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Big Data In Head And Neck Cancer

C. Resteghini, Annalisa Trama, E. Borgonovi, H. Hosni, G. Corrao, E. Orlandi, G. Calareso, L. Cecco, C. Piazza, L. Mainardi, L. Licitra
Published 2018 · Medicine

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Opinion statementHead and neck cancers can be used as a paradigm for exploring “big data” applications in oncology. Computational strategies derived from big data science hold the promise of shedding new light on the molecular mechanisms driving head and neck cancer pathogenesis, identifying new prognostic and predictive factors, and discovering potential therapeutics against this highly complex disease. Big data strategies integrate robust data input, from radiomics, genomics, and clinical-epidemiological data to deeply describe head and neck cancer characteristics. Thus, big data may advance research generating new knowledge and improve head and neck cancer prognosis supporting clinical decision-making and development of treatment recommendations.
This paper references
10.1038/s41598-018-19341-x
Integrated miRNA-mRNA spatial signature for oral squamous cell carcinoma: a prospective profiling study of Narrow Band Imaging guided resection
C. Farah (2018)
10.1002/hed.23464
Using computational strategies to predict potential drugs for nasopharyngeal carcinoma
M. Lan (2014)
10.1136/bmjopen-2013-004007
Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry
S. Gupta (2014)
10.1002/ijc.29210
Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012
J. Ferlay (2015)
10.1002/pon.4040
Social outcomes in adult survivors of childhood cancer compared to the general population: linkage of a cohort with population registers
A. Font-Gonzalez (2016)
Tumor control probability (TCP) and normal tissue complication probability (NTCP) in head and neck cancer.
V. Grégoire (2005)
10.1007/s00520-015-2968-2
Psychological distress, optimism and general health in breast cancer survivors: a data linkage study using the Scottish Health Survey
J. Leung (2015)
10.1186/s12885-015-1653-7
Label-free blood serum detection by using surface-enhanced Raman spectroscopy and support vector machine for the preoperative diagnosis of parotid gland tumors
B. Yan (2015)
10.1001/jama.2017.16635
Benefits and Risks of Machine Learning Decision Support Systems-Reply.
F. Cabitza (2017)
10.1016/j.ijrobp.2017.08.004
Quantitative Evaluation of Head and Neck Cancer Treatment-Related Dysphagia in the Development of a Personalized Treatment Deintensification Paradigm.
H. Quon (2017)
10.1007/s00216-015-8960-3
Fourier-transform-infrared-spectroscopy based spectral-biomarker selection towards optimum diagnostic differentiation of oral leukoplakia and cancer
Satarupa Banerjee (2015)
10.1136/bmj.328.7454.1490
Grading quality of evidence and strength of recommendations
D. Atkins (2004)
10.1001/JAMA.1995.03530220066035
Users' Guides to the Medical Literature: IX. A Method for Grading Health Care Recommendations
G. Guyatt (1995)
Inves - tigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients
H Elhalawani (2018)
10.1146/annurev-pharmtox-010818-021315
The Exposome: Molecules to Populations.
M. Niedzwiecki (2019)
10.1002/cncr.30253
Economic independence in survivors of cancer diagnosed at a young age: A Norwegian national cohort study
M. W. Gunnes (2016)
10.1016/j.oraloncology.2017.07.026
Complex integrated analysis of lncRNAs-miRNAs-mRNAs in oral squamous cell carcinoma.
S. Li (2017)
10.1371/journal.pone.0124653
Proton Pump Inhibitor Usage and the Risk of Myocardial Infarction in the General Population
N. Shah (2015)
10.1158/0008-5472.CAN-14-1458
A noninvasive procedure for early-stage discrimination of malignant and precancerous vocal fold lesions based on laryngeal dynamics analysis.
Jakob Unger (2015)
10.18632/oncotarget.13926
RIPK1 prevents aberrant ZBP1-initiated necroptosis
T. Berghe (2017)
10.1098/rsta.2016.0153
Big data need big theory too
P. Coveney (2016)
10.3174/ajnr.A5106
MRI-Based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma
S. Ramkumar (2017)
10.1001/JAMA.274.22.1800
Users' guides to the medical literature. IX. A method for grading health care recommendations. Evidence-Based Medicine Working Group.
