{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["CRANIAL Consortium"],"funding":["Cancer Research UK","Wellcome / EPSRC Centre for Interventional and Surgical Sciences","National Institute for Health Research (NIHR)","Wellcome Trust","Engineering and Physical Sciences Research Council"],"pagination":["1046519"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10076706"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13"],"pubmed_abstract":["<h4>Background</h4>Cerebrospinal fluid rhinorrhoea (CSFR) is a common complication following endonasal skull base surgery, a technique that is fundamental to the treatment of pituitary adenomas and many other skull base tumours. The CRANIAL study explored CSFR incidence and related risk factors, particularly skull base repair techniques, <i>via</i> a multicentre prospective observational study. We sought to use machine learning to leverage this complex multicentre dataset for CSFR prediction and risk factor analysis.<h4>Methods</h4>A dataset of 865 cases - 725 transsphenoidal approach (TSA) and 140 expanded endonasal approach (EEA) - with cerebrospinal fluid rhinorrhoea as the primary outcome, was used. Relevant variables were extracted from the data, and prediction variables were divided "],"journal":["Frontiers in oncology"],"pubmed_title":["Machine learning driven prediction of cerebrospinal fluid rhinorrhoea following endonasal skull base surgery: A multicentre prospective observational study."],"pmcid":["PMC10076706"],"funding_grant_id":["ACF-2021-02-013","EP/P012841/1","RCCPDB-Nov21\\100007","EP/P027938/1, EP/R004080/1, EP/P012841/1","203145/Z/16/Z","EP/R004080/1","EP/P027938/1","ACF-2021-18-009"],"pubmed_authors":["Ali AM","McAleavey P","Ahmed S","Halliday J","Ahmed R","Williams A","Lacy P","Ram B","Horsfall HL","Hughes M","Millward CP","Perera A","Bal J","Allison C","Mahmood S","Alalade A","Bahl A","Amarouche M","Rus M","Jenkins A","Patel V","Gatt D","Shapey J","Weir P","Dilnawaz A","Kumaria A","Maripi H","Bandyopadhyay S","Henderson D","Venkatesh A","Bjornson A","Abualsaud S","Al-Mahfoudh R","Hamdan A","Tysome J","CRANIAL Consortium","McSorley N","Abhinav K","Solomou G","Dalton A","Thomas N","Richardson D","Edlmann E","Mirz S","Donnelly N","Shaw S","Santarius T","Matloob S","Stew B","Gilkes CE","Kamel M","Khan MH","Hirst TC","Khan MS","Mathad N","Tsermoulas G","Varma A","Chowdhury YA","Bagchi A","Nadig A","Kalra N","Maroof S","Group NANI","Marwaha P","Khan DZ","Paraskevopoulos D","Hanna B","Glancz LJ","Hogg F","Giamouriadis A","Mendoza N","Naik P","Wang D","Veljanoski D","Bhatt P","Stapleton S","Chadwick A","Cernei C","Boukas A","Bautista M","Murray D","Roman E","Hand C","Maccormac O","Hernandez L","Lobo R","Collaborative BNTR","Hempenstall J","Paluzzi A","Wang Y","Statham P","Nicholson C","Hounat A","Hossain M","Dow G","Sharma R","Moudgil-Joshi J","Sitaraman M","Cabrilo I","Amirthalingam GNK","Fountain DM","Shrestha A","Dorward NL","Malhotra PS","Merola J","Sinha S","Haiderkhan Z","Gnanalingham K","Bhalla R","Schroeder BE","Pollock J","Choi D","Mannion R","Scott C","Al-Barazi S","Javadpour M","Pathmanaban O","Yang SZ","Youssef M","Nair R","Mckeon C","Abrar S","Mirza AB","McSweeney D","Shirazi S","Okasha M","Shoakazemi A","Piper RJ","Cudlip S","Muquit S","Sayal P","Bennett D","Hayhurst C","Mohamed A","Soon WC","Stoyanov D","Phillips N","Grieve J","Martin A","Maratos E","O'Donnell A","Hossain-Ibrahim K","Patkar P","Robertson I","Das A","Kolias AG","Shumon S","Marcus HJ","Hannan CJ"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning driven prediction of cerebrospinal fluid rhinorrhoea following endonasal skull base surgery: A multicentre prospective observational study.","description":"<h4>Background</h4>Cerebrospinal fluid rhinorrhoea (CSFR) is a common complication following endonasal skull base surgery, a technique that is fundamental to the treatment of pituitary adenomas and many other skull base tumours. The CRANIAL study explored CSFR incidence and related risk factors, particularly skull base repair techniques, <i>via</i> a multicentre prospective observational study. We sought to use machine learning to leverage this complex multicentre dataset for CSFR prediction and risk factor analysis.<h4>Methods</h4>A dataset of 865 cases - 725 transsphenoidal approach (TSA) and 140 expanded endonasal approach (EEA) - with cerebrospinal fluid rhinorrhoea as the primary outcome, was used. Relevant variables were extracted from the data, and prediction variables were divided ","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2026-06-03T18:40:50.679Z","creation":"2025-04-19T14:32:08.044Z"},"accession":"S-EPMC10076706","cross_references":{"pubmed":["37035179"],"doi":["10.3389/fonc.2023.1046519"]}}