2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma
暂无分享,去创建一个
Sebastian Starke | Steffen Löck | Mechthild Krause | Michael Baumann | Stefan Leger | Christian Richter | Stephanie E. Combs | Claus Belka | Daniel Zips | Andreas Schreiber | Alex Zwanenburg | Fabian Lohaus | Jan C. Peeken | Annett Linge | Inge Tinhofer | Panagiotis Balermpas | Ute Ganswindt | Esther G. C. Troost | Simon Boeke | S. Starke | I. Tinhofer | S. Leger | S. Löck | M. Krause | M. Baumann | C. Belka | D. Zips | A. Zwanenburg | P. Balermpas | S. Combs | Karoline Leger | F. Lohaus | A. Linge | A. Schreiber | G. Kalinauskaite | N. Guberina | M. Guberina | J. von der Grün | U. Ganswindt | J. Peeken | S. Boeke | C. Richter | E. Troost | Nika Guberina | Karoline Leger | Goda Kalinauskaite | Maja Guberina | Jens von der Grün
[1] J. Shaman,et al. Hydrometeorology and flood pulse dynamics drive diarrheal disease outbreaks and increase vulnerability to climate change in surface-water-dependent populations: A retrospective analysis , 2018, PLoS medicine.
[2] 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.
[3] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[4] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Dorit Merhof,et al. Image-based Survival Analysis for Lung Cancer Patients using CNNs. , 2018 .
[6] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[7] Aedín C. Culhane,et al. survcomp: an R/Bioconductor package for performance assessment and comparison of survival models , 2011, Bioinform..
[8] V. Budach,et al. Heat shock protein 70 and tumor‐infiltrating NK cells as prognostic indicators for patients with squamous cell carcinoma of the head and neck after radiochemotherapy: A multicentre retrospective study of the German Cancer Consortium Radiation Oncology Group (DKTK‐ROG) , 2017, International journal of cancer.
[9] Steffen Löck,et al. Assessing robustness of radiomic features by image perturbation , 2018, Scientific Reports.
[10] P. Lambin,et al. Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[11] Raphaël Marée,et al. Comparison of Deep Transfer Learning Strategies for Digital Pathology , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[12] Ender Konukoglu,et al. Post-radiochemotherapy PET radiomics in head and neck cancer - The influence of radiomics implementation on the reproducibility of local control tumor models. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[13] Daniel B. Mark,et al. TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .
[14] M. Alber,et al. Prognostic value of dynamic hypoxia PET in head and neck cancer: Results from a planned interim analysis of a randomized phase II hypoxia-image guided dose escalation trial. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[15] Martina Sollini,et al. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics , 2019, European Journal of Nuclear Medicine and Molecular Imaging.
[16] D. Aust,et al. Low Cancer Stem Cell Marker Expression and Low Hypoxia Identify Good Prognosis Subgroups in HPV(−) HNSCC after Postoperative Radiochemotherapy: A Multicenter Study of the DKTK-ROG , 2016, Clinical Cancer Research.
[17] Guangtao Zhai,et al. A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme , 2017, Scientific Reports.
[18] J. Steinbach,et al. Residual tumour hypoxia in head-and-neck cancer patients undergoing primary radiochemotherapy, final results of a prospective trial on repeat FMISO-PET imaging. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[19] J. Seuntjens,et al. Deep learning in head & neck cancer outcome prediction , 2019, Scientific Reports.
[20] B. Efron. The Efficiency of Cox's Likelihood Function for Censored Data , 1977 .
[21] Michael Baumann,et al. Exploratory prospective trial of hypoxia-specific PET imaging during radiochemotherapy in patients with locally advanced head-and-neck cancer. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[22] G. Pazour,et al. Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness , 2017, Scientific Reports.
[23] D. Brat,et al. Predicting cancer outcomes from histology and genomics using convolutional networks , 2017, Proceedings of the National Academy of Sciences.
