Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer
暂无分享,去创建一个
J. Langendijk | L. V. van Dijk | N. Sijtsema | Baoqiang Ma | Jiapan Guo | P. V. van Ooijen | Stefan Both | Hung Chu
[1] A. van der Schaaf,et al. CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma. , 2023, Medical physics.
[2] L. Wee,et al. Using 3D deep features from CT scans for cancer prognosis based on a video classification model: A multi-dataset feasibility study. , 2023, Medical physics.
[3] N. Albert,et al. Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis , 2022, Comput. Methods Programs Biomed..
[4] Shuyu Li,et al. Multi-view prediction of Alzheimer's disease progression with end-to-end integrated framework , 2021, J. Biomed. Informatics.
[5] Qin Liu,et al. Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer. , 2021, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[6] Perry B. Johnson,et al. Radiomics Predicts for Distant Metastasis in Locally Advanced Human Papillomavirus-Positive Oropharyngeal Squamous Cell Carcinoma , 2021, Cancers.
[7] R. He,et al. 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 , 2021, medRxiv.
[8] A. Bozzato,et al. HPV Status as Prognostic Biomarker in Head and Neck Cancer—Which Method Fits the Best for Outcome Prediction? , 2021, Cancers.
[9] K. Hirata,et al. Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images , 2021, BMC cancer.
[10] H. Galvão,et al. Five-year survival and prognostic factors for oropharyngeal squamous cell carcinoma: retrospective cohort of a cancer center , 2021, Oral and Maxillofacial Surgery.
[11] Mark A. Anastasio,et al. Impact of deep learning-based image super-resolution on binary signal detection , 2021, Journal of medical imaging.
[12] Nai-Ming Cheng,et al. Deep Learning for Fully Automated Prediction of Overall Survival in Patients with Oropharyngeal Cancer Using FDG-PET Imaging , 2021, Clinical Cancer Research.
[13] H. Aerts,et al. Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models. , 2021, European journal of radiology.
[14] G. Landry,et al. Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts , 2021, Scientific Reports.
[15] Mark A. Anastasio,et al. Task-based evaluation of deep image super-resolution in medical imaging , 2021, Medical Imaging.
[16] D. Kallogjeri,et al. 20 pack-year smoking history as strongest smoking metric predictive of HPV-positive oropharyngeal cancer outcomes. , 2021, American journal of otolaryngology.
[17] Yan Zhao,et al. MRI image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for hippocampal subfields segmentation , 2020, Comput. Medical Imaging Graph..
[18] Chi Liu,et al. Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics , 2020, Translational oncology.
[19] Yan Zhao,et al. Prediction of Alzheimer's Disease Progression with Multi-Information Generative Adversarial Network , 2020, IEEE Journal of Biomedical and Health Informatics.
[20] M. Prasad,et al. Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma , 2020, Cancers.
[21] Joelle Pineau,et al. RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy via Deep Learning. , 2020, International journal of radiation oncology, biology, physics.
[22] D. Dong,et al. Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[23] B. Burkey,et al. Impact of active smoking on outcomes in HPV+ oropharyngeal cancer , 2019, Head & neck.
[24] Max Dahele,et al. Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation. , 2019, International journal of radiation oncology, biology, physics.
[25] Zhenyu Liu,et al. Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[26] J. Seuntjens,et al. Deep learning in head & neck cancer outcome prediction , 2019, Scientific Reports.
[27] D. Sher,et al. Prediction of Local Persistence/Recurrence on PET/CT scans after Radiation Therapy Treatment of Head and Neck Cancer Using a Multi-objective Radiomics Model , 2018, International Journal of Radiation Oncology*Biology*Physics.
[28] W. Westra,et al. Prognostic factors for human papillomavirus–positive and negative oropharyngeal carcinomas , 2018, The Laryngoscope.
[29] Clifton D Fuller,et al. Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function. , 2018, International journal of radiation oncology, biology, physics.
[30] Christopher U. Jones,et al. Development and Validation of Nomograms Predictive of Overall and Progression-Free Survival in Patients With Oropharyngeal Cancer. , 2017, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[31] Issam El-Naqa,et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer , 2017, Scientific Reports.
[32] A. Garden,et al. Development and validation of a staging system for HPV-related oropharyngeal cancer by the International Collaboration on Oropharyngeal cancer Network for Staging (ICON-S): a multicentre cohort study. , 2016, The Lancet. Oncology.
[33] Shao Hui Huang,et al. External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma , 2015, Acta oncologica.
[34] P. Lambin,et al. Externally validated HPV-based prognostic nomogram for oropharyngeal carcinoma patients yields more accurate predictions than TNM staging. , 2014, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[35] E. King,et al. Tumour-infiltrating lymphocytes predict for outcome in HPV-positive oropharyngeal cancer , 2013, British Journal of Cancer.
[36] D. Rietveld,et al. Critical weight loss is a major prognostic indicator for disease-specific survival in patients with head and neck cancer receiving radiotherapy , 2013, British Journal of Cancer.
[37] K. Ang,et al. Human papillomavirus and survival of patients with oropharyngeal cancer. , 2010, The New England journal of medicine.
[38] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[39] N. Mantel. Evaluation of survival data and two new rank order statistics arising in its consideration. , 1966, Cancer chemotherapy reports.
[40] N. Sijtsema,et al. TransRP: Transformer-based PET/CT feature extraction incorporating clinical data for recurrence-free survival prediction in oropharyngeal cancer , 2023 .
[41] N. Sijtsema,et al. Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer , 2021, HECKTOR@MICCAI.
[42] N. Sijtsema,et al. Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images , 2021, HECKTOR@MICCAI.
[43] Qiongling Li,et al. Hippocampus Segmentation for Preterm and Aging Brains Using 3D Densely Connected Fully Convolutional Networks , 2020, IEEE Access.
[44] S. Klein,et al. Radiomics , 2020, Handbook of Medical Image Computing and Computer Assisted Intervention.