{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T11:02:15Z","timestamp":1761649335006},"reference-count":29,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,23]]},"DOI":"10.7759\/cureus.58835","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T15:31:13Z","timestamp":1713886273000},"source":"Crossref","is-referenced-by-count":2,"title":["Prediction of Obliteration After the Gamma Knife Radiosurgery of Arteriovenous Malformations Using Hand-Crafted Radiomics and Deep-Learning Methods"],"prefix":"10.7759","author":[{"given":"David J","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Megan","family":"Kollitz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitchell","family":"Ward","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajiv S","family":"Dharnipragada","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ribhav","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luke T","family":"Sabal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayush","family":"Singla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramachandra","family":"Tummala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kathryn","family":"Dusenbery","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoichi","family":"Watanabe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"ref1","article-title":"Arteriovenous malformation of the brain","author":"Bokhari MR","year":"2023","unstructured":"Bokhari MR, Bokhari SRA. Arteriovenous malformation of the brain. StatPearls. StatPearls (ed): StatPearls Publishing, Treasure Island, FL; 2023."},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-53486-6.00068-5","article-title":"Endovascular treatment of arteriovenous malformations","volume":"136","author":"Diaz O","year":"2016","unstructured":"Diaz O, Scranton R. Endovascular treatment of arteriovenous malformations. Handb Clin Neurol. 2016, 136:1311-7. 10.1016\/B978-0-444-53486-6.00068-5","journal-title":"Handb Clin Neurol"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1227\/01.NEU.0000320439.27895.24","article-title":"Long-term excess mortality in 623 patients with brain arteriovenous malformations","volume":"63","author":"Laakso A","year":"2008","unstructured":"Laakso A, Dashti R, Sepp\u00e4nen J, et al.. Long-term excess mortality in 623 patients with brain arteriovenous malformations. Neurosurgery. 2008, 63:244-55. 10.1227\/01.NEU.0000320439.27895.24","journal-title":"Neurosurgery"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.wneu.2018.12.220","article-title":"Intervening nidal brain parenchyma and risk of radiation-induced changes after radiosurgery for brain arteriovenous malformation: A study using an unsupervised machine learning algorithm","volume":"125","author":"Lee CC","year":"2019","unstructured":"Lee CC, Yang HC, Lin CJ, et al.. Intervening nidal brain parenchyma and risk of radiation-induced changes after radiosurgery for brain arteriovenous malformation: A study using an unsupervised machine learning algorithm. World Neurosurg. 2019, 125:e132-8. 10.1016\/j.wneu.2018.12.220","journal-title":"World Neurosurg"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1097\/00006123-199605000-00010","article-title":"Gamma knife radiosurgery for arteriovenous malformations: Long-term follow-up results focusing on complications occurring more than 5 years after irradiation","volume":"38","author":"Yamamoto M","year":"1996","unstructured":"Yamamoto M, Jimbo M, Hara M, Saito I, Mori K. Gamma knife radiosurgery for arteriovenous malformations: Long-term follow-up results focusing on complications occurring more than 5 years after irradiation. Neurosurgery. 1996, 38:906-14.","journal-title":"Neurosurgery"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/S0360-3016(99)00102-9","article-title":"Five year results of LINAC radiosurgery for arteriovenous malformations: Outcome for large AVMS","volume":"44","author":"Miyawaki L","year":"1999","unstructured":"Miyawaki L, Dowd C, Wara W, et al.. Five year results of LINAC radiosurgery for arteriovenous malformations: Outcome for large AVMS. Int J Radiat Oncol Biol Phys. 1999, 44:1089-106. 10.1016\/S0360-3016(99)00102-9","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.nec.2013.05.002","article-title":"Stereotactic radiosurgery of intracranial arteriovenous malformations","volume":"24","author":"Friedman WA","year":"2013","unstructured":"Friedman WA. Stereotactic radiosurgery of intracranial arteriovenous malformations. Neurosurg Clin N Am. 2013, 24:561-74. 10.1016\/j.nec.2013.05.002","journal-title":"Neurosurg Clin N Am"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1093\/neuopn\/okab004","article-title":"Systematic review and meta-analysis of the dose-response and risk factors for obliteration of arteriovenous malformations following radiosurgery: An update based on the last 20 years of published clinical evidence","volume":"2","author":"Zhu S","year":"2032","unstructured":"Zhu S, Brodin NP, Garg MK, LaSala P, Tom\u00e9 WA. Systematic review and meta-analysis of the dose-response and risk factors for obliteration of arteriovenous malformations following radiosurgery: An update based on the last 20 years of published clinical evidence. Neurosurg Open. 2032, 2:okab004. 