{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T02:39:41Z","timestamp":1792550381570,"version":"4.5.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Neurooncol"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1007\/s11060-026-05833-8","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T02:20:58Z","timestamp":1792549258000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Opportunities and challenges of artificial intelligence and digital pathology in adult CNS tumors"],"prefix":"10.1007","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6235-6227","authenticated-orcid":false,"given":"Mirna","family":"Lechpammer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacqueline T.","family":"Norrell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaime N.","family":"Parnas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5441-4119","authenticated-orcid":false,"given":"Morana","family":"Vojnic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan H.","family":"Sherman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,21]]},"reference":[{"issue":"8","key":"5833_CR1","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1093\/neuonc\/noab106","volume":"23","author":"DN Louis","year":"2021","unstructured":"Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R, von Deimling A, Ellison DW (2021) The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-Oncology 23(8):1231\u20131251. https:\/\/doi.org\/10.1093\/neuonc\/noab106","journal-title":"Neuro-Oncology"},{"issue":"5","key":"5833_CR2","doi-asserted-by":"publisher","first-page":"100240","DOI":"10.1016\/j.clinme.2024.100240","volume":"24","author":"A Lerner","year":"2024","unstructured":"Lerner A, Palmer K, Campion T, Millner TO, Scott E, Lorimer C, Paraskevopoulos D, McKenna G, Marino S, Lewis R, Plowman N (2024) Gliomas in adults: guidance on investigations, diagnosis, treatment and surveillance. Clin Med (Northfield Il) 24(5):100240. https:\/\/doi.org\/10.1016\/j.clinme.2024.100240","journal-title":"Clin Med (Northfield Il)"},{"issue":"12","key":"5833_CR3","doi-asserted-by":"publisher","first-page":"1710","DOI":"10.5858\/arpa.2013-0093-CP","volume":"137","author":"L Pantanowitz","year":"2013","unstructured":"Pantanowitz L, Sinard JH, Henricks WH, Fatheree LA, Carter AB, Contis L, Beckwith BA, Evans AJ, Lal A, Parwani AV (2013) Validating whole slide imaging for diagnostic purposes in pathology: guideline from the college of American pathologists pathology and laboratory quality Center. Arch Pathol Lab Med 137(12):1710\u20131722. https:\/\/doi.org\/10.5858\/arpa.2013-0093-CP","journal-title":"Arch Pathol Lab Med"},{"issue":"3","key":"5833_CR4","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1111\/his.13760","volume":"74","author":"M Salto-Tellez","year":"2019","unstructured":"Salto-Tellez M, Maxwell P, Hamilton PW (2019) Artificial intelligence\u2014the third revolution in pathology. Histopathology 74(3):372\u2013376. https:\/\/doi.org\/10.1111\/his.13760","journal-title":"Histopathology"},{"issue":"4","key":"5833_CR5","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1038\/s41416-020-01122-x","volume":"124","author":"A Echle","year":"2021","unstructured":"Echle A, Rindtorff NT, Brinker TJ, Luedde T, Pearson AT, Kather JN (2021) Deep learning in cancer pathology: a new generation of clinical biomarkers. Br J Cancer 124(4):686\u2013696. https:\/\/doi.org\/10.1038\/s41416-020-01122-x","journal-title":"Br J Cancer"},{"key":"5833_CR6","unstructured":"Food and Drug Administration (2016) Technical performance assessment of digital pathology whole slide imaging devices: guidance for industry and food and Drug administration staff. Center for Devices and Radiological