{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T13:58:01Z","timestamp":1788875881343,"version":"build-2803163510"},"reference-count":74,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"\n OBJECTIVE<\/jats:title>\n It is crucial to differentiate between prolactinoma and nonfunctioning pituitary adenoma (NFPA) prior to treatment because their preferred therapeutic approaches differ. The optimal treatment for NFPA causing compressive symptoms is transsphenoidal surgical debulking, whereas most prolactinomas respond favorably to dopamine agonist therapy. Both can manifest as hyperprolactinemia; however, existing guidelines solely rely on measurement of the serum prolactin level, which has been proven insufficient. The aim of this study was to distinguish prolactinoma and NFPA with moderate hyperprolactinemia using a clinical-radiomic model before treatment.<\/jats:p>\n <\/jats:sec>\n \n METHODS<\/jats:title>\n A total of 203 patients (174 female, median age 33.0 years) with moderate hyperprolactinemia were diagnosed with prolactinoma (n = 113) or NFPA (n = 90) and divided into training and validation sets retrospectively. An elastic net algorithm was used to screen radiomic features, leading to the establishment of a fusion radiomic model. Subsequently, a clinical-radiomic model was developed by integrating the radiomic model with the significant clinical features for individualized predictions. The calibration, discrimination, and clinical applicability of the models were evaluated. Additionally, the performance of the final selection model was validated using a dataset of 32 patients from other centers.<\/jats:p>\n <\/jats:sec>\n \n RESULTS<\/jats:title>\n The fusion radiomic model was developed using 3 significant radiomic features, achieving areas under the curve (AUCs) of 0.900 in the training set and 0.890 in the validation set. The clinical-radiomic model was created by integrating the fusion radiomic model with 3 additional clinical features (age, sex, and serum prolactin level). This model demonstrated excellent recognition and calibration capabilities, with AUCs of 0.94, 0.93, and 0.90 in the training, validation, and external multicenter validation sets, respectively. The decision curve analysis indicated that the constructed models had substantial clinical applicability for patients with prolactinoma and NFPA.<\/jats:p>\n <\/jats:sec>\n \n CONCLUSIONS<\/jats:title>\n Our clinical-radiomic model exhibited high sensitivity and exceptional performance in the differential diagnosis of prolactinoma and NFPA with hyperprolactinemia. This model offers significant potential for the noninvasive development of personalized diagnostic and treatment strategies for individuals with these conditions.<\/jats:p>\n <\/jats:sec>","DOI":"10.3171\/2025.6.jns25579","type":"journal-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T01:39:45Z","timestamp":1762565985000},"page":"600-610","source":"Crossref","is-referenced-by-count":2,"title":["Multiparameter MRI-based clinical-radiomic model to differentiate prolactinoma from hyperprolactinemic nonfunctioning pituitary adenoma"],"prefix":"10.3171","volume":"144","author":[{"given":"Yanghua","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing;"},{"name":"Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuming","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing;"},{"name":"Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuaiwei","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Mou","sequence":"additional","affiliation":[{"name":"Department of Radiology, Sichuan Provincial People\u2019s Hospital, Sichuan Academy of Medical Sciences, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing;"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"issue":"10","key":"b1","series-title":"N Engl J Med","first-page":"937","article-title":"Pituitary-tumor endocrinopathies","volume":"382","author":"Melmed S","year":"2020","unstructured":"Melmed S. 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J Neurosurg. 2020;133(2):321\u2013328."},{"issue":"6","key":"b4","series-title":"Endocr Pract","first-page":"572","article-title":"The prolactin per unit tumor volume ratio accurately distinguishes prolactinomas from secondary hyperprolactinemia due to stalk effect","volume":"28","author":"Faje A","year":"2022","unstructured":"Faje A, Jones P, Swearingen B, Tritos NA. The prolactin per unit tumor volume ratio accurately distinguishes prolactinomas from secondary hyperprolactinemia due to stalk effect. Endocr Pract. 2022;28(6):572\u2013577."},{"issue":"12","key":"b5","series-title":"Nat Rev Endocrinol","first-page":"722","article-title":"Diagnosis and management of prolactin-secreting pituitary adenomas: a Pituitary Society international Consensus Statement","volume":"19","author":"Petersenn S","year":"2023","unstructured":"Petersenn S, Fleseriu M, Casanueva FF, Diagnosis and management of prolactin-secreting pituitary adenomas: a Pituitary Society international Consensus Statement. 