G. Guyatt (1995)
10.1007/s13347-017-0265-3
Forecasting in Light of Big Data
H. Hosni (2018)
10.1158/1078-0432.CCR-17-2345
Development and Validation of a Gene Signature for Patients with Head and Neck Carcinomas Treated by Postoperative Radio(chemo)therapy
S. Schmidt (2018)
10.1088/1361-6560/aa73cc
Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics.
M. Folkert (2017)
10.18632/oncotarget.6436
Development of a ten-signature classifier using a support vector machine integrated approach to subdivide the M1 stage into M1a and M1b stages of nasopharyngeal carcinoma with synchronous metastases to better predict patients' survival
Rou Jiang (2016)
10.1016/S0140-6736(00)90011-4
Chemotherapy added to locoregional treatment for head and neck squamous-cell carcinoma: three meta-analyses of updated individual data
J. Pignon (2000)
10.1097/RCT.0000000000000682
Computed Tomography-Based Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinoma
S. Ranjbar (2018)
10.1016/j.ejmp.2015.04.009
A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities.
Gabriele Guidi (2015)
10.1038/s41598-017-13448-3
A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling
S. Leger (2017)
10.1016/j.ejmp.2017.10.008
Cochlea CT radiomics predicts chemoradiotherapy induced sensorineural hearing loss in head and neck cancer patients: A machine learning and multi-variable modelling study.
H. Abdollahi (2018)
10.21873/CGP.20063
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.
Shujun Huang (2018)
10.1097/MLR.0b013e31829b1dbd
Caveats for the use of operational electronic health record data in comparative effectiveness research.
W. Hersh (2013)
10.1001/jama.2017.7797
Unintended Consequences of Machine Learning in Medicine
F. Cabitza (2017)
10.1038/nbt1206-1565
What is a support vector machine?
William Stafford Noble (2006)
10.1039/c5mb00468c
A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.
V. Randhawa (2015)
10.1001/jama.2018.5602
Big Data and Predictive Analytics: Recalibrating Expectations
N. Shah (2018)
10.1155/2015/162439
The Potential of GMP-Compliant Platelet Lysate to Induce a Permissive State for Cardiovascular Transdifferentiation in Human Mediastinal Adipose Tissue-Derived Mesenchymal Stem Cells
C. Siciliano (2015)
10.1016/j.ijrobp.2009.02.065
Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework.
H. Zhang (2009)
10.3389/fonc.2018.00035
Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia
H. Gabryś (2018)
10.1158/0008-5472.CAN-18-0878
Determination of Tumor Margins with Surgical Specimen Mapping Using Near-Infrared Fluorescence.
Rebecca W. Gao (2018)
10.1016/j.ejmp.2016.10.005
A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation.
G Guidi (2016)
10.3390/ijerph15071558
Differential Effect of Smoking on Gene Expression in Head and Neck Cancer Patients
A. Irimie (2018)
10.7326/M15-2970
Two Ways of Knowing: Big Data and Evidence-Based Medicine
Ida Sim (2016)
10.1117/1.JMI.4.3.034502
Confident texture-based laryngeal tissue classification for early stage diagnosis support
Sara Moccia (2017)
10.1038/srep11044
Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer
Chintan Parmar (2015)
10.18632/oncotarget.17550
Quantitative prediction of oral cancer risk in patients with oral leukoplakia
Y. Liu (2017)
10.1016/j.wneu.2017.11.132
Virtual Reality-Based Simulators for Cranial Tumor Surgery: A Systematic Review.
T. Mazur (2018)
10.15265/IY-2014-0002
IBM's Health Analytics and Clinical Decision Support.
M. S. Kohn (2014)
10.1371/journal.pone.0100234
Prediction of Survival with Alternative Modeling Techniques Using Pseudo Values
T. van der Ploeg (2014)
10.1016/j.ijrobp.2014.08.350
Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy.
