{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T13:19:52Z","timestamp":1788787192479,"version":"build-2803163510"},"reference-count":70,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"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>Background: Radiomics offers quantitative, high-dimensional data from conventional imaging and holds promise for improving diagnosis and treatment of pituitary adenomas (PAs). This systematic review aimed to synthesize current clinical applications of radiomics in PAs, focusing on diagnostic, predictive, and prognostic modeling. Methods: This review followed the PRISMA 2020 guidelines. A systematic search was performed in PubMed, Scopus, and Web of Science on 10 January 2024, and updated on 5 March 2024, using predefined keywords and MeSH terms. Studies were included if they evaluated radiomics-based models using MRI for diagnosis, classification, consistency, invasiveness, treatment response, or recurrence in human PA populations. Data extraction included study design, sample size, MRI sequences, feature types, machine learning algorithms, and model performance metrics. Study quality was assessed via the Newcastle-Ottawa Scale. Descriptive statistics summarized study characteristics; no meta-analysis was performed due to heterogeneity. Results: Out of 341 identified articles, 49 studies met inclusion criteria, encompassing a total of more than 9350 patients. The majority were retrospective (43 studies, 88%). MRI sequences used included T2-weighted imaging (35 studies, 71%), contrast-enhanced T1WI (34 studies, 69%), and T1WI (21 studies, 43%). PyRadiomics was the most common feature extraction tool (20 studies, 41%). Machine learning was employed in 43 studies (88%), predominantly support vector machines (16 studies, 33%), random forests (9 studies, 18%), and logistic regression (9 studies, 18%). Deep learning methods were applied in 17 studies (35%). Regarding diagnostic performance, 22 studies (45%) reported an (AUC) \u22650.85 in test datasets. External validation was performed in only 6 studies (12%). Radiomics applications included histological subtype prediction (14 studies, 29%), surgical outcome prediction (13 studies, 27%), invasiveness assessment (7 studies, 15%), tumor consistency evaluation (8 studies, 16%), and response to medical or radiotherapy treatments (3 studies, 6%). One study (2%) addressed automated segmentation and volumetry. Conclusions: Radiomics enables high-performance, noninvasive prediction of PA subtypes, consistency, invasiveness, treatment response, and recurrence, with 22 studies (45%) reporting AUC \u22650.85. Despite promising results, clinical translation remains limited by methodological heterogeneity, low external validation (6 studies, 12%), and lack of standardization.<\/jats:p>","DOI":"10.3390\/jcm14186595","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:11:01Z","timestamp":1758269461000},"page":"6595","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Radiomics in Pituitary Adenomas: A Systematic Review of Clinical Applications and Predictive Models"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6463-5000","authenticated-orcid":false,"given":"Edoardo","family":"Agosti","sequence":"first","affiliation":[{"name":"Neurosurgery Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcello","family":"Mangili","sequence":"additional","affiliation":[{"name":"Neurosurgery Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pier Paolo","family":"Panciani","sequence":"additional","affiliation":[{"name":"Neurosurgery Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7811-4612","authenticated-orcid":false,"given":"Lorenzo","family":"Ugga","sequence":"additional","affiliation":[{"name":"Department of Advanced Medical and Surgical Sciences, University of Campania \u201cLuigi Vanvitelli\u201d, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0901-6266","authenticated-orcid":false,"given":"Vittorio","family":"Rampinelli","sequence":"additional","affiliation":[{"name":"Unit of Otorhinolaryngology\u2014Head and Neck Surgery, Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Ravanelli","sequence":"additional","affiliation":[{"name":"Radiology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Fiorindi","sequence":"additional","affiliation":[{"name":"Neurosurgery Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4023-1909","authenticated-orcid":false,"given":"Marco Maria","family":"Fontanella","sequence":"additional","affiliation":[{"name":"Neurosurgery Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,18]]},"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","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":"ref_3","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s12022-022-09703-7","article-title":"Overview of the 2022 WHO Classification of Pituitary Tumors","volume":"33","author":"Asa","year":"2022","journal-title":"Endocr. Pathol."