Health, Rockville, MD. https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/technical-performance-assessment-digital-pathology-whole-slide-imaging-devices"},{"issue":"12","key":"5833_CR7","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1136\/jclinpath-2017-204644","volume":"70","author":"BJ Williams","year":"2017","unstructured":"Williams BJ, Bottoms D, Treanor D (2017) Future-proofing pathology: the case for clinical adoption of digital pathology. J Clin Pathol 70(12):1010\u20131018. https:\/\/doi.org\/10.1136\/jclinpath-2017-204644","journal-title":"J Clin Pathol"},{"issue":"11","key":"5833_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1002\/alz.70775","volume":"21","author":"AM Rosado","year":"2025","unstructured":"Rosado AM, Vizcarra JC, Sharma SRR, Keene CD, White CLI, Kim A, Sl F, Gg K, Cn C, Me F, Pearce TM, Dugger BN, Gutman DA (2025) Digital neuropathology of neurodegenerative disorders: foundations, research advances, and future directions. Alzheimers Dementia 21(11):21. https:\/\/doi.org\/10.1002\/alz.70775","journal-title":"Alzheimers Dementia"},{"key":"5833_CR9","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.csbj.2018.01.001","volume":"16","author":"D Komura","year":"2018","unstructured":"Komura D, Ishikawa S (2018) Machine learning methods for histopathological image analysis. Comput Struct Biotechnol J 16:34\u201342. https:\/\/doi.org\/10.1016\/j.csbj.2018.01.001","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"5833_CR10","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s40708-025-00257-y","volume":"12","author":"S Iftikhar","year":"2025","unstructured":"Iftikhar S, Anjum N, Siddiqui AB, Ur Rehman M, Ramzan N (2025) Explainable CNN for brain tumor detection and classification through XAI based key features identification. Brain Inf 12(1):10. https:\/\/doi.org\/10.1186\/s40708-025-00257-y","journal-title":"Brain Inf"},{"issue":"11","key":"5833_CR11","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1038\/s41571-019-0252-y","volume":"16","author":"K Bera","year":"2019","unstructured":"Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A (2019) Artificial intelligence in digital pathology \u2014 new tools for diagnosis and precision oncology. Nat Rev Clin Oncol 16(11):703\u2013715. https:\/\/doi.org\/10.1038\/s41571-019-0252-y","journal-title":"Nat Rev Clin Oncol"},{"issue":"3","key":"5833_CR12","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1002\/path.5331","volume":"249","author":"E Abels","year":"2019","unstructured":"Abels E, Pantanowitz L, Aeffner F, Zarella MD, Van der Laak J, Bui MM, Vemuri VN, Parwani AV, Gibbs J, Agosto-Arroyo E, Beck AH, Kozlowski C (2019) Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the digital pathology association. J Pathol 249(3):286\u2013294. https:\/\/doi.org\/10.1002\/path.5331","journal-title":"J Pathol"},{"issue":"2","key":"5833_CR13","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1109\/JBHI.2020.3032060","volume":"25","author":"K Stacke","year":"2020","unstructured":"Stacke K, Eilertsen G, Unger J, Lundstr\u00f6m C (2020) Measuring domain shift for deep learning in histopathology. IEEE J Biomed Health Inf 25(2):325\u2013336. https:\/\/doi.org\/10.1109\/JBHI.2020.3032060","journal-title":"IEEE J Biomed Health Inf"},{"issue":"3","key":"5833_CR14","doi-asserted-by":"publisher","first-page":"578","DOI":"10.3390\/cancers12030578","volume":"12","author":"S Rathore","year":"2020","unstructured":"Rathore S, Niazi T, Iftikhar MA, Chaddad A (2020) Glioma grading via analysis of digital pathology images using machine learning. Cancers (Basel) 12(3):578. https:\/\/doi.org\/10.3390\/cancers12030578","journal-title":"Cancers (Basel)"},{"issue":"1","key":"5833_CR15","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1038\/s44303-024-00020-8","volume":"2","author":"JP