X. Yang (2014)
Big Data – and its contributions to perioperative medicine
DI Sessler (2014)
10.1155/2015/639021
Toward a Literature-Driven Definition of Big Data in Healthcare
E. Baro (2015)
10.1016/j.artmed.2017.03.004
Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification
Marco Pota (2017)
10.1007/s11307-018-1227-6
Emerging Intraoperative Imaging Modalities to Improve Surgical Precision
I. Alam (2018)
10.1038/sdata.2017.77
Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges
Hesham Abdallah S.R. Aubrey L. James Andrew J. Joel E. Sh Elhalawani Mohamed White Zafereo Wong Berends AboH (2017)
10.1007/s11548-011-0669-y
Automatic detection and classification of nasopharyngeal carcinoma on PET/CT with support vector machine
Bangxian Wu (2011)
10.1111/anae.12537
Big Data – and its contributions to peri‐operative medicine
D. Sessler (2014)
10.1016/S0140-6736(16)31592-6
Progress in evidence-based medicine: a quarter century on
B. Djulbegovic (2017)
10.1001/jamaoto.2018.0602
RNA Oncoimmune Phenotyping of HPV-Positive p16-Positive Oropharyngeal Squamous Cell Carcinomas by Nodal Status
Wesley H. Stepp (2018)
10.1186/1471-2105-14-170
Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
S. Chang (2012)
10.1002/lary.26763
The color of cancer: Margin guidance for oral cancer resection using elastic scattering spectroscopy
G. Grillone (2017)
10.1016/j.ejca.2016.07.021
Prostate cancer changes in clinical presentation and treatments in two decades: an Italian population-based study.
A. Trama (2016)
10.1016/j.radonc.2009.04.014
Meta-analysis of chemotherapy in head and neck cancer (MACH-NC): an update on 93 randomised trials and 17,346 patients.
J. Pignon (2009)
10.1097/RCT.0000000000000667
Diffusion Tensor Imaging of Lumbar Vertebras in Female Adolescent Idiopathic Scoliosis: Initial Findings
D. Wang (2018)
10.1016/j.pcl.2015.12.007
Big Data and Predictive Analytics: Applications in the Care of Children.
S. Suresh (2016)
10.1093/ije/dyp155
Cohort Profile: The Scottish Health Surveys Cohort: linkage of study participants to routinely collected records for mortality, hospital discharge, cancer and offspring birth characteristics in three nationwide studies
L. Gray (2010)
10.1016/j.canlet.2017.06.004
Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma.
Bin Zhang (2017)
10.1016/j.ejca.2015.07.043
Prognoses and improvement for head and neck cancers diagnosed in Europe in early 2000s: The EUROCARE-5 population-based study.
G. Gatta (2015)
10.4258/hir.2015.21.2.67
Health and Social Media: Perfect Storm of Information
L. Fernández-Luque (2015)
10.1001/jama.2017.16611
Benefits and Risks of Machine Learning Decision Support Systems.
Marco D. Huesch (2017)
10.2427/8981
Building reliable evidence from real-world data: methods, cautiousness and recommendations
G. Corrao (2013)
10.1001/jamaoto.2017.2308
Personalized Medicine and the Contradictions and Limits of First-Generation Deescalation Trials in Patients With Human Papillomavirus-Positive Oropharyngeal Cancer.
E. Orlandi (2018)
10.1097/PAS.0000000000000086
A Quantitative Histomorphometric Classifier (QuHbIC) Identifies Aggressive Versus Indolent p16-Positive Oropharyngeal Squamous Cell Carcinoma
J. Lewis (2014)
10.3389/fonc.2018.00131
Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review
A. Jethanandani (2018)
10.1186/1471-2288-14-137
Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints
T. van der Ploeg (2014)
10.1371/journal.pone.0031989
Molecular Prognostic Prediction for Locally Advanced Nasopharyngeal Carcinoma by Support Vector Machine Integrated Approach
X. Wan (2012)
10.1038/ncomms12474
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
K. Yu (2016)
10.1001/jamaoncol.2016.6413
Human-Machine Collaboration in Cancer and Beyond: The Centaur Care Model
Ian M Goldstein (2017)
10.1001/JAMA.1992.03490170092032
Evidence-based medicine. A new approach to teaching the practice of medicine.