},{"key":"ref_4","first-page":"762","article-title":"Management of Hormone-Secreting Pituitary Adenomas","volume":"19","author":"Mehta","year":"2017","journal-title":"Neuro Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3368","DOI":"10.1210\/jc.2017-00773","article-title":"Risk of Recurrence in Pituitary Neuroendocrine Tumors: A Prospective Study Using a Five-Tiered Classification","volume":"102","author":"Raverot","year":"2017","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1001\/jama.2016.19699","article-title":"Diagnosis and Treatment of Pituitary Adenomas: A Review","volume":"317","author":"Molitch","year":"2017","journal-title":"JAMA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1007\/s11547-021-01389-x","article-title":"A Deep Look into Radiomics","volume":"126","author":"Scapicchio","year":"2021","journal-title":"Radiol. Med."},{"key":"ref_8","doi-asserted-by":"crossref","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":"ref_9","doi-asserted-by":"crossref","unstructured":"Maniaci, A., Lavalle, S., Gagliano, C., Lentini, M., Masiello, E., Parisi, F., Iannella, G., Cilia, N.D., Salerno, V., and Cusumano, G. (2024). The Integration of Radiomics and Artificial Intelligence in Modern Medicine. Life, 14.","DOI":"10.3390\/life14101248"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Maiuri, F., and Del Basso De Caro, M. (2024). Radiomics in Meningiomas: Pathological and Biomolecular Correlation. Meningiomas: From Pathology to Clinics, Springer Nature.","DOI":"10.1007\/978-3-031-76680-0"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zheng, B., Zhao, Z., Zheng, P., Liu, Q., Li, S., Jiang, X., Huang, X., Ye, Y., and Wang, H. (2024). The Current State of MRI-Based Radiomics in Pituitary Adenoma: Promising but Challenging. Front. Endocrinol., 15.","DOI":"10.3389\/fendo.2024.1426781"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5334\/jbsr.3470","article-title":"A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke\u2019s Cleft Cysts","volume":"108","author":"Taslicay","year":"2024","journal-title":"J. Belg. Soc. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1007\/s11604-020-01021-4","article-title":"Discrimination between Pituitary Adenoma and Craniopharyngioma Using MRI-Based Image Features and Texture Features","volume":"38","author":"Zhang","year":"2020","journal-title":"Jpn. J. Radiol."},{"key":"ref_15","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_16","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. Investig."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107301","DOI":"10.1016\/j.clineuro.2022.107301","article-title":"Deep Learning Model-Based Approach for Preoperative Prediction of Ki67 Labeling Index Status in a Noninvasive Way Using Magnetic Resonance Images: A Single-Center Study","volume":"219","author":"Shu","year":"2022","journal-title":"Clin. Neurol. Neurosurg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1007\/s10278-024-01121-x","article-title":"Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models","volume":"38","author":"Liu","year":"2024","journal-title":"J. Imaging Inform. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s00701-024-05977-4","article-title":"Is Radiomics a Useful Addition to Magnetic Resonance Imaging in the Preoperative Classification of PitNETs?","volume":"166","author":"Sathya","year":"2024","journal-title":"Acta Neurochir."},{"key":"ref_20","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_21","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_22","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_23","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_24","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1016\/j.csbj.2021.05.023","article-title":"Image-Driven Classification of Functioning and Nonfunctioning Pituitary Adenoma by Deep Convolutional Neural Networks","volume":"19","author":"Li","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_25","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_26","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_27","doi-asserted-by":"crossref","unstructured":"Liu, C.-X., Heng, L.-J., Han, Y., Wang, S.-Z., Yan, L.-F., Yu, Y., Ren, J.-L., Wang, W., Hu, Y.-C., and Cui, G.-B. (2021). Usefulness of the Texture Signatures Based on Multiparametric MRI in Predicting Growth Hormone Pituitary Adenoma Subtypes. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.640375"},{"key":"ref_28","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_29","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_30","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_31","doi-asserted-by":"crossref","unstructured":"Zhu, H., Fang, Q., Huang, Y., and Xu, K. (2020). Semi-Supervised Method for Image Texture Classification of Pituitary Tumors via CycleGAN and Optimized Feature Extraction. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01230-x"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1210\/clinem\/dgab371","article-title":"Development and Evaluation of Deep Learning-Based Automated Segmentation of Pituitary Adenoma in Clinical Task","volume":"106","author":"Wang","year":"2021","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_34","first-page":"3626","article-title":"The value of conventional magnetic resonance imaging based radiomic model in predicting the texture of pituitary macroadenoma","volume":"100","author":"Chen","year":"2020","journal-title":"Zhonghua Yi Xue Za Zhi"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Fang, Y., Wang, H., Feng, M., Chen, H., Zhang, W., Wei, L., Pei, Z., Wang, R., and Wang, S. (2022). Application of Convolutional Neural Network in the Diagnosis of Cavernous Sinus Invasion in Pituitary Adenoma. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.835047"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"280","DOI":"10.3174\/ajnr.A7387","article-title":"Thin-Slice Pituitary MRI with Deep Learning-Based Reconstruction for Preoperative Prediction of Cavernous Sinus Invasion by Pituitary Adenoma: A Prospective Study","volume":"43","author":"Kim","year":"2022","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"110647","DOI":"10.1016\/j.ejrad.2022.110647","article-title":"Deep Learning-Based Image Reconstruction Improves Radiologic Evaluation of Pituitary Axis and Cavernous Sinus Invasion in Pituitary Adenoma","volume":"158","author":"Park","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"ref_39","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":"Chin. Neurosurg. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s00234-024-03287-1","article-title":"Multi-Center Application of a Convolutional Neural Network for Preoperative Detection of Cavernous Sinus Invasion in Pituitary Adenomas","volume":"66","author":"Fang","year":"2024","journal-title":"Neuroradiology"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Feng, T., Fang, Y., Pei, Z., Li, Z., Chen, H., Hou, P., Wei, L., Wang, R., and Wang, S. (2022). A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.900519"},{"key":"ref_42","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_43","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_44","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_45","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ko, C.-C., Chen, J.-H., Chang, K.-T., Chen, T.-Y., Lim, S.-W., Tsui, Y.-K., and Su, M.-Y. (2020). Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.590083"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, Y.-J., Hsieh, H.-P., Hung, K.-C., Shih, Y.-J., Lim, S.-W., Kuo, Y.-T., Chen, J.-H., and Ko, C.-C. (2022). Deep Learning for Prediction of Progression and Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.813806"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shen, C., Liu, X., Jin, J., Han, C., Wu, L., Wu, Z., Su, Z., and Chen, X. (2023). A Novel Magnetic Resonance Imaging-Based Radiomics and Clinical Predictive Model for the Regrowth of Postoperative Residual Tumor in Non-Functioning Pituitary Neuroendocrine Tumor. Medicina, 59.","DOI":"10.3390\/medicina59091525"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3783","DOI":"10.1007\/s00330-021-07895-w","article-title":"Machine Learning Solutions in Radiology: Does the Emperor Have No Clothes?","volume":"31","author":"Cuocolo","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., and Brennan, S.E. (2021). The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ, 372.","DOI":"10.1136\/bmj.n71"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: The Bridge between Medical Imaging and Personalized Medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_51","unstructured":"(2025, August 25). The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-Based Phenotyping | Radiology. Available online: https:\/\/pubs.rsna.org\/doi\/10.1148\/radiol.2020191145."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Staartjes, V.E., Serra, C., Muscas, G., Maldaner, N., Akeret, K., van Niftrik, C.H.B., Fierstra, J., Holzmann, D., and 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.","DOI":"10.3171\/2018.8.FOCUS18243"},{"key":"ref_53","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_54","doi-asserted-by":"crossref","unstructured":"Fan, Y., Hua, M., Mou, A., Wu, M., Liu, X., Bao, X., Wang, R., and Feng, M. (2019). Preoperative Noninvasive Radiomics Approach Predicts Tumor Consistency in Patients With Acromegaly: Development and Multicenter Prospective Validation. Front. Endocrinol., 10.","DOI":"10.3389\/fendo.2019.00403"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1007\/s11102-020-01032-4","article-title":"A Novel Diagnostic Method for Pituitary Adenoma Based on Magnetic Resonance Imaging Using a Convolutional Neural Network","volume":"23","author":"Qian","year":"2020","journal-title":"Pituitary"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, W., Sun, M., Fan, Y., Wang, H., Feng, M., Zhou, S., and Wang, R. (2021). Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing\u2019s Disease. Front. Endocrinol., 12.","DOI":"10.3389\/fendo.2021.635795"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, C., Huang, W., Cheng, Y., Teng, Y., Zhang, L., and Xu, J. (2021). Machine Learning-Based Radiomics of the Optic Chiasm Predict Visual Outcome Following Pituitary Adenoma Surgery. J. Pers. Med., 11.","DOI":"10.3390\/jpm11100991"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Villalonga, J.F., Solari, D., Cuocolo, R., De Lucia, V., Ugga, L., Gragnaniello, C., Pailler, J.I., Cervio, A., Campero, A., and Cavallo, L.M. (2022). Clinical Application of the \u201cSellar Barrier\u2019s Concept\u201d for Predicting Intraoperative CSF Leak in Endoscopic Endonasal Surgery for Pituitary Adenomas with a Machine Learning Analysis. Front. Surg., 9.","DOI":"10.3389\/fsurg.2022.934721"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Gargya, S., and Jain, S. (2023). CAD System Design for Pituitary Tumor Classification Based on Transfer Learning Technique. Curr. Med. Imaging, 20.","DOI":"10.2174\/0115734056246146231018110415"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"7482","DOI":"10.1007\/s00330-023-09963-9","article-title":"Predicting Visual Recovery in Pituitary Adenoma Patients Post-Endoscopic Endonasal Transsphenoidal Surgery: Harnessing Delta-Radiomics of the Optic Chiasm from MRI","volume":"33","author":"Zhang","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1097\/JS9.0000000000000357","article-title":"Preoperative Volume of the Optic Chiasm Is an Easily Obtained Predictor for Visual Recovery of Pituitary Adenoma Patients Following Endoscopic Endonasal Transsphenoidal Surgery: A Cohort Study","volume":"109","author":"Zhang","year":"2023","journal-title":"Int. J. Surg."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"e324","DOI":"10.1016\/j.wneu.2024.06.043","article-title":"A Predictive Model for Intraoperative Cerebrospinal Fluid Leak During Endonasal Pituitary Adenoma Resection Using a Convolutional Neural Network","volume":"189","author":"Behzadi","year":"2024","journal-title":"World Neurosurg."},{"key":"ref_63","first-page":"171","article-title":"Automated Volumetric Assessment of Pituitary Adenoma","volume":"83","author":"Zanier","year":"2024","journal-title":"Endocrine"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s00234-024-03315-0","article-title":"Usefulness of Pituitary High-Resolution 3D MRI with Deep-Learning-Based Reconstruction for Perioperative Evaluation of Pituitary Adenomas","volume":"66","author":"Ishimoto","year":"2024","journal-title":"Neuroradiology"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.asjsur.2024.07.132","article-title":"Radiomics Using Multiparametric Magnetic Resonance Imaging to Predict Postoperative Visual Outcomes of Patients with Pituitary Adenoma","volume":"48","author":"Zhang","year":"2024","journal-title":"Asian J. Surg."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Agosti, E., Cuocolo, R., Mangili, M., Rampinelli, V., Veiceschi, P., Cappelletti, M., Panciani, P.P., Piazza, A., Bove, I., and Solari, D. (2025). Radiomics for Preoperative Assessment of Pituitary Adenoma Consistency with T2-Weighted MRI: A Multicenter Study. J. Neurol. Surg. Part B Skull Base.","DOI":"10.1055\/a-2607-0735"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1007\/s00330-024-11057-z","article-title":"Quality of Radiomics Research: Comprehensive Analysis of 1574 Unique Publications from 89 Reviews","volume":"35","author":"Kocak","year":"2025","journal-title":"Eur. Radiol."},{"key":"ref_68","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_69","doi-asserted-by":"crossref","unstructured":"Fan, Y., Jiang, S., Hua, M., Feng, S., Feng, M., and Wang, R. (2019). Machine Learning-Based Radiomics Predicts Radiotherapeutic Response in Patients With Acromegaly. Front. Endocrinol., 10.","DOI":"10.3389\/fendo.2019.00588"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMAScR): Checklist and Explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Intern. Med."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/18\/6595\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:48:43Z","timestamp":1760035723000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/18\/6595"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,18]]},"references-count":70,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["jcm14186595"],"URL":"https:\/\/doi.org\/10.3390\/jcm14186595","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,18]]}}}