large dataset of HNSCC PET/CT images using deep learning approaches. We develop a series of deep learning models based on the DenseNet architecture using a negative log-likelihood loss function that utilizes PET/CT images and clinical data as separate input channels to predict PFS in days. Internal model validation based on 10-fold cross-validation using the training data (N = 224) yielded C-index values up to 0.622 (without) and 0.842 (with) censoring status considered in C-index computation, respectively. We then implemented model ensembling approaches based on the training data cross-validation folds to predict the PFS of the test set patients (N = 101). External validation on the test set for the best ensembling method yielded a C-index value of 0.694, placing 2
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