{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T17:20:35Z","timestamp":1781112035993,"version":"3.54.1"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to develop a predictive model for cavernous sinus dural invasion in pituitary adenomas by retrospectively analyzing clinical and imaging data. It explores the associations between clinical and radiomics features and cavernous sinus dural invasion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Clinical data and coronal T2-weighted MRI images were collected from patients diagnosed with pituitary adenomas at our institution between December 2012 and December 2022. Tumor regions of interest (ROIs) were segmented using 3D Slicer, and radiomics features were extracted. Statistically significant radiomics features were identified using Lasso regression and univariate analysis. Clinical features were screened using univariate and multivariate logistic regression analyses. These selected features were incorporated into ten machine learning algorithms to construct three predictive models: a clinical feature model, a radiomics feature model, and a combined clinical and radiomics feature model. Model performance was evaluated to determine the best-performing model, which was further interpreted.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 252 patients with histopathologically confirmed pituitary adenomas were included. The analysis identified Knosp grade, tumor left-right diameter, pedunculated satellite tumor, and clival invasion as significant clinical predictors, along with radiomics features including original.4, original.10, log-sigma-5-0-mm-3D.29, log-sigma-5-0-mm-3D.91, wavelet-LLH.37, wavelet-LHL.37, and wavelet-HLL.8. The combined clinical and radiomics model outperformed models based solely on clinical or radiomics features. Among the ten machine learning algorithms, the LightGBM model demonstrated the best predictive performance, achieving an area under the curve (AUC) of 0.86 and an accuracy (ACC) of 0.76.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A machine learning model integrating clinical and radiomics features can effectively predict cavernous sinus dural invasion in pituitary adenomas preoperatively, providing a reliable basis for diagnosing tumor invasiveness and developing surgical plans. The LightGBM algorithm exhibited the highest predictive efficacy. Furthermore, the pedunculated satellite tumor feature emerged as a novel imaging marker for cavernous sinus dural invasion, offering new insights into the study of invasive pituitary adenomas.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2025.1706895","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T05:11:31Z","timestamp":1763615491000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Radiomics-based multiple machine learning approaches for investigating medial wall invasion of the cavernous sinus in pituitary adenomas"],"prefix":"10.3389","volume":"15","author":[{"given":"Yuyang","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiansheng","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengwei","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianshun","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangfeng","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shousen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.4158\/EP-2018-0034","article-title":"Update on the clinicopathology of pituitary adenomas","volume":"24","author":"Lim","year":"2018","journal-title":"Endocr Pract"},{"key":"B2","doi-asserted-by":"publisher","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":"B3","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.3390\/cancers11121936","article-title":"Pituitary adenomas and invasiveness from anatomo-surgical, radiological, and histological perspectives: a systematic literature review","volume":"11","author":"Serioli","year":"2019","journal-title":"Cancers"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.3760\/cma.j.issn.1001-2346.2017.02.001","article-title":"Anatomy of membranous structures associated with pituitary adenomas and their clinical significance","volume":"33","author":"Qi","year":"2017","journal-title":"Chin J Neurosurg"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1227\/neu.0000000000001912","article-title":"Dural invasion as a marker of aggressive pituitary adenomas","volume":"90","author":"Cooper","year":"2022","journal-title":"Neurosurgery"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1177\/003591574003300717","article-title":"Extrasellar extensions of pituitary adenomas: (section of neurology)","volume":"33","author":"Jefferson","year":"1940","journal-title":"Proc R Soc Med"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.3171\/jns.1965.22.3.0268","article-title":"Invasive pituitary adenomas","volume":"22","author":"Martins","year":"1965","journal-title":"J Neurosurg"},{"key":"B8","doi-asserted-by":"publisher","first-page":"4","DOI":"10.19347\/j.cnki.2096-1413.201915002","article-title":"Clinical anatomical study on the membran-like structures of trigeminal nerve pituitary gland and cavernous sinus","volume":"4","author":"Kang","year":"2019","journal-title":"Clin Res Pract"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1007\/s11154-020-09557-6","article-title":"Neuroimaging of aggressive pituitary tumors","volume":"21","author":"Bonneville","year":"2020","journal-title":"Rev Endocr Metab Disord"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.wneu.2024.03.066","article-title":"MRI 3D SPACE T2WI for pituitary adenoma cavernous sinus invasion diagnosis","volume":"185","author":"Chen","year":"2024","journal-title":"World Neurosurg"},{"key":"B11","volume-title":"The significance of