Redlich","year":"2024","unstructured":"Redlich JP, Feuerhake F, Weis J, Schaadt NS, Teuber-Hanselmann S, Buck C, Luttmann S, Eberle A, Nikolin S, Appenzeller A, Portmann A, Homeyer A (2024) Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review. npj Imag 2(1):16. https:\/\/doi.org\/10.1038\/s44303-024-00020-8","journal-title":"npj Imag"},{"issue":"1","key":"5833_CR16","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1093\/neuonc\/noaa163","volume":"23","author":"L Jin","year":"2021","unstructured":"Jin L, Shi F, Chun Q, Chen H, Ma Y, Wu S, Hameed NF, Mei C, Lu J, Zhang J, Aibaidula A, Shen D, Wu J (2021) Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers. Neuro-Oncology 23(1):44\u201352. https:\/\/doi.org\/10.1093\/neuonc\/noaa163","journal-title":"Neuro-Oncology"},{"issue":"1","key":"5833_CR17","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s43856-023-00276-y","volume":"3","author":"S Steyaert","year":"2023","unstructured":"Steyaert S, Qiu YL, Zheng Y, Mukherjee P, Vogel H, Gevaert O (2023) Multimodal deep learning to predict prognosis in adult and pediatric brain tumors. Commun Med (Lond) 3(1):44. https:\/\/doi.org\/10.1038\/s43856-023-00276-y","journal-title":"Commun Med (Lond)"},{"issue":"6","key":"5833_CR18","doi-asserted-by":"publisher","first-page":"382","DOI":"10.4132\/jptm.2025.07.10","volume":"59","author":"M Sharma","year":"2025","unstructured":"Sharma M, Kumari K, Navpreet N, Bharti S, Kumari R (2025) Attitudes toward artificial intelligence in pathology: a survey-based study of pathologists in northern India. J Pathol Transl Med 59(6):382\u2013389. https:\/\/doi.org\/10.4132\/jptm.2025.07.10","journal-title":"J Pathol Transl Med"},{"key":"5833_CR19","doi-asserted-by":"publisher","first-page":"108902","DOI":"10.1016\/j.compbiomed.2024.108902","volume":"179","author":"R Nakagaki","year":"2024","unstructured":"Nakagaki R, Debsarkar SS, Kawanaka H, Aronow BJ, Prasath VSB (2024) Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data. Comput Biol Med 179:108902. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108902","journal-title":"Comput Biol Med"},{"key":"5833_CR20","doi-asserted-by":"publisher","unstructured":"Lv Q, Liu Y, Sun Y, Wu M (2024) Insight into deep learning for glioma IDH medical image analysis: a systematic review. Med (Baltim) 103(7):e37150. https:\/\/doi.org\/10.1097\/MD.0000000000037150","DOI":"10.1097\/MD.0000000000037150"},{"issue":"1","key":"5833_CR21","doi-asserted-by":"publisher","first-page":"22623","DOI":"10.1038\/s41598-022-26170-6","volume":"12","author":"B Liechty","year":"2022","unstructured":"Liechty B, Xu Z, Zhang Z, Slocum C, Bahadir CD, Sabuncu MR, Pisapia DJ (2022) Machine learning can aid in prediction of IDH mutation from H&E-stained histology slides in infiltrating gliomas. Sci Rep 12(1):22623. https:\/\/doi.org\/10.1038\/s41598-022-26170-6","journal-title":"Sci Rep"},{"issue":"8","key":"5833_CR22","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1038\/s43018-020-0085-8","volume":"1","author":"Y Fu","year":"2020","unstructured":"Fu Y, Jung AW, Torne RV, Gonzalez S, V\u00f6hringer H, Shmatko A, Yates LR, Jimenez-Linan M, Moore L, Gerstung M (2020) Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat Cancer 1(8):800\u2013810. https:\/\/doi.org\/10.1038\/s43018-020-0085-8","journal-title":"Nat Cancer"},{"key":"5833_CR23","doi-asserted-by":"publisher","unstructured":"Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Vel\u00e1zquez Vega JE, Brat DJ, Cooper LAD (2018) Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci USA 115(13):E2970\u2013E2979. https:\/\/doi.org\/10.1073\/pnas.1717139115","DOI":"10.1073\/pnas.1717139115"},{"key":"5833_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2024.111397","volume-title":"IDH-mutant glioma risk stratification