Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer
Open Access
- 22 November 2023
- journal article
- research article
- Published by Elsevier in Computer Methods and Programs in Biomedicine
- Vol. 244, 107939
Abstract
No abstract availableKeywords
Funding Information
- China Scholarship Council
- Hanarth Fund Foundation
This publication has 24 references indexed in Scilit:
- Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohortsScientific Reports, 2021
- Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?Journal of Thoracic Disease, 2021
- 2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinomaScientific Reports, 2020
- From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and OpportunitiesIEEE Signal Processing Magazine, 2019
- Deep learning in head & neck cancer outcome predictionScientific Reports, 2019
- Competing risk nomograms for nasopharyngeal carcinoma in the intensity-modulated radiotherapy era: A big-data, intelligence platform-based analysisRadiotherapy and Oncology, 2018
- DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural networkBMC Medical Research Methodology, 2018
- 18F-FDG PET image biomarkers improve prediction of late radiation-induced xerostomiaRadiotherapy and Oncology, 2018
- Multimodal Imaging of Head and Neck Squamous Cell CarcinomaCancer Control, 2017
- Metabolic Reprogramming of Stromal Fibroblasts through p62-mTORC1 Signaling Promotes Inflammation and TumorigenesisCancer Cell, 2014