{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T19:22:43Z","timestamp":1790709763915,"version":"4.1.0"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,7]],"date-time":"2024-01-07T00:00:00Z","timestamp":1704585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Radiomic analysis has emerged as a valuable tool for extracting quantitative features from medical imaging data, providing in-depth insights into various contexts and diseases. By employing methods derived from advanced computational techniques, radiomics quantifies textural information through the evaluation of the spatial distribution of signal intensities and inter-voxel relationships. In recent years, these techniques have gained considerable attention also in the field of pituitary tumors, with promising results. Indeed, the extraction of radiomic features from pituitary magnetic resonance imaging (MRI) images has been shown to provide useful information on various relevant aspects of these diseases. Some of the key topics that have been explored in the existing literature include the association of radiomic parameters with histopathological and clinical data and their correlation with tumor invasiveness and aggressive behavior. Their prognostic value has also been evaluated, assessing their role in the prediction of post-surgical recurrence, response to medical treatments, and long-term outcomes. This review provides a comprehensive overview of the current knowledge and application of radiomics in pituitary tumors. It also examines the current limitations and future directions of radiomic analysis, highlighting the major challenges that need to be addressed before a consistent integration of these techniques into routine clinical practice.<\/jats:p>","DOI":"10.3390\/jcm13020336","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T05:21:38Z","timestamp":1704691298000},"page":"336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7550-7023","authenticated-orcid":false,"given":"Fabio","family":"Bioletto","sequence":"first","affiliation":[{"name":"Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nunzia","family":"Prencipe","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4745-2624","authenticated-orcid":false,"given":"Alessandro Maria","family":"Berton","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luigi Simone","family":"Aversa","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5625-2640","authenticated-orcid":false,"given":"Daniela","family":"Cuboni","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuele","family":"Varaldo","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentina","family":"Gasco","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2809-8105","authenticated-orcid":false,"given":"Ezio","family":"Ghigo","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7742-4499","authenticated-orcid":false,"given":"Silvia","family":"Grottoli","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1056\/NEJMra1810772","article-title":"Pituitary-Tumor Endocrinopathies","volume":"382","author":"Melmed","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bioletto, F., Berton, A.M., Prencipe, N., Varaldo, E., Bona, C., and Grottoli, S. (2022). Markers of Aggressiveness in Pituitary Tumors: Update and Perspectives. J. Clin. Med., 11.","DOI":"10.3390\/jcm11216508"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"G1","DOI":"10.1530\/EJE-17-0796","article-title":"European Society of Endocrinology Clinical Practice Guidelines for the management of aggressive pituitary tumours and carcinomas","volume":"178","author":"Raverot","year":"2018","journal-title":"Eur. J. Endocrinol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Berton, A.M., Prencipe, N., Bertero, L., Baldi, M., Bima, C., Corsico, M., Bianchi, A., Mantovani, G., Ferra\u00f9, F., and Sartorato, P. (2023). Resistance to Somatostatin Analogs in Italian Acromegaly Patients: The MISS Study. J. Clin. Med., 12.","DOI":"10.3390\/jcm12010025"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1055\/s-0035-1549005","article-title":"Prediction of Consistency of Pituitary Adenomas by Magnetic Resonance Imaging","volume":"76","author":"Smith","year":"2015","journal-title":"J. Neurol. Surg. Part B Skull Base"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1007\/s00234-019-02211-2","article-title":"Preoperative evaluation of tumour consistency in pituitary macroadenomas: A machine learning-based histogram analysis on conventional T2-weighted MRI","volume":"61","author":"Zeynalova","year":"2019","journal-title":"Neuroradiology"},{"key":"ref_7","first-page":"12898","article-title":"Relationship between pituitary adenoma texture and collagen content revealed by comparative study of MRI and pathology analysis","volume":"8","author":"Wei","year":"2015","journal-title":"Int. J. Clin. Exp. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1148\/rg.2021210037","article-title":"Radiomics in oncology: A practical guide","volume":"41","author":"Shur","year":"2021","journal-title":"Radiographics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging\u2014\u201chow-to\u201d guide and critical reflection","volume":"11","author":"Cester","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s42000-021-00302-5","article-title":"Clinical relevance of tumor consistency in pituitary adenoma","volume":"20","author":"Pian","year":"2021","journal-title":"Hormones"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1002\/jmri.27930","article-title":"Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings","volume":"55","author":"Wan","year":"2022","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1007\/s00234-020-02502-z","article-title":"Prediction of pituitary adenoma surgical consistency: Radiomic data mining and machine learning on T2-weighted MRI","volume":"62","author":"Cuocolo","year":"2020","journal-title":"Neuroradiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s11102-016-0760-z","article-title":"Predictive role of dynamic contrast enhanced T1-weighted MR sequences in pre-surgical evaluation of macroadenomas consistency","volume":"20","author":"Romano","year":"2017","journal-title":"Pituitary"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108","DOI":"10.3171\/2009.3.JNS0929","article-title":"Extended transsphenoidal approach for pituitary adenomas invading the anterior cranial base, cavernous sinus, and clivus: A single-center experience with 126 consecutive cases","volume":"112","author":"Zhao","year":"2010","journal-title":"J. Neurosurg."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/s11102-011-0308-1","article-title":"Excess mortality for patients with residual disease following resection of pituitary adenomas","volume":"14","author":"Sughrue","year":"2011","journal-title":"Pituitary"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"403","DOI":"10.3389\/fendo.2019.00403","article-title":"Preoperative noninvasive radiomics approach predicts tumor consistency in patients with acromegaly: Development and multicenter prospective validation","volume":"10","author":"Fan","year":"2019","journal-title":"Front. Endocrinol."},{"key":"ref_17","first-page":"610","article-title":"Pituitary adenomas with invasion of the cavernous sinus space: A magnetic resonance imaging classification compared with surgical findings","volume":"33","author":"Knosp","year":"1993","journal-title":"Neurosurgery"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"803","DOI":"10.3171\/2014.12.JNS141083","article-title":"Invasion of the cavernous sinus space in pituitary adenomas: Endoscopic verification and its correlation with an MRI-based classification","volume":"122","author":"Micko","year":"2015","journal-title":"J. Neurosurg."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1007\/s00330-018-5725-3","article-title":"Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images","volume":"29","author":"Niu","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s41016-022-00290-4","article-title":"Prediction of high infiltration levels in pituitary adenoma using MRI-based radiomics and machine learning","volume":"8","author":"Zhang","year":"2022","journal-title":"Chinese Neurosurg. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109125","DOI":"10.1016\/j.ejrad.2020.109125","article-title":"Preoperative vascular heterogeneity and aggressiveness assessment of pituitary macroadenoma based on dynamic contrast-enhanced MRI texture analysis","volume":"129","author":"Liu","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_22","first-page":"36","article-title":"Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas","volume":"37","author":"Baysal","year":"2022","journal-title":"Medeni. Med. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108892","DOI":"10.1016\/j.ejrad.2020.108892","article-title":"A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging","volume":"125","author":"Peng","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1570","DOI":"10.1007\/s00330-021-08361-3","article-title":"Radiomics analysis allows for precise prediction of silent corticotroph adenoma among non-functioning pituitary adenomas","volume":"32","author":"Rui","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1007\/s40618-023-02042-2","article-title":"Radiomics model and clinical scale for the preoperative diagnosis of silent corticotroph adenomas","volume":"46","author":"Wang","year":"2023","journal-title":"J. Endocrinol. Invest."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3692","DOI":"10.1007\/s00330-017-5180-6","article-title":"Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery","volume":"28","author":"Zhang","year":"2018","journal-title":"Eur. Radiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1007\/s11102-020-01077-5","article-title":"Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas","volume":"23","author":"Park","year":"2020","journal-title":"Pituitary"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"640375","DOI":"10.3389\/fonc.2021.640375","article-title":"Bin Usefulness of the Texture Signatures Based on Multiparametric MRI in Predicting Growth Hormone Pituitary Adenoma Subtypes","volume":"11","author":"Liu","year":"2021","journal-title":"Front. Oncol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1007\/s00234-019-02266-1","article-title":"Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning","volume":"61","author":"Ugga","year":"2019","journal-title":"Neuroradiology"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, H., Liu, Z., Li, F., Shi, F., Xia, Y., Zhou, Q., and Zeng, Q. (2023). Preoperatively Predicting Ki67 Expression in Pituitary Adenomas Using Deep Segmentation Network and Radiomics Analysis Based on Multiparameter MRI. Acad. Radiol., 1\u201311.","DOI":"10.1016\/j.acra.2023.05.023"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3312","DOI":"10.1007\/s00330-023-09412-7","article-title":"Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas","volume":"33","author":"Wang","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1007\/s40618-019-01159-7","article-title":"Non-invasive and real-time proliferative activity estimation based on a quantitative radiomics approach for patients with acromegaly: A multicenter study","volume":"43","author":"Fan","year":"2020","journal-title":"J. Endocrinol. Invest."