ArticleScientific reports2020
Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer.
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30citing papers in PubMed, 2 pooled it
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment.Neuroradiology · 2022Pooled it
- Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.European journal of nuclear medicine and molecular imaging · 2022Pooled it
- Longitudinal radiomics of liver metastases during magnetic resonance-guided radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Review
- Deep learning model using contrast-enhanced CT for predicting overall survival in oropharyngeal squamous cell carcinoma: a prospective multicenter study.European radiology · 2026Article
- Cross-modal fusion of cytomorphology andFrontiers in immunology · 2026Article
- Radiomic-Based Machine Learning Classifiers for HPV Status Prediction in Oropharyngeal Cancer: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Predicting tumor recurrence site after reirradiation in head and neck cancer: a retrospective external validation of a published [18F]-FDG PET radiomic signature.La Radiologia medica · 2025Article
- The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review.Oral and maxillofacial surgery · 2025Review
- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- Prognostic modeling in head and neck cancer: deep learning or handcrafted radiomics?BJR artificial intelligence · 2025Review
- A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients.Scientific reports · 2024Article
- Radiomics Features in Predicting Human Papillomavirus Status in Oropharyngeal Squamous Cell Carcinoma: A Systematic Review, Quality Appraisal, and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Review
- Making head and neck cancer clinical data Findable-Accessible-Interoperable-Reusable to support multi-institutional collaboration and federated learning.BJR artificial intelligence · 2024Article
- MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures.Biomarker research · 2023Article
- CT-based radiomics can identify physiological modifications of bone structure related to subjects' age and sex.La Radiologia medica · 2023Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- Development and usage of an anesthesia data warehouse: lessons learnt from a 10-year project.Journal of clinical monitoring and computing · 2023Article
- Artificial intelligence to predict outcomes of head and neck radiotherapy.Clinical and translational radiation oncology · 2023Article
- A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples.Frontiers in microbiology · 2023Review
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Authors and funding
33 authors.
Marta Bogowicz *University Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland. marta.bogowicz@usz.ch.ORCID http://orcid.org/0000-0002-4747-5375
Arthur Jochems *GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands.
Timo M DeistGROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands.
Stephanie Tanadini-LangUniversity Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland.
Shao Hui HuangPrincess Margaret Cancer Center- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada.ORCID http://orcid.org/0000-0002-8072-4388
Biu ChanPrincess Margaret Cancer Center- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada.
John N WaldronPrincess Margaret Cancer Center- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada.
Scott BratmanPrincess Margaret Cancer Center- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada.ORCID http://orcid.org/0000-0001-8610-4908
Brian O'SullivanPrincess Margaret Cancer Center- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada.
Oliver RiestererUniversity Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland.
Gabriela StuderUniversity Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland.
Jan UnkelbachUniversity Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland.
Samir BarakatGROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands.
Ruud H BrakenhoffAmsterdam UMC, Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Amsterdam, The Netherlands.
Irene NautaAmsterdam UMC, Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Amsterdam, The Netherlands.
Silvia E GazzaniParma University Hospital, Radiology Department, Parma, Italy.
Giuseppina CalaresoIRCCS Fondazione Istituto Nazionale dei Tumori, Radiology Department, Milan, Italy.
Kathrin ScheckenbachUniversity Hospital Duesseldorf, Heinrich-Heine-University, Department of Otorhinolaryngology & Head/Neck, Surgery, Duesseldorf, Germany.
Frank HoebersDepartment of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre, Department of Radiation Oncology, Maastricht, The Netherlands.
Frederik W R WesselingDepartment of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre, Department of Radiation Oncology, Maastricht, The Netherlands.
Simon KeekGROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands.
Sebastian SanduleanuGROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands.
Ralph T H LeijenaarGROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands.
Marije R VergeerAmsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiation Oncology, Amsterdam, The Netherlands.
C René LeemansAmsterdam UMC, Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Amsterdam, The Netherlands.
Chris H J TerhaardUniversity Medical Center Utrecht, Department of Radiotherapy, Utrecht, The Netherlands.
Michiel W M van den BrekelThe Netherlands Cancer Institute, Department of Head and Neck Oncology and Surgery, Amsterdam, The Netherlands.
Olga Hamming-VriezeThe Netherlands Cancer Institute, Department of Radiation Oncology, Amsterdam, The Netherlands.
Martijn A van der HeijdenThe Netherlands Cancer Institute, Department of Head and Neck Oncology and Surgery, Amsterdam, The Netherlands.
Hesham M ElhalawaniDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0001-9848-2623
Clifton D FullerDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0002-5264-3994
Matthias GuckenbergerUniversity Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zurich, Switzerland.
Philippe LambinGROW-School for Oncology and Developmental Biology-Maastricht University Medical Centre-, Department of Precision Medicine, The D Lab: Decision Support for Precision Medicine-, Maastricht, The Netherlands.ORCID http://orcid.org/0000-0002-9034-0177
Funding
Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8MTargeting the p53 pathway for reversal of oral premalignancyP50CA097007 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI MYERS, JEFFREY NICHOLAS · 2002 to 2012
$26.1MUsing Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Establish Objective Clinical Outcome Measures for Mandibular OsteoradionecrosisR01DE025248 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LAI, STEPHEN Y · 2016 to 2020
$2.9MSMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer TreatmentR01CA214825 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI CANAHUATE, GUADALUPE, FULLER, CLIFTON DAVID · 2017 to 2019
$1.1Mimaging Radiation-Associated Dysphagia (iRAD)R01CA218148 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI FULLER, CLIFTON DAVID, HUTCHESON, KATHERINE ARNOLD · 2017 to 2019
$810kFellow and Resident Radiation Oncology iNtensive Training in Imaging and Informatics to Empower Research Careers (FRONTI2ER)R25EB025787 · NIBIB · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DAS, PRAJNAN, FULLER, CLIFTON DAVID · 2018 to 2022
$535kUsing Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Establish Objective Clinical Outcome Measures for Mandibular OsteoradionecrosisR56DE025248 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LAI, STEPHEN Y · 2015 to 2015
$470kNCI NIH HHS P30 CA016672NCI NIH HHS P50 CA097007NCI NIH HHS R01 CA214825NCI NIH HHS R01 CA218148NIBIB NIH HHS R25 EB025787NIDCR NIH HHS R01 DE025248NIDCR NIH HHS R56 DE025248
Abstract
A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data leaving the hospitals ("privacy-preserving" distributed learning). This study tested feasibility of distributed learning of radiomics data for prediction of two year overall survival and HPV status in head and neck cancer (HNC) patients. Pretreatment CT images were collected from 1174 HNC patients in 6 different cohorts. 981 radiomic features were extracted using Z-Rad software implementation. Hierarchical clustering was performed to preselect features. Classification was done using logistic regression. In the validation dataset, the receiver operating characteristics (ROC) were compared between the models trained in the centralized and distributed manner. No difference in ROC was observed with respect to feature selection. The logistic regression coefficients were identical between the methods (absolute difference <10
Indexed as
Data AccuracyDeep LearningPrivacyHead and Neck NeoplasmsHumansImage Interpretation, Computer-AssistedPapillomaviridaePapillomavirus InfectionsPrognosisRetrospective StudiesROC CurveSurvival RateTomography, X-Ray Computed
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