G. Guyatt (1992)
10.3233/CBM-160375
A 80-gene set potentially predicts the relapse in laryngeal carcinoma optimized by support vector machine.
B. Yang (2017)
10.1002/hed.24253
Automated analysis of confocal laser endomicroscopy images to detect head and neck cancer
A. Dittberner (2016)
10.1080/00016489.2016.1247984
A recurrence model for laryngeal cancer based on SVM and gene function clustering
Jili Su (2017)
10.1038/nrclinonc.2012.196
Predicting outcomes in radiation oncology—multifactorial decision support systems
P. Lambin (2013)
10.1088/1361-6560/aa71f8
Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method.
C. McIntosh (2017)
10.1038/ncomms5006
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
10.1038/s41598-017-14687-0
Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients
Hesham Aasheesh Abdallah S. R. Aubrey James Andrew Joel S Elhalawani Kanwar Mohamed White Zafereo Wong Beren (2017)
10.1158/1078-0432.CCR-17-0906
Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging
Guolan Lu (2017)
10.3389/fonc.2015.00272
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
Chintan Parmar (2015)
diagnosed at a young age : a Norwegian national cohort study
L Gray (2016)
10.1002/lary.27159
Detecting oropharyngeal carcinoma using multispectral, narrow‐band imaging and machine learning
Shamik Mascharak (2018)
10.1016/j.ctro.2017.11.009
Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy
J. Dean (2018)
10.1016/S1470-2045(17)30445-X
Burden and centralised treatment in Europe of rare tumours: results of RARECAREnet-a population-based study.
G. Gatta (2017)
10.1007/s11548-017-1686-2
Highly immersive virtual reality laparoscopy simulation: development and future aspects
T. Huber (2017)
10.1162/99608F92.F06C6E61
Artificial Intelligence—The Revolution Hasn’t Happened Yet
Michael I. Jordan (2019)
10.1002/mp.12967
Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers
T. Deist (2018)
10.1158/1078-0432.CCR-16-2910
Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma
Bin Zhang (2017)
10.1186/s13073-016-0323-y
Making sense of big data in health research: Towards an EU action plan
C. Auffray (2016)
10.1016/j.radonc.2016.05.015
Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy.
J. Dean (2016)
10.1155/2014/434072
Evaluation and Integration of Genetic Signature for Prediction Risk of Nasopharyngeal Carcinoma in Southern China
Xiuchan Guo (2014)
10.1109/MITP.2014.3
Big Data and Predictive Analytics: What's New?
S. Earley (2014)
10.1155/2014/362738
A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics
S. Li (2014)



This paper is referenced by
10.1007/s11517-019-02031-9
Advanced computing solutions for analysis of laryngeal disorders
H. Turkmen (2019)
10.26574/maedica.2019.14.2.126
Radiomic Machine Learning and Texture Analysis - New Horizons for Head and Neck Oncology.
C. Mirestean (2019)
10.1016/j.ejrad.2019.108755
Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma.
H. Wang (2019)
10.1109/ACCESS.2020.3023743
Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor
Sai Li (2020)
10.1016/j.jacr.2019.05.045
Feeding the Data Monster: Data Science in Head and Neck Cancer for Personalized Therapy.
L. Marcu (2019)
10.1007/978-3-030-06067-1_3
Acute Respiratory Failure in the Oncologic Patient: New Era, New Issues
B. Ferreyro (2018)
10.1186/s12957-020-02091-4
Head and neck cutaneous melanoma: 5-year survival analysis in a Serbian university center
Aleksandar Višnjić (2020)
10.1016/j.ejrad.2020.108910
Computed Tomography Angiography findings can predict massive bleeding in head and neck tumours.
A. Cannavale (2020)
10.3390/cancers11101409
Radiomics and Texture Analysis in Laryngeal Cancer. Looking for New Frontiers in Precision Medicine through Imaging Analysis
C. Chiesa-Estomba (2019)
10.1016/J.FUTURE.2020.09.040
Estimation of laryngeal closure duration during swallowing without invasive X-rays
Shitong Mao (2021)
10.1186/s41199-020-0047-y
Transcriptomics and Epigenomics in head and neck cancer: available repositories and molecular signatures
M. S. Serafini (2020)
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