10.1093\/neuopn\/okab004","journal-title":"Neurosurg Open"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1159\/000493070","article-title":"Gamma knife radiosurgery of arteriovenous malformations: Long-term outcomes and late effects","volume":"34","author":"Pollock BE","year":"2019","unstructured":"Pollock BE. Gamma knife radiosurgery of arteriovenous malformations: Long-term outcomes and late effects. Prog Neurol Surg. 2019, 34:238-47. 10.1159\/000493070","journal-title":"Prog Neurol Surg"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8140(02)00103-2","article-title":"An analysis of the dose-response for arteriovenous malformation radiosurgery and other factors affecting obliteration","volume":"63","author":"Flickinger JC","year":"2002","unstructured":"Flickinger JC, Kondzioka D, Maitz AH, Lunsford LD. An analysis of the dose-response for arteriovenous malformation radiosurgery and other factors affecting obliteration. Radiother Oncol. 2002, 63:347-54. 10.1016\/S0167-8140(02)00103-2","journal-title":"Radiother Oncol"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000026203","article-title":"The predictors of clinical outcomes in brainstem arteriovenous malformations after stereotactic radiosurgery","volume":"100","author":"Ai X","year":"2021","unstructured":"Ai X, Xu J. The predictors of clinical outcomes in brainstem arteriovenous malformations after stereotactic radiosurgery. Medicine. 2021, 100:e26203. 10.1097\/MD.0000000000026203","journal-title":"Medicine"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/srep21161","article-title":"Using a machine learning approach to predict outcomes after radiosurgery for cerebral arteriovenous malformations","volume":"6","author":"Oermann EK","year":"2016","unstructured":"Oermann EK, Rubinsteyn A, Ding D, et al.. Using a machine learning approach to predict outcomes after radiosurgery for cerebral arteriovenous malformations. Sci Rep. 2016, 6:21161. 10.1038\/srep21161","journal-title":"Sci Rep"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1093\/neuros\/nyaa004","article-title":"Stereotactic radiosurgery for Spetzler-Martin grade I and II arteriovenous malformations: International Society of Stereotactic Radiosurgery (ISRS) Practice Guideline","volume":"87","author":"Graffeo CS","year":"2020","unstructured":"Graffeo CS, Sahgal A, De Salles A, et al.. Stereotactic radiosurgery for Spetzler-Martin grade I and II arteriovenous malformations: International Society of Stereotactic Radiosurgery (ISRS) Practice Guideline. Neurosurgery. 2020, 87:442-52. 10.1093\/neuros\/nyaa004","journal-title":"Neurosurgery"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5747-x","article-title":"Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types","volume":"29","author":"Zhang Y","year":"2019","unstructured":"Zhang Y, Zhang B, Liang F, et al.. Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types. Eur Radiol. 2019, 29:2157-65. 10.1007\/s00330-018-5747-x","journal-title":"Eur Radiol"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2021.767165","article-title":"Radiomics analysis for predicting epilepsy in patients with unruptured brain arteriovenous malformations","volume":"12","author":"Zhao S","year":"2021","unstructured":"Zhao S, Zhao Q, Jiao Y, et al.. Radiomics analysis for predicting epilepsy in patients with unruptured brain arteriovenous malformations. Front Neurol. 2021, 12:767165. 10.3389\/fneur.2021.767165","journal-title":"Front Neurol"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2022.07.008","article-title":"Assessment of gamma knife radiosurgery for unruptured cerebral arterioveneus malformations based on multi-parameter radiomics of MRI","volume":"92","author":"Gao D","year":"2022","unstructured":"Gao D, Meng X, Jin H, Liu A, Sun S. Assessment of gamma knife radiosurgery for unruptured cerebral arterioveneus malformations based on multi-parameter radiomics of MRI. Magn Reson Imaging. 2022, 92:251-9. 10.1016\/j.mri.2022.07.008","journal-title":"Magn Reson Imaging"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-25089-2","article-title":"ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides","volume":"12","author":"Praveen SP","year":"2022","unstructured":"Praveen SP, Srinivasu PN, Shafi J, Wozniak M, Ijaz MF. ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides. Sci Rep. 2022, 12:20804. 10.1038\/s41598-022-25089-2","journal-title":"Sci Rep"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1177\/003693305700200504","article-title":"Cerebral vascular accidents in patients over the age of 60. II. Prognosis","volume":"2","author":"Rankin J","year":"1957","unstructured":"Rankin J. Cerebral vascular accidents in patients over the age of 60. II. Prognosis. Scott Med J. 1957, 2:200-15. 