{"authors":[{"id":null,"fullName":"Keek, Simon A.","name":"Simon A.","surname":"Keek","rank":1,"pid":null},{"id":null,"fullName":"Wesseling, Frederik W. R.","name":"Frederik W. R.","surname":"Wesseling","rank":2,"pid":null},{"id":null,"fullName":"Woodruff, Henry C.","name":"Henry C.","surname":"Woodruff","rank":3,"pid":null},{"id":null,"fullName":"Van Timmeren, Janita E.","name":"Janita E.","surname":"Van Timmeren","rank":4,"pid":null},{"id":null,"fullName":"Nauta, Irene H.","name":"Irene H.","surname":"Nauta","rank":5,"pid":null},{"id":null,"fullName":"Hoffmann, Thomas K.","name":"Thomas K.","surname":"Hoffmann","rank":6,"pid":null},{"id":null,"fullName":"Cavalieri, Stefano","name":"Stefano","surname":"Cavalieri","rank":7,"pid":null},{"id":null,"fullName":"Calareso, Giuseppina","name":"Giuseppina","surname":"Calareso","rank":8,"pid":null},{"id":null,"fullName":"Primakov, Sergey","name":"Sergey","surname":"Primakov","rank":9,"pid":null},{"id":null,"fullName":"Leijenaar, Ralph T. H.","name":"Ralph T. H.","surname":"Leijenaar","rank":10,"pid":null},{"id":null,"fullName":"Licitra, Lisa","name":"Lisa","surname":"Licitra","rank":11,"pid":null},{"id":null,"fullName":"Ravanelli, Marco","name":"Marco","surname":"Ravanelli","rank":12,"pid":null},{"id":null,"fullName":"Scheckenbach, Kathrin","name":"Kathrin","surname":"Scheckenbach","rank":13,"pid":null},{"id":null,"fullName":"Poli, Tito","name":"Tito","surname":"Poli","rank":14,"pid":null},{"id":null,"fullName":"Lanfranco, Davide","name":"Davide","surname":"Lanfranco","rank":15,"pid":null},{"id":null,"fullName":"Vergeer, Marije R.","name":"Marije R.","surname":"Vergeer","rank":16,"pid":null},{"id":null,"fullName":"Leemans, C. René","name":"C. René","surname":"Leemans","rank":17,"pid":null},{"id":null,"fullName":"Brakenhoff, Ruud H.","name":"Ruud H.","surname":"Brakenhoff","rank":18,"pid":null},{"id":null,"fullName":"Hoebers, Frank J. P.","name":"Frank J. P.","surname":"Hoebers","rank":19,"pid":null},{"id":null,"fullName":"Lambin, Philippe","name":"Philippe","surname":"Lambin","rank":20,"pid":null}],"openAccessColor":null,"publiclyFunded":null,"eoscIfGuidelines":null,"type":"other","language":{"code":"eng","label":"English"},"countries":null,"subjects":[{"subject":{"scheme":"FOS","value":"03 medical and health sciences"},"provenance":null},{"subject":{"scheme":"FOS","value":"0302 clinical medicine"},"provenance":null},{"subject":{"scheme":"keyword","value":"DDC 570 / Life sciences"},"provenance":null},{"subject":{"scheme":"keyword","value":"radiomics"},"provenance":null},{"subject":{"scheme":"keyword","value":"Head and Neck Neoplasms"},"provenance":null},{"subject":{"scheme":"keyword","value":"Machine learning"},"provenance":null},{"subject":{"scheme":"keyword","value":"head and neck cancer"},"provenance":null},{"subject":{"scheme":"keyword","value":"Precision Medicine"},"provenance":null},{"subject":{"scheme":"keyword","value":"DDC 610 / Medicine & health"},"provenance":null},{"subject":{"scheme":"keyword","value":"Maschinelles Lernen"},"provenance":null},{"subject":{"scheme":"SDG","value":"3. Good health"},"provenance":null},{"subject":{"scheme":"keyword","value":"survival study"},"provenance":null}],"mainTitle":"A prospectively validated prognostic model for patients with locally advanced squamous cell carcinoma of the head and neck based on radiomics of computed tomography images","subTitle":null,"descriptions":["Background: Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve outcomes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard. Patient and methods: Data of 666 retrospective- and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivariable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radiomics features. Patient risk stratification in three groups was assessed through Kaplan–Meier (KM) curves. A log-rank test was performed for significance (p-value < 0.05). The prognostic accuracy was reported through the concordance index (CI). Results: A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups (p < 0∙01, CI of 0.79 as validation). Conclusion: A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8."],"publicationDate":"2021-06-29","publisher":null,"embargoEndDate":"2022-08-31","sources":null,"formats":null,"contributors":null,"coverages":null,"bestAccessRight":null,"container":null,"documentationUrls":null,"codeRepositoryUrl":null,"programmingLanguage":null,"contactPeople":null,"contactGroups":null,"tools":null,"size":null,"version":null,"geoLocations":null,"id":"doi_________::280b6f324751cb037cd8fa74d46250ae","originalIds":["50|datacite____::280b6f324751cb037cd8fa74d46250ae","10.18725/oparu-44321"],"pids":[{"scheme":"doi","value":"10.18725/oparu-44321"}],"dateOfCollection":null,"lastUpdateTimeStamp":null,"indicators":{"citationImpact":{"citationCount":0.0,"influence":2.1746283E-9,"popularity":1.272318E-9,"impulse":0.0,"citationClass":"C5","influenceClass":"C5","impulseClass":"C5","popularityClass":"C5"}},"projects":[{"id":"corda__h2020::7d386b683cadfaf9aec343d449010b74","code":"952103","acronym":"EuCanImage","title":"A European Cancer Image Platform Linked to Biological and Health Data for Next-Generation Artificial Intelligence and Precision Medicine in Oncology","funder":"European Commission","pids":[{"scheme":"doi","value":"10.3030/952103"}]},{"id":"corda__h2020::310bdc5c18d40affab829a1bfc7321ee","code":"766276","acronym":"PREDICT","title":"A new era in personalised medicine: Radiomics as decision support tool for diagnostics and theragnostics in oncology","funder":"European Commission","pids":[{"scheme":"doi","value":"10.3030/766276"}]},{"id":"corda__h2020::a24da50b5e9dd26753efe8780d2e60c3","code":"952172","acronym":"CHAIMELEON","title":"Accelerating the lab to market transition of AI tools for cancer management","funder":"European Commission","pids":[{"scheme":"doi","value":"10.3030/952172"}]},{"id":"corda__h2020::691882059070d2aa7a4ac147c19fc2a9","code":"694812","acronym":"HYPOXIMMUNO","title":"Tackling the Achilles Heel of Immunotherapy: Validating imaging biomarkers and targeting the immunological niche of tumour hypoxia","funder":"European Commission","pids":[{"scheme":"doi","value":"10.3030/694812"}]},{"id":"corda__h2020::ef1799deb5fb50dc545cf8aca6c530b1","code":"689715","acronym":"BD2Decide","title":"Big Data and models for personalized Head and Neck Cancer Decision support","funder":"European Commission","pids":[{"scheme":"doi","value":"10.3030/689715"}]},{"id":"corda__h2020::d475a742a5492aba84678b4a911f9692","code":"957565","acronym":"AUTO.DISTINCT","title":"A fully automated deep learning-based software for fast, robust and accurate detection and segmentation of tumours and metastasis","funder":"European Commission","pids":[{"scheme":"doi","value":"10.3030/957565"}]}],"organizations":[{"legalName":"Maastricht University","acronym":"UM","id":"openorgs____::a7e0018f6064f0dab7a8c9a48a61a1c6","pids":[{"scheme":"FundRef","value":"501100011097"},{"scheme":"mag_id","value":"34352273"},{"scheme":"ROR","value":"https://ror.org/02jz4aj89"},{"scheme":"RRID","value":"RRID:SCR_011359"},{"scheme":"RRID","value":"RRID:nlx_144070"},{"scheme":"wikidata","value":"Q1137652"},{"scheme":"Wikidata","value":"Q1137652"},{"scheme":"fundref","value":"501100011097"},{"scheme":"FundRef","value":"501100001835"},{"scheme":"GRID","value":"grid.5012.6"},{"scheme":"OrgRef","value":"542667"},{"scheme":"PIC","value":"999975911"},{"scheme":"fundref","value":"501100001835"},{"scheme":"ISNI","value":"0000000104816099"},{"scheme":"OrgReg","value":"NL0008"}],"countries":[{"code":"NL","label":"Netherlands"}],"websiteurl":"http://www.maastrichtuniversity.nl/"}],"communities":[{"code":"cancer-research","label":"Cancer Research","provenance":null},{"code":"netherlands","label":"Netherlands Research Portal","provenance":null}],"collectedFrom":[{"key":"openaire____::9e3be59865b2c1c335d32dae2fe7b254","value":"Datacite"}],"instances":[{"pids":[{"scheme":"doi","value":"10.18725/oparu-44321"}],"license":"CC BY","type":"Other ORP type","urls":["https://dx.doi.org/10.18725/oparu-44321"],"publicationDate":"2021-06-29","refereed":"nonPeerReviewed","hostedBy":{"key":"openaire____::55045bd2a65019fd8e6741a755395c8c","value":"Unknown Repository"},"collectedFrom":{"key":"openaire____::9e3be59865b2c1c335d32dae2fe7b254","value":"Datacite"}}],"links":[{"header":{"relationType":"resultProject","relationClass":"isProducedBy","relatedIdentifier":"corda__h2020::7d386b683cadfaf9aec343d449010b74","relatedRecordType":"project","relationProvenance":"iis","trust":"0.897"},"collectedfrom":[{"dsId":"openaire____::a55eb91348674d853191f4f4fd73d078","dsName":"CORDA - 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