{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T19:22:38Z","timestamp":1790709758074,"version":"4.1.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Slovak Technical University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:sec>\n                <jats:title>Abstract<\/jats:title>\n                <jats:p>Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of \u201cRadiomics\u201d involves the extraction of high-dimensional features, often referred to as \u201cRadiomic features,\u201d from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1007\/s11517-024-03163-3","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T07:02:04Z","timestamp":1721113324000},"page":"3581-3597","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Radiomics of pituitary adenoma using computer vision: a review"],"prefix":"10.1007","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7648-7298","authenticated-orcid":false,"given":"Tomas","family":"Zilka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6929-9694","authenticated-orcid":false,"given":"Wanda","family":"Benesova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"key":"3163_CR1","doi-asserted-by":"publisher","unstructured":"Baysal B, Eser MB, Dogan MB, Kursun MA (2022) Multivariable diagnostic prediction model to detect hormone secretion profile from T2W MRI radiomics with artificial neural networks in pituitary adenomas. Medeniyet Med J 37:36\u201343. ISSN 21494606. https:\/\/doi.org\/10.4274\/MMJ.galenos.2022.58538","DOI":"10.4274\/MMJ.galenos.2022.58538"},{"key":"3163_CR2","doi-asserted-by":"publisher","unstructured":"Cuocolo R, Ugga L, Solari D, Corvino S, A. D\u2019Amico, D. Russo, P. Cappabianca, L. M. Cavallo, and A. Elefante, (2020) Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI. Neuroradiology 62:1649\u20131656. ISSN 14321920. https:\/\/doi.org\/10.1007\/s00234-020-02502-z","DOI":"10.1007\/s00234-020-02502-z"},{"key":"3163_CR3","unstructured":"Egger J, Bauer MHA, Kuhnt D, Freisleben B, Nimsky C (2010) Pituitary adenoma segmentation. In: Proceedings of biosignal 2010, JULY 14-16, 2010. BERLIN, GERMANY"},{"key":"3163_CR4","doi-asserted-by":"publisher","unstructured":"Egger J, Kapur T, Nimsky C, Kikinis R (2012) Pituitary adenoma volumetry with 3D slicer. PLoS ONE 7:12. ISSN 19326203. https:\/\/doi.org\/10.1371\/journal.pone.0051788","DOI":"10.1371\/journal.pone.0051788"},{"key":"3163_CR5","doi-asserted-by":"publisher","first-page":"403","DOI":"10.3389\/fendo.2019.00403","volume":"10","author":"Y Fan","year":"2019","unstructured":"Fan Y, Hua M, Mou A, Wu M, Liu X, Bao X, Wang R, Feng M (2019a) Preoperative noninvasive radiomics approach predicts tumor consistency in patients with acromegaly: development and multicenter prospective validation. Front Endocrinol 10:403","journal-title":"Front Endocrinol"},{"key":"3163_CR6","doi-asserted-by":"publisher","first-page":"108647","DOI":"10.1016\/j.ejrad.2019.108647","volume":"121","author":"Y Fan","year":"2019","unstructured":"Fan Y, Liu Z, Hou B, Li L, Liu X, Liu Z, Wang R, Lin Y, Feng F, Tian J, Feng M (2019b) Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma. Eur J Radiol 121:108647","journal-title":"Eur J Radiol"},{"key":"3163_CR7","doi-asserted-by":"publisher","first-page":"900519","DOI":"10.3389\/fnins.2022.900519","volume":"16","author":"T Feng","year":"2022","unstructured":"Feng T, Fang Y, Pei Z, Li Z, Chen H, Hou P, Wei L, Wang R, Wang S (2022) A convolutional neural network model for detecting sellar floor destruction of pituitary adenoma on magnetic resonance imaging scans. Front Neurosci 16:900519","journal-title":"Front Neurosci"},{"issue":"2","key":"3163_CR8","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563\u2013577","journal-title":"Radiology"},{"key":"3163_CR9","doi-asserted-by":"crossref","unstructured":"Jia