via whole slide images: identifying pathological feature associations","author":"X Wang","year":"2025","unstructured":"Wang X, Wang Z, Wang W, Liu Z, Ma Z, Guo Y, Su D, Sun Q, Pei D, Duan W (2025) IDH-mutant glioma risk stratification via whole slide images: identifying pathological feature associations. iScience 28. https:\/\/doi.org\/10.1016\/j.isci.2024.111397"},{"issue":"5","key":"5833_CR25","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1016\/S1470-2045(19)30098-1","volume":"20","author":"P Kickingereder","year":"2019","unstructured":"Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, Brugnara G, Schell M, Kessler T, Foltyn M, Harting I, Sahm F, Prager M, Nowosielski M, Wick A, Nolden M, Radbruch A, Debus J, Schlemmer H-P, Heiland S, Platten M, von Deimling A, van den Bent MJ, Gorlia T, Wick W, Bendszus M, Maier-Hein KH (2019) Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol 20(5):728\u2013740. https:\/\/doi.org\/10.1016\/S1470-2045(19)30098-1","journal-title":"Lancet Oncol"},{"key":"5833_CR26","doi-asserted-by":"publisher","first-page":"799662","DOI":"10.3389\/fonc.2022.799662","volume":"12","author":"TC Booth","year":"2022","unstructured":"Booth TC, Grzeda M, Chelliah A, Roman A, Al Busaidi A, Dragos C, Shuaib H, Luis A, Mirchandani A, Alparslan B, Mansoor N, Lavrador J, Vergani F, Ashkan K, Modat M, Ourselin S (2022) Imaging biomarkers of glioblastoma treatment response: a systematic review and meta-analysis of recent machine learning studies. Front Oncol 12:799662. https:\/\/doi.org\/10.3389\/fonc.2022.799662","journal-title":"Front Oncol"},{"issue":"1","key":"5833_CR27","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1038\/s41698-024-00575-0","volume":"8","author":"S Khalighi","year":"2024","unstructured":"Khalighi S, Reddy K, Midya A, Pandav KB, Madabhushi A, Abedalthagafi M (2024) Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. Npj Precis Onc 8(1):80. https:\/\/doi.org\/10.1038\/s41698-024-00575-0","journal-title":"Npj Precis Onc"},{"issue":"7","key":"5833_CR28","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.3390\/cancers14071627","volume":"14","author":"M Lechpammer","year":"2022","unstructured":"Lechpammer M, Rao R, Shah S, Mirheydari M, Bhattacharya D, Koehler A, Toukam DK, Haworth KJ, Pomeranz Krummel D, Sengupta S (2022) Advances in immunotherapy for the treatment of adult glioblastoma: overcoming chemical and physical barriers. Cancers (Basel) 14(7):1627. https:\/\/doi.org\/10.3390\/cancers14071627","journal-title":"Cancers (Basel)"},{"issue":"9","key":"5833_CR29","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.3390\/biomedicines13092285","volume":"13","author":"K Evangelou","year":"2025","unstructured":"Evangelou K, Kotsantis I, Kalyvas A, Kyriazoglou A, Economopoulou P, Velonakis G, Gavra M, Psyrri A, Boviatsis EJ, Stavrinou LC (2025) Artificial intelligence in the diagnosis and treatment of brain gliomas. Biomedicines 13(9):2285. https:\/\/doi.org\/10.3390\/biomedicines13092285","journal-title":"Biomedicines"},{"issue":"1","key":"5833_CR30","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1002\/path.6263","volume":"263","author":"H Zhou","year":"2024","unstructured":"Zhou H, Watson M, Bernadt CT, Lin S, Lin CY, Ritter JH, Wein A, Mahler S, Rawal S, Govindan R, Yang C, Cote RJ (2024) AI-guided histopathology predicts brain metastasis in lung cancer patients. J Pathol 263(1):89\u201398. https:\/\/doi.org\/10.1002\/path.6263","journal-title":"J Pathol"},{"issue":"7861","key":"5833_CR31","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1038\/s41586-021-03512-4","volume":"594","author":"MY Lu","year":"2021","unstructured":"Lu MY, Chen TY, Williamson DFK, Zhao M, Shady M, Lipkova J, Mahmood F (2021) AI-based pathology predicts origins for cancers of