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J Magn Reson Imaging. 2019;50(2):519\u2013528."},{"key":"b31","series-title":"Front Endocrinol (Lausanne)","first-page":"403","article-title":"Preoperative noninvasive radiomics approach predicts tumor consistency in patients with acromegaly: development and multicenter prospective validation","volume":"10","author":"Fan Y","year":"2019","unstructured":"Fan Y, Hua M, Mou A, Preoperative noninvasive radiomics approach predicts tumor consistency in patients with acromegaly: development and multicenter prospective validation. Front Endocrinol (Lausanne). 2019;10:403."},{"issue":"6","key":"b32","series-title":"Neuro Oncol","first-page":"862","article-title":"MRI features predict survival and molecular markers in diffuse lower-grade gliomas","volume":"19","author":"Zhou H","year":"2017","unstructured":"Zhou H, Valli\u00e8res M, Bai HX, MRI features predict survival and molecular markers in diffuse lower-grade gliomas. 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J Neurosurg. 2023;139(2):305\u2013314."},{"issue":"6","key":"b34","series-title":"J Neurosurg","first-page":"1663","article-title":"Development and validation of a radiomics-visual evoked potential nomogram for preoperative prediction of visual outcome after endoscopic craniopharyngioma resection","volume":"142","author":"Qiao N","year":"2025","unstructured":"Qiao N, Li C, Zheng F, Development and validation of a radiomics-visual evoked potential nomogram for preoperative prediction of visual outcome after endoscopic craniopharyngioma resection. J Neurosurg. 2025;142(6):1663\u20131673."},{"issue":"2","key":"b35","series-title":"Acad Radiol","first-page":"612","article-title":"Noninvasive radiomics approach predicts dopamine agonists treatment response in patients with prolactinoma: a multicenter study","volume":"32","author":"Fan Y","year":"2025","unstructured":"Fan Y, Guo S, Tao C, Noninvasive radiomics approach predicts dopamine agonists treatment response in patients with prolactinoma: a multicenter study. Acad Radiol. 2025;32(2):612\u2013623."},{"key":"b36","series-title":"Nat Commun","first-page":"4006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts HJWL","year":"2014","unstructured":"Aerts HJWL, Velazquez ER, Leijenaar RTH, Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. 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J Endocrinol Invest. 2020;43(6):755\u2013765."},{"issue":"24","key":"b19","series-title":"Clin Cancer Res","first-page":"6288","article-title":"A coclinical radiogenomic validation study: conserved magnetic resonance radiomic appearance of periostin-expressing glioblastoma in patients and xenograft models","volume":"24","author":"Zinn PO","year":"2018","unstructured":"Zinn PO, Singh SK, Kotrotsou A, A coclinical radiogenomic validation study: conserved magnetic resonance radiomic appearance of periostin-expressing glioblastoma in patients and xenograft models. Clin Cancer Res. 2018;24(24):6288\u20136299."},{"issue":"4","key":"b20","series-title":"AJR Am J Roentgenol","first-page":"W784","article-title":"Receiver operating characteristic analysis","volume":"197","author":"Erturk SM","year":"2011","unstructured":"Erturk SM. Receiver operating characteristic analysis. AJR Am J Roentgenol. 2011;197(4):W784\u2013W785."},{"issue":"1","key":"b21","series-title":"Biometrics","first-page":"120","article-title":"Akaike\u2019s information criterion in generalized estimating equations","volume":"57","author":"Pan W","year":"2001","unstructured":"Pan W. Akaike\u2019s information criterion in generalized estimating equations. Biometrics. 2001;57(1):120\u2013125."},{"issue":"9","key":"b22","series-title":"Crit Care Med","first-page":"2052","article-title":"Assessing the calibration of mortality benchmarks in critical care: the Hosmer-Lemeshow test revisited","volume":"35","author":"Kramer AA","year":"2007","unstructured":"Kramer AA, Zimmerman JE. Assessing the calibration of mortality benchmarks in critical care: the Hosmer-Lemeshow test revisited. Crit Care Med. 2007;35(9):2052\u20132056."},{"key":"b23","series-title":"BMC Med Inform Decis Mak","first-page":"53","article-title":"Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers","volume":"8","author":"Vickers AJ","year":"2008","unstructured":"Vickers AJ, Cronin AM, Elkin EB, Gonen M. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 2008;8:53."},{"issue":"1","key":"b24","series-title":"Endocrine","first-page":"140","article-title":"Discrimination of prolactinoma from hyperprolactinemic non-functioning adenoma","volume":"37","author":"Hong JW","year":"2010","unstructured":"Hong JW, Lee MK, Kim SH, Lee EJ. Discrimination of prolactinoma from hyperprolactinemic non-functioning adenoma. Endocrine. 2010;37(1):140\u2013147."},{"issue":"7","key":"b25","series-title":"J Endocrinol Invest","first-page":"508","article-title":"Serum prolactin concentration at presentation of non-functioning pituitary macroadenomas","volume":"36","author":"Behan LA","year":"2013","unstructured":"Behan LA, O\u2019Sullivan EP, Glynn N, Serum prolactin concentration at presentation of non-functioning pituitary macroadenomas. J Endocrinol Invest. 