J, Meng L, Song G, Sun S, Li C, Tian J, Zhang Y (2020) Prediction of response to stereotactic radiotherapy for nonfunctioning pituitary adenoma using radiomic features. PREPRINT (Version 1) available at Research Square","DOI":"10.21203\/rs.2.21209\/v1"},{"key":"3163_CR10","unstructured":"Kim B, Wattenberg M, Gilmer J, Cai C, Wexler J, Viegas F (2018) Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In: International conference on machine learning, pp 2668\u20132677. PMLR"},{"key":"3163_CR11","doi-asserted-by":"publisher","first-page":"2731","DOI":"10.1007\/s00330-018-5876-2","volume":"29","author":"B Kocak","year":"2019","unstructured":"Kocak B, Durmaz ES, Kadioglu P, Polat Korkmaz O, Comunoglu N, Tanriover N, Kocer N, Islak C, Kizilkilic O (2019) Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on t2-weighted mri. Eur Radiol 29:2731\u20132739","journal-title":"Eur Radiol"},{"key":"3163_CR12","doi-asserted-by":"crossref","unstructured":"Lei T, Nandi AK (2022) Image segmentation: principles, techniques, and applications. Wiley","DOI":"10.1002\/9781119859048"},{"key":"3163_CR13","doi-asserted-by":"publisher","first-page":"3077","DOI":"10.1016\/j.csbj.2021.05.023","volume":"19","author":"H Li","year":"2021","unstructured":"Li H, Zhao Q, Zhang Y, Sai K, Xu L, Mou Y, Xie Y, Ren J, Jiang X (2021a) Image-driven classification of functioning and nonfunctioning pituitary adenoma by deep convolutional neural networks. Comput Struct Biotechnol J 19:3077\u20133086","journal-title":"Comput Struct Biotechnol J"},{"issue":"2","key":"3163_CR14","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1016\/j.acra.2023.05.023","volume":"31","author":"H Li","year":"2024","unstructured":"Li H, Liu Z, Li F, Shi F, Xia Y, Zhou Q, Zeng Q (2024) Preoperatively predicting KI67 expression in pituitary adenomas using deep segmentation network and radiomics analysis based on multiparameter MRI. Acad Radiol 31(2):617\u2013627","journal-title":"Acad Radiol"},{"key":"3163_CR15","doi-asserted-by":"publisher","first-page":"758690","DOI":"10.3389\/fmed.2021.758690","volume":"8","author":"Q Li","year":"2021","unstructured":"Li Q, Zhu Y, Chen M, Guo R, Hu Q, Lu Y, Deng Z, Deng S, Zhang T, Wen H, Gao R, Nie Y, Li H, Chen J, Shi G, Shen J, Cheung WW, Liu Z, Guo Y, Chen Y (2021b) Development and validation of a deep learning algorithm to automatic detection of pituitary microadenoma from MRI. Front Med 8:758690","journal-title":"Front Med"},{"key":"3163_CR16","doi-asserted-by":"publisher","first-page":"103966","DOI":"10.1016\/j.compbiomed.2020.103966","volume":"124","author":"LF Machado","year":"2020","unstructured":"Machado LF, Elias PC, Moreira AC, Dos Santos AC, Junior LOM (2020) MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas. Comput Biol Med 124:103966","journal-title":"Comput Biol Med"},{"issue":"4","key":"3163_CR17","doi-asserted-by":"publisher","first-page":"488","DOI":"10.2967\/jnumed.118.222893","volume":"61","author":"ME Mayerhoefer","year":"2020","unstructured":"Mayerhoefer ME, Materka A, Langs G, H\u00e4ggstr\u00f6m I, Szczypi\u0144ski P, Gibbs P, Cook G (2020) Introduction to radiomics. J Nucl Med 61(4):488\u2013495","journal-title":"J Nucl Med"},{"issue":"8","key":"3163_CR18","doi-asserted-by":"publisher","first-page":"2470","DOI":"10.1177\/02841851231174462","volume":"64","author":"BAR Mendi","year":"2023","unstructured":"Mendi BAR, Batur H, \u00c7ay N, \u00c7ak\u0131r BT (2023) Radiomic analysis of preoperative magnetic resonance imaging for the prediction of pituitary adenoma consistency. Acta Radiol 64(8):2470\u20132478","journal-title":"Acta Radiol"},{"issue":"10","key":"3163_CR19","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber M-A, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp C, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin H-C, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993\u20132024. https:\/\/doi.org\/10.1109\/TMI.2014.2377694","journal-title":"IEEE Trans Med Imaging"},{"key":"3163_CR20","unstructured":"Mour\u00e3o N, Calisto FM, Nascimento J (2020) MIMBCD-UI: AI visual explaination - Lesions Types, 01. http:\/\/dx.doi.org\/10.13140\/RG.2.2.33693.26086"},{"key":"3163_CR21","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1007\/s00330-018-5725-3","volume":"29","author":"J Niu","year":"2019","unstructured":"Niu J, Zhang S, Ma S, Diao J, Zhou W, Tian J, Zang Y, Jia W (2019) Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images. Eur Radiol 29:1625\u20131634","journal-title":"Eur Radiol"},{"key":"3163_CR22","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1007\/s11102-020-01077-5","volume":"23","author":"YW Park","year":"2020","unstructured":"Park YW, Kang Y, Ahn SS, Ku CR, Kim EH, Kim SH, Lee EJ, Kim SH, Lee S-K (2020) Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas. Pituitary 23:691\u2013700","journal-title":"Pituitary"},{"issue":"8","key":"3163_CR23","doi-asserted-by":"publisher","first-page":"e3069","DOI":"10.1210\/clinem\/dgab159","volume":"106","author":"YW Park","year":"2021","unstructured":"Park YW, Eom J, Kim S, Kim H, Ahn SS, Ku CR, Kim EH, Lee EJ, Kim SH, Lee S-K (2021) Radiomics with ensemble machine learning predicts dopamine agonist response in patients with prolactinoma. J Clin Endocrinol & Metab 106(8):e3069\u2013e3077","journal-title":"J Clin Endocrinol & Metab"},{"key":"3163_CR24","doi-asserted-by":"publisher","first-page":"108892","DOI":"10.1016\/j.ejrad.2020.108892","volume":"125","author":"A Peng","year":"2020","unstructured":"Peng A, Dai H, Duan H, Chen Y, Huang J, Zhou L, Chen L (2020) A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging. Eur J Radiol 125:108892","journal-title":"Eur J Radiol"},{"key":"3163_CR25","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1007\/s11102-020-01032-4","volume":"23","author":"Y Qian","year":"2020","unstructured":"Qian Y, Qiu Y, Li C-C, Wang Z-Y, Cao B-W, Huang H-X, Ni Y-H, Chen L-L, Sun J-Y (2020) A novel diagnostic method for pituitary adenoma based on magnetic resonance imaging using a convolutional neural network. Pituitary 23:246\u2013252","journal-title":"Pituitary"},{"key":"3163_CR26","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3163_CR27","doi-asserted-by":"crossref","unstructured":"Rui W, Qiao N, Wu Y, Zhang Y, Aili A, Zhang Z, Ye H, Wang Y, Zhao Y, Yao Z(2022) Radiomics analysis allows for precise prediction of silent corticotroph adenoma among non-functioning pituitary adenomas. European Radiology, pp 1\u20139,","DOI":"10.1007\/s00330-021-08361-3"},{"issue":"1","key":"3163_CR28","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s00701-024-05977-4","volume":"166","author":"A Sathya","year":"2024","unstructured":"Sathya A, Goyal-Honavar A, Chacko AG, Jasper A, Chacko G, Devakumar D, Seelam JA, Sasidharan BK, Pavamani SP, Thomas HMT (2024) Is radiomics a useful addition to magnetic resonance imaging in the preoperative classification of PitNETs? Acta Neurochir 166(1):91","journal-title":"Acta Neurochir"},{"issue":"12","key":"3163_CR29","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.3390\/mi12121473","volume":"12","author":"X Shu","year":"2021","unstructured":"Shu X, Zhou Y, Li F, Zhou T, Meng X, Wang F, Zhang Z, Pu J, Xu B (2021) Three-dimensional semantic segmentation of pituitary adenomas based on the deep learning framework-nnU-net: a clinical perspective. Micromachines 12(12):1473","journal-title":"Micromachines"},{"key":"3163_CR30","doi-asserted-by":"publisher","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","volume":"9","author":"N Siddique","year":"2021","unstructured":"Siddique N, Paheding S, Elkin