{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T11:14:38Z","timestamp":1793272478765,"version":"4.5.0"},"reference-count":33,"publisher":"The Endocrine Society","issue":"9","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Context<\/jats:title>\n                    <jats:p>The resection plan of pituitary adenoma (PA) needs preoperative observation of the sellar region. Radiomics prediction requires high-quality segmentations. Manual delineation is time-consuming and subject to rater variability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This work aims to create an automated segmentation method for the sellar region, several tools to extract invasiveness-related features, and evaluate their clinical usefulness by predicting the tumor consistency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patients included were diagnosed with pituitary adenoma at Peking Union Medical College Hospital. A deep convolutional neural network, called gated-shaped U-net (GSU-Net), was created to automatically segment the sellar region into 8 classes. Five magnetic resonance imaging (MRI) features were extracted from the segmentation results, including tumor diameters, volume, optic chiasma height, Knosp grading system, and degree of internal carotid artery contact. The clinical usefulness of the proposed methods was evaluated by the diagnostic accuracy of the tumor consistency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 163 patients with confirmed pituitary adenoma were included as the first group and were randomly divided into a training data set and test data set (131 and 32 patients, respectively). Fifty patients with confirmed acromegaly were included as the second group. The Dice coefficient of pituitary adenoma in important image slices was 0.940. The proposed methods achieved accuracies of more than 80% for the prediction of 5 invasive-related MRI features. Methods derived from the automatic segmentation showed better performance than original methods and achieved areas under the curve of 0.840 and 0.920 for clinical models and radiomics models, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed methods could automatically segment the sellar region and extract features with high accuracy. The outstanding performance of the prediction of the tumor consistency indicates the methods\u2019 clinical usefulness for supporting neurosurgeons in judging patients\u2019 conditions, predicting prognosis, and other downstream tasks during the preoperative period.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1210\/clinem\/dgab371","type":"journal-article","created":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T05:30:37Z","timestamp":1622525437000},"page":"2535-2546","source":"Crossref","is-referenced-by-count":58,"title":["Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task"],"prefix":"10.1210","volume":"106","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6937-0665","authenticated-orcid":false,"given":"He","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanghua","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1666-2717","authenticated-orcid":false,"given":"Ming","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2080-5474","authenticated-orcid":false,"given":"Renzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"80","published-online":{"date-parts":[[2021,6,1]]},"reference":[{"key":"2021120108562922900_CIT0001","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.wneu.2018.02.162","article-title":"Endoscopic endonasal surgery treatment strategies for invasive pituitary adenoma: analysis of four approaches","volume":"115","author":"Ji-Hu","year":"2018","journal-title":"World Neurosurg."},{"issue":"1","key":"2021120108562922900_CIT0002","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s10654-015-0033-6","article-title":"Smoking, obesity and the risk of pituitary adenoma: a large prospective cohort study (the HUNT Study)","volume":"31","author":"Wiedmann","year":"2016","journal-title":"Eur J Epidemiol."},{"issue":"9","key":"2021120108562922900_CIT0003","doi-asserted-by":"crossref","first-page":"4268","DOI":"10.1210\/jc.2010-0537","article-title":"Incidence of pituitary adenomas in Northern Finland in 1992-2007","volume":"95","author":"Raappana","year":"2010","journal-title":"J Clin Endocrinol Metab."},{"issue":"9","key":"2021120108562922900_CIT0004","first-page":"1125","article-title":"Review ofactinic keratosis. 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