{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T05:13:56Z","timestamp":1783660436579,"version":"3.55.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Glioblastoma (GB) remains one of the most aggressive brain tumors, with limited survival and high recurrence rates. In most cases, GB recurrence occurs locally, either on the residual tumor after surgery or within 2\u202fcm of the resection cavity\u2014but in rarer cases, tumor cells can spread beyond this margin, leading to distant recurrence. By\n                      <jats:italic>spread<\/jats:italic>\n                      , we refer to the spatial dissemination of tumor cells beyond the typical local site, which can involve distant brain regions or even the leptomeninges, significantly impacting treatment planning and surgical decision-making. This study proposes the application of Machine Learning (ML) and Deep Learning (DL) approaches to MRI data from GB patients in the preoperative phases, aiming to develop predictive models to predict the extent of recurrence spread, on the integrated analysis of clinical, imaging, and instrumental data. Additionally, we plan to design a (semi) automatic segmentation tool for tumor delineation in MRI, which will support both the implementation of the study and serve as a standalone instrument to standardize volume measurement in neuroimaging.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analytics<\/jats:title>\n                    <jats:p>A multicenter retrospective collection of clinical and radiological variables will be performed for all eligible GB patients. Variables will include demographic, surgical, pathological, and preoperative MRI features. Predictive modelling will use classical ML algorithms (e.g., Random Forest, SVM, Multilayer perceptron, etc.) and a 3D U-Net architecture for DL-based image segmentation. Dimensionality reduction (PCA, LASSO, etc.) will be used to prevent overfitting and improve model generalizability. Model performance will be assessed through Area Under the Curve (AUC), P-R curve, F-score, accuracy, sensitivity, specificity, confusion matrix, and Dice score for segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The development of Artificial Intelligence (AI)-based predictive models for GB is expected to provide a major contribution to outcome prediction, early targeted interventions, and personalized care. These tools may support optimized resource allocation, reduce healthcare costs, and improve patient and family outcomes. The findings from this study will serve as a foundation for a future prospective multicenter validation study.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2026.1818594","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T04:25:49Z","timestamp":1783657549000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine and deep learning based on magnetic resonance imaging to segment glioblastoma and predict the spread of recurrence: a multicenter retrospective protocol"],"prefix":"10.3389","volume":"17","author":[{"given":"Luana","family":"Conte","sequence":"first","affiliation":[{"name":"Department of Physics and Chemistry \u201cE. Segr\u00e8\u201d, University of Palermo","place":["Palermo, Italy"]},{"name":"Laboratori Nazionali del Sud, National Institute for Nuclear Physics (INFN)","place":["Catania, Italy"]},{"name":"Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), University of Salento & ASL Lecce","place":["Lecce, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erica","family":"Lo Turco","sequence":"additional","affiliation":[{"name":"Azienda Ospedaliero Universitaria \u201cR. Dulbecco\u201d","place":["Catanzaro, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosaria V.","family":"Abbritti","sequence":"additional","affiliation":[{"name":"Service de Neurochirurgie H\u00f4pital \u201cLariboisi\u00e8re\u201d","place":["Paris, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caterina","family":"Accettura","sequence":"additional","affiliation":[{"name":"Department of Experimental Medicine, University of Salento","place":["Lecce, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Raso","sequence":"additional","affiliation":[{"name":"Department of Physics and Chemistry \u201cE. Segr\u00e8\u201d, University of Palermo","place":["Palermo, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edvige","family":"Iaboni","sequence":"additional","affiliation":[{"name":"Unit of Neurosurgery, Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina","place":["Messina, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ugo","family":"De Giorgi","sequence":"additional","affiliation":[{"name":"Department of Experimental Medicine, University of Salento","place":["Lecce, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giorgio","family":"De Nunzio","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Physics \u201cE. De Giorgi\u201d, University of Salento","place":["Lecce, Italy"]},{"name":"National Institute for Nuclear Physics (INFN)","place":["Lecce, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donato","family":"Cascio","sequence":"additional","affiliation":[{"name":"Department of Physics and Chemistry \u201cE. Segr\u00e8\u201d, University of Palermo","place":["Palermo, Italy"]},{"name":"Laboratori Nazionali del Sud, National Institute for Nuclear Physics (INFN)","place":["Catania, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Caffo","sequence":"additional","affiliation":[{"name":"Unit of Neurosurgery, Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina","place":["Messina, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"v1","DOI":"10.1093\/neuonc\/noac202","article-title":"CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2015\u20132019","volume":"24","author":"Ostrom","year":"2022","journal-title":"Neuro Oncol"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"571","DOI":"10.3390\/cancers17040571","article-title":"Artificial intelligence-assisted drug and biomarker discovery for glioblastoma: a scoping review of the literature","volume":"17","author":"Conte","year":"2025","journal-title":"Cancers"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1056\/NEJMoa043330","article-title":"Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma","volume":"352","author":"Stupp","year":"2005","journal-title":"N Engl J Med"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1001\/jamaoncol.2016.1373","article-title":"Association of the extent of resection with survival in glioblastoma","volume":"2","author":"Brown","year":"2016","journal-title":"JAMA Oncol"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1007\/s11899-015-0291-0","article-title":"The value and relevance of the T cell lymphoma registries and international collaborations: the case of COMPLETE and the T-cell project","volume":"10","author":"Bellei","year":"2015","journal-title":"Curr Hematol Malig Rep"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"4261","DOI":"10.3390\/ijerph17124261","article-title":"A new technological advancement of the drug-induced sleep endoscopy (Dise) procedure: the \u201call in one glance\u201d strategy","volume":"17","author":"Arigliani","year":"2020","journal-title":"Int J Environ Res Public Health"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"164","DOI":"10.14639\/0392-100X-N0409","article-title":"A review of the \u201cOMICS\u201d for management of patients with obstructive sleep apnoea","volume":"40","author":"Conte","year":"2020","journal-title":"Acta Otorhinolaryngol Ital"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"14","DOI":"10.15265\/IY-2014-0020","article-title":"Big data in medicine is driving big changes","volume":"23","author":"Martin-Sanchez","year":"2014","journal-title":"Yearb Med Inform"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1016\/j.athoracsur.2020.03.128","article-title":"Predicting survival after extracorporeal membrane oxygenation by using machine learning","volume":"110","author":"Ayers","year":"2020","journal-title":"Ann Thorac Surg"},{"key":"ref10","volume-title":"Springer Texts in Statistics","author":"James","year":"2013"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big data and machine learning in health care","volume":"319","author":"Beam","year":"2018","journal-title":"JAMA"},{"key":"ref12","volume-title":"Pattern Classification","author":"Duda","year":"2000"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J Cancer"},{"key":"ref14","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4615-5725-8","volume-title":"Feature Extraction, Construction and Selection","author":"Liu","year":"1998"},{"key":"ref15","volume-title":"How Much is the Black Box? 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