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s11102-011-0347-7","article-title":"Clinical factors involved in the recurrence of pituitary adenomas after surgical remission: A structured review and meta-analysis","volume":"15","author":"Roelfsema","year":"2012","journal-title":"Pituitary"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1530\/EJE-18-0291","article-title":"MRI texture analysis as a predictor of tumor recurrence or progression in patients with clinically non-functioning pituitary adenomas","volume":"179","author":"Galm","year":"2018","journal-title":"Eur. J. Endocrinol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Machado, L.F., Elias, P.C.L., Moreira, A.C., dos Santos, A.C., and Murta Junior, L.O. (2020). MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas. Comput. Biol. Med., 124.","DOI":"10.1016\/j.compbiomed.2020.103966"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"590083","DOI":"10.3389\/fonc.2020.590083","article-title":"Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas","volume":"10","author":"Zhang","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"780628","DOI":"10.3389\/fneur.2021.780628","article-title":"A Preoperative MRI-Based Radiomics-Clinicopathological Classifier to Predict the Recurrence of Pituitary Macroadenoma within 5 Years","volume":"12","author":"Zhang","year":"2022","journal-title":"Front. Neurol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108647","DOI":"10.1016\/j.ejrad.2019.108647","article-title":"Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma","volume":"121","author":"Fan","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2964","DOI":"10.1210\/clinem\/dgaa387","article-title":"Multivariable Prediction Model for Biochemical Response to First-Generation Somatostatin Receptor Ligands in Acromegaly","volume":"105","author":"Coopmans","year":"2020","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"379","DOI":"10.4158\/EP-2018-0500","article-title":"Predictive markers for postsurgical medical management of acromegaly: A systematic review and consensus treatment guidelines","volume":"25","author":"Ezzat","year":"2019","journal-title":"Endocr. Pract."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.tem.2012.11.007","article-title":"Novel pathway for somatostatin analogs in patients with acromegaly","volume":"24","author":"Gadelha","year":"2013","journal-title":"Trends Endocrinol. Metab."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1530\/EJE-12-0236","article-title":"Prolactinomas resistant to standard doses of cabergoline: A multicenter study of 92 patients","volume":"167","author":"Vroonen","year":"2012","journal-title":"Eur. J. Endocrinol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1530\/EJE-09-0012","article-title":"Characterization of resistance to the prolactin-lowering effects of cabergoline in macroprolactinomas: A study in 122 patients","volume":"160","author":"Delgrange","year":"2009","journal-title":"Eur. J. Endocrinol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1007\/s00330-018-5876-2","article-title":"Predicting response to somatostatin analogues in acromegaly: Machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI","volume":"29","author":"Kocak","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1007\/s11102-019-01023-0","article-title":"MRI texture analysis in acromegaly and its role in predicting response to somatostatin receptor ligands","volume":"23","author":"Galm","year":"2020","journal-title":"Pituitary"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"E3069","DOI":"10.1210\/clinem\/dgab159","article-title":"Radiomics with Ensemble Machine Learning Predicts Dopamine Agonist Response in Patients with Prolactinoma","volume":"106","author":"Park","year":"2021","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"588","DOI":"10.3389\/fendo.2019.00588","article-title":"Machine Learning-Based Radiomics Predicts Radiotherapeutic Response in Patients with Acromegaly","volume":"10","author":"Fan","year":"2019","journal-title":"Front. Endocrinol."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/2\/336\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:41:31Z","timestamp":1760103691000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/2\/336"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,7]]},"references-count":47,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["jcm13020336"],"URL":"https:\/\/doi.org\/10.3390\/jcm13020336","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,7]]}}}