CP, Devabhaktuni V (2021) U-net and its variants for medical image segmentation: a review of theory and applications. Ieee Access 9:82031\u201382057","journal-title":"Ieee Access"},{"key":"3163_CR31","unstructured":"\u0160imundi\u0107 A-M (2009) Measures of diagnostic accuracy: basic definitions. ejifcc 19(4):203"},{"issue":"5","key":"3163_CR32","doi-asserted-by":"publisher","first-page":"E12","DOI":"10.3171\/2018.8.FOCUS18243","volume":"45","author":"VE Staartjes","year":"2018","unstructured":"Staartjes VE, Serra C, Muscas G, Maldaner N, Akeret K, van Niftrik CH, Fierstra J, Holzmann D, Regli L (2018) Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study. Neurosurg Focus 45(5):E12","journal-title":"Neurosurg Focus"},{"key":"3163_CR33","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1007\/s00234-019-02266-1","volume":"61","author":"L Ugga","year":"2019","unstructured":"Ugga L, Cuocolo R, Solari D, Guadagno E, D\u2019Amico A, Somma T, Cappabianca P, del Basso ML, de Caro LM, Cavallo, Brunetti A (2019) Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning. Neuroradiology 61:1365\u20131373","journal-title":"Neuroradiology"},{"issue":"21","key":"3163_CR34","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJ Van Griethuysen","year":"2017","unstructured":"Van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RG, Fillion-Robin J-C, Pieper S, Aerts HJ (2017) Computational radiomics system to decode the radiographic phenotype. Cancer research 77(21):e104\u2013e107","journal-title":"Cancer research"},{"key":"3163_CR35","doi-asserted-by":"publisher","first-page":"102368","DOI":"10.1016\/j.media.2022.102368","volume":"77","author":"D Wallis","year":"2022","unstructured":"Wallis D, Buvat I (2022) Clever Hans effect found in a widely used brain tumour MRI dataset. Med Image Anal 77:102368","journal-title":"Med Image Anal"},{"issue":"5","key":"3163_CR36","doi-asserted-by":"publisher","first-page":"1491","DOI":"10.1002\/jmri.27930","volume":"55","author":"T Wan","year":"2022","unstructured":"Wan T, Wu C, Meng M, Liu T, Li C, Ma J, Qin Z (2022) Radiomic features on multiparametric MRI for preoperative evaluation of pituitary macroadenomas consistency: preliminary findings. J Magn Reson Imaging 55(5):1491\u20131503","journal-title":"J Magn Reson Imaging"},{"key":"3163_CR37","doi-asserted-by":"crossref","unstructured":"Wang H, Chang J, Zhang W, Fang Y, Li S, Fan Y, Jiang S, Yao Y, Deng K, Lu L et al (2023) Radiomics model and clinical scale for the preoperative diagnosis of silent corticotroph adenomas. J Endocrinol Investig 46(9):1843\u20131854","DOI":"10.1007\/s40618-023-02042-2"},{"key":"3163_CR38","doi-asserted-by":"crossref","unstructured":"Wang Y, Chen S, Shi F, Cheng X, Xu Q, Li J, Luo S, Jiang P, Wei Y, Zhou C et al (2021) MR-based radiomics for differential diagnosis between cystic pituitary adenoma and Rathke cleft cyst. Comput Math Methods Med 1:6438861","DOI":"10.1155\/2021\/6438861"},{"key":"3163_CR39","doi-asserted-by":"publisher","first-page":"3961","DOI":"10.1007\/s00259-021-05242-1","volume":"48","author":"G Wu","year":"2021","unstructured":"Wu G, Jochems A, Refaee T, Ibrahim A, Yan C, Sanduleanu S, Woodruff HC, Lambin P (2021) Structural and functional radiomics for lung cancer. Eur J Nucl Med Mol Imaging 48:3961\u20133974","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"3163_CR40","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1007\/s00234-019-02211-2","volume":"61","author":"A Zeynalova","year":"2019","unstructured":"Zeynalova A, Ko\u00e7k B, Durmaz ES, \u00c7omuno\u011flu N, Ozcan K, Ozcan G, Turk O, Tanriover N, Kocer N, K\u0131z\u0131lk\u0131l\u0131\u00e7 O, I\u015flak C (2019) Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI. Neuroradiology 