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
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.
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 5 papers.
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5 citing papers in PubMed, 9 citations in OpenAlex.
- An evaluation of uncertainty quantification methods and measures for deep learning outcome prediction models in head and neck cancer radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer.Physics and imaging in radiation oncology · 2023Article
- Role of PET/CT in Oropharyngeal Cancers.Cancers · 2023Review
- 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
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8 authors at 1 institution in 1 country.
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Abstract
PET/CT images provide a rich data source for clinical prediction models in head and neck squamous cell carcinoma (HNSCC). Deep learning models often use images in an end-to-end fashion with clinical data or no additional input for predictions. However, in the context of HNSCC, the tumor region of interest may be an informative prior in the generation of improved prediction performance. In this study, we utilize a deep learning framework based on a DenseNet architecture to combine PET images, CT images, primary tumor segmentation masks, and clinical data as separate channels to predict progression-free survival (PFS) in days for HNSCC patients. Through internal validation (10-fold cross-validation) based on a large set of training data provided by the 2021 HECKTOR Challenge, we achieve a mean C-index of 0.855 ± 0.060 and 0.650 ± 0.074 when observed events are and are not included in the C-index calculation, respectively. Ensemble approaches applied to cross-validation folds yield C-index values up to 0.698 in the independent test set (external validation), leading to a 1
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.