unknown primary. Nature 594(7861):106\u2013110. https:\/\/doi.org\/10.1038\/s41586-021-03512-4","journal-title":"Nature"},{"issue":"5","key":"5833_CR32","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1093\/neuonc\/noae042","volume":"26","author":"M Lechpammer","year":"2024","unstructured":"Lechpammer M (2024) Can confocal laser endomicroscopy replace frozen section in diagnosis of brain tumors? A definite maybe. Neuro-Oncology 26(5):933\u2013934. https:\/\/doi.org\/10.1093\/neuonc\/noae042","journal-title":"Neuro-Oncology"},{"issue":"1","key":"5833_CR33","doi-asserted-by":"publisher","first-page":"108","DOI":"10.3390\/brainsci14010108","volume":"14","author":"M Lechpammer","year":"2024","unstructured":"Lechpammer M, Todd A, Tang V, Morningstar T, Borowsky A, Shahlaie K, Kintner JA, McPherson JD, Bishop JW, Fereidouni F, Harmany ZT, Coley N, Zagzag D, Wong JWH, Tao J, Hesson LB, Burnett L, Levenson R (2024) Neuropathological applications of microscopy with ultraviolet surface excitation (MUSE): a concordance study of human primary and metastatic brain tumors. Brain Sci 14(1):108. https:\/\/doi.org\/10.3390\/brainsci14010108","journal-title":"Brain Sci"},{"issue":"Suppl 1","key":"5833_CR34","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1093\/neuros\/nyz310_634","volume":"66","author":"TC Hollon","year":"2019","unstructured":"Hollon TC, Pandian B, Khalsa SSS, D\u2019amico R, Sisti MB, Bruce JN, Muraszko KM, Thompson GB, Freudiger C, Hervey-Jumper SL, McKhann GM, Camelo-Piragua S, Canoll PD, Lee H, Orringer DA (2019) Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Neurosurgery 66(Suppl 1):310\u2013634. https:\/\/doi.org\/10.1093\/neuros\/nyz310_634","journal-title":"Neurosurgery"},{"issue":"6","key":"5833_CR35","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1213\/ANE.0000000000007088","volume":"139","author":"ED Pelletier","year":"2024","unstructured":"Pelletier ED, Jeffries SD, Song K, Hemmerling TM (2024) Comparative analysis of machine-learning model performance in image analysis: the impact of dataset diversity and size. Anesth Analg 139(6):1332\u20131339. https:\/\/doi.org\/10.1213\/ANE.0000000000007088","journal-title":"Anesth Analg"},{"issue":"1","key":"5833_CR36","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D (2019) Key challenges for delivering clinical impact with artificial intelligence. BMC Med 17(1):195. https:\/\/doi.org\/10.1186\/s12916-019-1426-2","journal-title":"BMC Med"},{"issue":"1","key":"5833_CR37","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44\u201356. https:\/\/doi.org\/10.1038\/s41591-018-0300-7","journal-title":"Nat Med"},{"key":"5833_CR38","doi-asserted-by":"publisher","unstructured":"Holzinger A, Biemann C, Pattichis CS, Kell DB (2017) What do we need to build explainable AI systems for the medical domain? arXiv. https:\/\/doi.org\/10.48550\/arXiv.1712.09923","DOI":"10.48550\/arXiv.1712.09923"},{"key":"5833_CR39","unstructured":"US Food and Drug Administration (2021) Machine learning (AI\/ML)-based software as a medical device (SaMD) action plan. https:\/\/www.fda.gov\/media\/145022\/download"},{"issue":"4","key":"5833_CR40","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1093\/medlaw\/fwad013","volume":"31","author":"C Jones","year":"2023","unstructured":"Jones C, Thornton J, Wyatt JC (2023) Artificial intelligence and clinical decision support: clinicians\u2019 perspectives on trust, trustworthiness, and liability. Med Law Rev 31(4):501\u2013520. https:\/\/doi.org\/10.1093\/medlaw\/fwad013","journal-title":"Med Law Rev"},{"issue":"1","key":"5833_CR41","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1038\/s41379-021-00919-2","volume":"35","author":"V Baxi","year":"2022","unstructured":"Baxi V, Edwards R, Montalto M, Saha S (2022) Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol 35(1):23\u201332. https:\/\/doi.org\/10.1038\/s41379-021-00919-2","journal-title":"Mod Pathol"},{"key":"5833_CR42","unstructured":"European Medicines Agency (2020) EMA regulatory science to 2025: strategic reflection. European Medicines Agency, Amsterdam"},{"key":"5833_CR43","doi-asserted-by":"publisher","unstructured":"Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ, Ashrafian H, Beam AL, Collins GS, Darzi A, Deeks JJ, ElZarrad MK, Espinoza C, Esteva A, Faes L, Ferrante di Ruffano L, Fletcher J, Golub R, Harvey H, Haug C, Holmes C, Jonas A, Keane PA, Kelly CJ, Lee AY, Lee CS, Manna E, Matcham J, McCradden M, Moher D, Monteiro J, Mulrow C, Oakden-Rayner L, Paltoo D, Panico MB, Price G, Rowley S, Savage R, Sarkar R, Vollmer SJ, Yau C et al. (2020) Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health 2(10):e549\u2013e560. https:\/\/doi.org\/10.1016\/S2589-7500(20)30219-3","DOI":"10.1016\/S2589-7500(20)30219-3"},{"issue":"1","key":"5833_CR44","doi-asserted-by":"publisher","first-page":"62","DOI":"10.3390\/bioengineering12010062","volume":"12","author":"A Abdusalomov","year":"2025","unstructured":"Abdusalomov A, Mirzakhalilov S, Umirzakova S, Shavkatovich Buriboev A, Meliboev A, Muminov B, Jeon HS (2025) Accessible AI diagnostics and lightweight brain tumor detection on medical edge devices. Bioeng (Basel) 12(1):62. https:\/\/doi.org\/10.3390\/bioengineering12010062","journal-title":"Bioeng (Basel)"},{"issue":"11","key":"5833_CR45","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1038\/s42256-019-0114-4","volume":"1","author":"B Mittelstadt","year":"2019","unstructured":"Mittelstadt B (2019) Principles alone cannot guarantee ethical AI. Nat Mach Intell 1(11):501\u2013507. https:\/\/doi.org\/10.1038\/s42256-019-0114-4","journal-title":"Nat Mach Intell"},{"key":"5833_CR46","unstructured":"International Brain Tumour Alliance (2020) The brain tumour Patients\u2019 Charter of rights. https:\/\/theibta.org\/charter\/. 4 Aug 2026"},{"key":"5833_CR47","unstructured":"National Brain Tumor Society (2026) Policy & advocacy initiatives. https:\/\/braintumor.org. 4 Aug 2026"},{"issue":"5","key":"5833_CR48","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1001\/jama.2017.21903","volume":"319","author":"M Calvert","year":"2018","unstructured":"Calvert M, Kyte D, Mercieca-Bebber R, Slade A, Chan AW, King MT, Hunn A, Bottomley A, Regnault A, Chan A, Ells C, O\u2019connor D, Revicki D, Patrick D, Altman D, Basch E, Velikova G, Price G, Draper H, Blazeby J, Scott J, Coast J, Norquist J, Brown J, Haywood K, Johnson LL, Campbell L, Frank L, von Hildebrand M, Brundage M, Palmer M, Kluetz P, Stephens R, Golub RM, Mitchell S, Groves T (2018) Guidelines for inclusion of patient-reported outcomes in clinical trial protocols: the SPIRIT-PRO extension. JAMA 319(5):483\u2013494. https:\/\/doi.org\/10.1001\/jama.2017.21903","journal-title":"JAMA"}],"container-title":["Journal of Neuro-Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11060-026-05833-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11060-026-05833-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11060-026-05833-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T02:21:00Z","timestamp":1792549260000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11060-026-05833-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,21]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["5833"],"URL":"https:\/\/doi.org\/10.1007\/s11060-026-05833-8","relation":{},"ISSN":["0167-594X","1573-7373"],"issn-type":[{"value":"0167-594X","type":"print"},{"value":"1573-7373","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,21]]},"assertion":[{"value":"31 August 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"25"}}