61:767\u2013774","journal-title":"Neuroradiology"},{"issue":"04","key":"3163_CR41","first-page":"221","volume":"8","author":"C Zhang","year":"2022","unstructured":"Zhang C, Heng X, Neng W, Chen H, Sun A, Li J, Wang M (2022a) Prediction of high infiltration levels in pituitary adenoma using MRI-based radiomics and machine learning. Chin Neurosurg J 8(04):221\u2013229","journal-title":"Chin Neurosurg J"},{"key":"3163_CR42","doi-asserted-by":"publisher","first-page":"3692","DOI":"10.1007\/s00330-017-5180-6","volume":"28","author":"S Zhang","year":"2018","unstructured":"Zhang S, Song G, Zang Y, Jia J, Wang C, Li C, Tian J, Dong D, Zhang Y (2018a) Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery. Eur Radiol 28:3692\u20133701","journal-title":"Eur Radiol"},{"key":"3163_CR43","doi-asserted-by":"publisher","first-page":"3692","DOI":"10.1007\/s00330-017-5180-6","volume":"28","author":"S Zhang","year":"2018","unstructured":"Zhang S, Song G, Zang Y, Jia J, Wang C, Li C, Tian J, Dong D, Zhang Y (2018b) Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery. Eur Radiol 28:3692\u20133701","journal-title":"Eur Radiol"},{"key":"3163_CR44","doi-asserted-by":"publisher","first-page":"590083","DOI":"10.3389\/fonc.2020.590083","volume":"10","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Ko C-C, Chen J-H, Chang K-T, Chen T-Y, Lim S-W, Tsui Y-K, Su M-Y (2020) Radiomics approach for prediction of recurrence in non-functioning pituitary macroadenomas. Front Oncol 10:590083","journal-title":"Front Oncol"},{"key":"3163_CR45","doi-asserted-by":"publisher","first-page":"780628","DOI":"10.3389\/fneur.2021.780628","volume":"12","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Luo Y, Kong X, Wan T, Long Y, Ma J (2022b) A preoperative MRI-based radiomics-clinicopathological classifier to predict the recurrence of pituitary macroadenoma within 5 years. Front Neurol 12:780628","journal-title":"Front Neurol"},{"key":"3163_CR46","doi-asserted-by":"crossref","unstructured":"Zuki\u010d D, Egger J, Bauer MHA, Kuhnt D, Carl B, Freisleben B, Kolb A, Nimsky C (2011) Preoperative volume determination for pituitary adenoma. Medical Imaging 2011: Computer-Aided Diagnosis, vol 7963. SPIE","DOI":"10.1117\/12.877660"},{"key":"3163_CR47","doi-asserted-by":"crossref","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit M-C, Dinapoli N, Dinh CV, Echegaray S, El Naqa I, Fedorov AY, Gatta R, Gillies RJ, Goh V, G\u00f6tz M, Guckenberger M, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RT, Lenkowicz J, Lippert F, Losneg\u00e5rd A, Maier-Hein KH, Morin O, M\u00fcller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EA, Rahmim A, Rao AU, Scherer J, Siddique MM, Sijtsema NM, Socarras Fernandez J, Spezi E, Steenbakkers RJ, Tanadini-Lang S, Thorwarth D, Troost EG, Upadhaya T, Valentini V, van Dijk LV, van Griethuysen J, van Velden FH, Whybra P, Richter C, L\u00f6ck S (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2):328\u2013338","DOI":"10.1148\/radiol.2020191145"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03163-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03163-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03163-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T16:07:25Z","timestamp":1731773245000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03163-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,16]]},"references-count":47,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["3163"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03163-3","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,16]]},"assertion":[{"value":"3 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The ethical standard and informed consent are not applicable to this survey study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare no Conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}