interbed in invasive pituitary tumors","author":"Huang","year":"2018"},{"key":"B12","volume-title":"Research and application of invasive pituitary adenomas growth corridors","author":"Hu","year":"2019"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1007\/s11102-023-01302-x","article-title":"Predicting tumor consistency and extent of resection in non-functioning pituitary tumors","volume":"26","author":"Fiore","year":"2023","journal-title":"Pituitary"},{"key":"B14","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s11102-024-01495-9","article-title":"Reappraising prediction of surgical complexity of non-functioning pituitary adenomas after transsphenoidal surgery: the modified TRANSSPHER grade","volume":"28","author":"Fiore","year":"2025","journal-title":"Pituitary"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12885-018-5024-z","article-title":"Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients","volume":"18","author":"Zheng","year":"2018","journal-title":"BMC Cancer"},{"key":"B16","doi-asserted-by":"publisher","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":"B17","doi-asserted-by":"publisher","DOI":"10.1002\/med.21846","article-title":"A review in radiomics: making personalized medicine a reality via routine imaging","volume":"42","author":"Guiot","year":"2022","journal-title":"Medicinal Res Rev"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1038\/s41571-021-00560-7","article-title":"Predicting cancer outcomes with radiomics and artificial intelligence in radiology","volume":"19","author":"Bera","year":"2022","journal-title":"Nat Rev Clin Oncol"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.900121","article-title":"Novel nomograms to predict delayed hyponatremia after transsphenoidal surgery for pituitary adenoma","volume":"13","author":"Lin","year":"2022","journal-title":"Front Endocrinol"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1038\/s41598-024-83319-1","article-title":"Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma","volume":"15","author":"Lin","year":"2025","journal-title":"Sci Rep"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.bmb.a011611","article-title":"Assessing obesity: classification and epidemiology","volume":"53","author":"Seidell","year":"1997","journal-title":"Br Med Bull"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.689943","article-title":"Invasive corridor of clivus extension in pituitary adenoma: bony anatomic consideration, surgical outcome and technical nuances","volume":"11","author":"Wu","year":"2021","journal-title":"Front Oncol"},{"key":"B23","doi-asserted-by":"publisher","first-page":"0023","DOI":"10.34133\/hds.0023","article-title":"simpleNomo: A python package of making nomograms for visualizable calculation of logistic regression models","volume":"3","author":"Hong","year":"2023","journal-title":"Health Data Sci"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1007\/s10143-020-01404-1","article-title":"Pituitary adenoma with posterior area invasion of cavernous sinus: surgical anatomy, approach, and outcomes","volume":"44","author":"Wu","year":"2021","journal-title":"Neurosurgical Rev"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1227\/00006123-199310000-00008","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":"B26","doi-asserted-by":"publisher","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":"B27","doi-asserted-by":"publisher","DOI":"10.1007\/s11102-020-01122-3","article-title":"Diagnostic value of Knosp grade and modified Knosp grade for cavernous sinus invasion in pituitary adenomas: a systematic review and meta-analysis","volume":"24","author":"Fang","year":"2021","journal-title":"Pituitary"},{"key":"B28","doi-asserted-by":"publisher","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":"B29","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.723513","article-title":"Identification of the extradural and intradural extension of pituitary adenomas to the suprasellar region: classification, surgical strategies, and outcomes","volume":"11","author":"Yang","year":"2021","journal-title":"Front Oncol"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000034767","article-title":"Analysis of risk factors of pituitary neoplasms invading the sphenoidal sinus","volume":"102","author":"Luo","year":"2023","journal-title":"Medicine"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.3171\/2016.3.JNS153044","article-title":"Interrater and intrarater reliability of the Knosp scale for pituitary adenoma grading","volume":"126","author":"Mooney","year":"2017","journal-title":"J Neurosurg"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2282020695","article-title":"Comparison of growth hormone\u2013producing and non\u2013growth hormone\u2013producing pituitary adenomas: imaging characteristics and pathologic correlation","volume":"228","author":"Hagiwara","year":"2003","journal-title":"Radiology"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.3171\/jns.1986.64.3.0402","article-title":"The occurrence of dural invasion in pituitary adenomas","volume":"64","author":"Selman","year":"1986","journal-title":"J Neurosurg"},{"key":"B34","doi-asserted-by":"publisher","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"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1706895\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T05:11:33Z","timestamp":1763615493000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1706895\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,20]]},"references-count":34,"alternative-id":["10.3389\/fonc.2025.1706895"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2025.1706895","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,20]]},"article-number":"1706895"}}