2013;36(7):508\u2013514."},{"issue":"1","key":"b26","series-title":"Endocrine","first-page":"138","article-title":"Tumor volume improves preoperative differentiation of prolactinomas and nonfunctioning pituitary adenomas","volume":"74","author":"Wright K","year":"2021","unstructured":"Wright K, Lee M, Escobar N, Tumor volume improves preoperative differentiation of prolactinomas and nonfunctioning pituitary adenomas. Endocrine. 2021;74(1):138\u2013145."},{"key":"b27","series-title":"World Neurosurg","first-page":"e488","article-title":"Serum prolactin level to tumor size ratio as a potential parameter for preoperative differentiation of prolactinomas from hyperprolactinemia-causing non-functional pituitary adenomas","volume":"159","author":"Kim JH","year":"2022","unstructured":"Kim JH, Hur KY, Hong SD, Serum prolactin level to tumor size ratio as a potential parameter for preoperative differentiation of prolactinomas from hyperprolactinemia-causing non-functional pituitary adenomas. World Neurosurg. 2022;159:e488\u2013e496."},{"issue":"2","key":"b28","series-title":"AJNR Am J Neuroradiol","first-page":"208","article-title":"Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches","volume":"39","author":"Zhou M","year":"2018","unstructured":"Zhou M, Scott J, Chaudhury B, Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol. 2018;39(2):208\u2013216."},{"issue":"9","key":"b29","series-title":"Neuro Oncol","first-page":"1251","article-title":"Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation","volume":"20","author":"Kang D","year":"2018","unstructured":"Kang D, Park JE, Kim YH, Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro Oncol. 2018;20(9):1251\u20131261."},{"issue":"2","key":"b30","series-title":"J Magn Reson Imaging","first-page":"519","article-title":"Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis","volume":"50","author":"Artzi M","year":"2019","unstructured":"Artzi M, Bressler I, Ben Bashat D. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis. J Magn Reson Imaging. 2019;50(2):519\u2013528."},{"key":"b31","series-title":"Front Endocrinol (Lausanne)","first-page":"403","article-title":"Preoperative noninvasive radiomics approach predicts tumor consistency in patients with acromegaly: development and multicenter prospective validation","volume":"10","author":"Fan Y","year":"2019","unstructured":"Fan Y, Hua M, Mou A, Preoperative noninvasive radiomics approach predicts tumor consistency in patients with acromegaly: development and multicenter prospective validation. Front Endocrinol (Lausanne). 2019;10:403."},{"issue":"6","key":"b32","series-title":"Neuro Oncol","first-page":"862","article-title":"MRI features predict survival and molecular markers in diffuse lower-grade gliomas","volume":"19","author":"Zhou H","year":"2017","unstructured":"Zhou H, Valli\u00e8res M, Bai HX, MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol. 2017;19(6):862\u2013870."},{"issue":"2","key":"b33","series-title":"J Neurosurg","first-page":"305","article-title":"Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics","volume":"139","author":"Zhong S","year":"2023","unstructured":"Zhong S, Ren JX, Yu ZP, Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics. J Neurosurg. 2023;139(2):305\u2013314."},{"issue":"6","key":"b34","series-title":"J Neurosurg","first-page":"1663","article-title":"Development and validation of a radiomics-visual evoked potential nomogram for preoperative prediction of visual outcome after endoscopic craniopharyngioma resection","volume":"142","author":"Qiao N","year":"2025","unstructured":"Qiao N, Li C, Zheng F, Development and validation of a radiomics-visual evoked potential nomogram for preoperative prediction of visual outcome after endoscopic craniopharyngioma resection. J Neurosurg. 2025;142(6):1663\u20131673."},{"issue":"2","key":"b35","series-title":"Acad Radiol","first-page":"612","article-title":"Noninvasive radiomics approach predicts dopamine agonists treatment response in patients with prolactinoma: a multicenter study","volume":"32","author":"Fan Y","year":"2025","unstructured":"Fan Y, Guo S, Tao C, Noninvasive radiomics approach predicts dopamine agonists treatment response in patients with prolactinoma: a multicenter study. Acad Radiol. 2025;32(2):612\u2013623."},{"key":"b36","series-title":"Nat Commun","first-page":"4006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts HJWL","year":"2014","unstructured":"Aerts HJWL, Velazquez ER, Leijenaar RTH, Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006."},{"issue":"9","key":"b37","series-title":"Eur Radiol","first-page":"3692","article-title":"Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery","volume":"28","author":"Zhang S","year":"2018","unstructured":"Zhang S, Song G, Zang Y, Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery. 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