[24] Uri Shaham,et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network , 2016, BMC Medical Research Methodology.
[25] Lawrence Carin,et al. Adversarial Time-to-Event Modeling , 2018, ICML.
[26] Timothy Solberg,et al. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers , 2018, Medical physics.
[27] Benjamin Haibe-Kains,et al. A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? , 2008, Bioinform..
[28] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Junzhou Huang,et al. Deep convolutional neural network for survival analysis with pathological images , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[30] Samuel H. Hawkins,et al. Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma , 2016, Tomography.
[31] P. Lambin,et al. Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer , 2015, Front. Oncol..
[32] S. Sathiya Keerthi,et al. Regression with Conditional GAN , 2019, ArXiv.
[33] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[34] V. Budach,et al. HPV in , 2014 .
[35] Trevor Hastie,et al. Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. , 2011, Journal of statistical software.
[36] P. Lambin,et al. Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries , 2017, International journal of radiation oncology, biology, physics.
[37] Alex Lallement,et al. Survey on deep learning for radiotherapy , 2018, Comput. Biol. Medicine.
[38] D. Aust,et al. HPV status, cancer stem cell marker expression, hypoxia gene signatures and tumour volume identify good prognosis subgroups in patients with HNSCC after primary radiochemotherapy: A multicentre retrospective study of the German Cancer Consortium Radiation Oncology Group (DKTK-ROG). , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[39] R. Tibshirani. The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.
[40] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[41] Steffen Löck,et al. A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling , 2017, Scientific Reports.
[42] Lawrence O. Hall,et al. Exploring deep features from brain tumor magnetic resonance images via transfer learning , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[43] Balaji Krishnapuram,et al. On Ranking in Survival Analysis: Bounds on the Concordance Index , 2007, NIPS.
[44] Jing Ning,et al. Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients , 2019, PloS one.
[45] Issam El-Naqa,et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer , 2017, Scientific Reports.
[46] R. Gillies,et al. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study , 2018, PLoS medicine.
[47] M. Pencina,et al. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation , 2004, Statistics in medicine.
[48] Daniel Cremers,et al. Radiomics in radiooncology - Challenging the medical physicist. , 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[49] F. Buchholz,et al. Independent validation of tumour volume, cancer stem cell markers and hypoxia-associated gene expressions for HNSCC after primary radiochemotherapy , 2019, Clinical and translational radiation oncology.
[50] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[51] Chaofeng Liang,et al. A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme , 2017, Scientific Reports.
[52] Steffen Löck,et al. CT imaging during treatment improves radiomic models for patients with locally advanced head and neck cancer. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[53] Ruud H. Brakenhoff,et al. The molecular biology of head and neck cancer , 2011, Nature Reviews Cancer.
[54] J. Robertson,et al. Face Dependence of Schottky Barriers Heights of Silicides and Germanides on Si and Ge , 2017, Scientific Reports.
[55] Mechthild Krause,et al. Radiation oncology in the era of precision medicine , 2016, Nature Reviews Cancer.
[56] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[57] Jonathan A. Javitch,et al. The E2.65A mutation disrupts dynamic binding poses of SB269652 at the dopamine D2 and D3 receptors , 2018, PLoS Comput. Biol..
[58] I. El Naqa,et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities , 2015, Physics in medicine and biology.
[59] Stephen Baek,et al. Deep segmentation networks predict survival of non-small cell lung cancer , 2019, Scientific Reports.
[60] Xun Zhu,et al. Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data , 2018, PLoS Comput. Biol..
[61] Matthias Schmid,et al. Boosting the Concordance Index for Survival Data – A Unified Framework To Derive and Evaluate Biomarker Combinations , 2013, PloS one.
[62] V. Budach,et al. Development and Validation of a Gene Signature for Patients with Head and Neck Carcinomas Treated by Postoperative Radio(chemo)therapy , 2018, Clinical Cancer Research.