10.1177\/003693305700200504","journal-title":"Scott Med J"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep Residual Learning for Image Recognition","author":"He K","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV; 2016. 770-8:10.1109\/CVPR.2016.90"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107660","article-title":"ResNet and its application to medical image processing: Research progress and challenges","volume":"240","author":"Xu W","year":"2023","unstructured":"Xu W, Fu YL, Zhu D. ResNet and its application to medical image processing: Research progress and challenges. Comput Methods Programs Biomed. 2023, 240:107660. 10.1016\/j.cmpb.2023.107660","journal-title":"Comput Methods Programs Biomed"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848","article-title":"ImageNet: A large-scale hierarchical image database","author":"Deng J","year":"2009","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. ImageNet: A large-scale hierarchical image database. Miami, FL; 2009. 10.1109\/CVPR.2009.5206848"},{"key":"ref22","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"2019","author":"Paszke A","unstructured":"Paszke A, Gross S, Massa F, et al.. PyTorch: An imperative style, high-performance deep learning library.  Adv Neural Inf Process Syst 32. 2019:2019-32.","journal-title":"Adv Neural Inf Process Syst 32"},{"key":"ref23","article-title":"Fundamentals of data mining in genomics and proteomics","author":"Dubitzky W","year":"2007","unstructured":"Dubitzky W, Granzow M, Berrar D. Fundamentals of data mining in genomics and proteomics. Springer Science + Business Media, New York; 2007."},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"van Griethuysen JJ","year":"2017","unstructured":"van Griethuysen JJ, Fedorov A, Parmar C, et al.. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017, 77:e104-7. 10.1158\/0008-5472.CAN-17-0339","journal-title":"Cancer Res"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2012.05.001","article-title":"3D Slicer as an image computing platform for the Quantitative Imaging Network","volume":"30","author":"Fedorov A","year":"2012","unstructured":"Fedorov A, Beichel R, Kalpathy-Cramer J, et al.. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012, 30:1323-41. 10.1016\/j.mri.2012.05.001","journal-title":"Magn Reson Imaging"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocn.2014.12.017","article-title":"Clinical outcome and complications of gamma knife radiosurgery for intracranial arteriovenous malformations","volume":"22","author":"Bir SC","year":"2015","unstructured":"Bir SC, Ambekar S, Maiti TK, Nanda A. Clinical outcome and complications of gamma knife radiosurgery for intracranial arteriovenous malformations. J Clin Neurosci. 2015, 22:1117-22. 10.1016\/j.jocn.2014.12.017","journal-title":"J Clin Neurosci"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1111\/1754-9485.13261","article-title":"A review of medical image data augmentation techniques for deep learning applications","volume":"65","author":"Chlap P","year":"2021","unstructured":"Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol. 2021, 65:545-63. 10.1111\/1754-9485.13261","journal-title":"J Med Imaging Radiat Oncol"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16919-9","article-title":"Multiple Instance Neuroimage Transformer,","author":"Singla A","year":"2022","unstructured":"Singla A, Zhao C, Do DK, et al.. Multiple Instance Neuroimage Transformer,. Predictive Intelligence in Medicine: 5th International Workshop, PRIME 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22. Rekik, I, Adeli E, Park SH, Cintas C (ed): Springer, Singapore; 2022. 36-48. 10.1007\/978-3-031-16919-9"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-12170-z","article-title":"A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy","volume":"12","author":"Tanaka S","year":"2022","unstructured":"Tanaka S, Kadoya N, Sugai Y, et al.. A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy. Sci Rep. 2022, 12:8899. 10.1038\/s41598-022-12170-z","journal-title":"Sci Rep"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/241403-prediction-of-obliteration-after-the-gamma-knife-radiosurgery-of-arteriovenous-malformations-using-hand-crafted-radiomics-and-deep-learning-methods","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T15:31:17Z","timestamp":1713886277000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/241403-prediction-of-obliteration-after-the-gamma-knife-radiosurgery-of-arteriovenous-malformations-using-hand-crafted-radiomics-and-deep-learning-methods"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,23]]},"references-count":29,"URL":"https:\/\/doi.org\/10.7759\/cureus.58835","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2024,4,23]]}}}