[1] Mete O, Lopes MB. Overview of the 2017 WHO Classification of Pituitary Tumors[J]. Endocr Pathol, 2017, 28:228-243.
[2] Melmed S. Pituitary-tumor endocrinopathies[J]. N Engl J Med, 2020, 382:937-950.
[3] Park HS, Lloyd S, Decker RH, Wilson LD, Yu JB. Overview of the surveillance, epidemiology, and end results database:evolution, data variables, and quality assurance[J]. Curr Probl Cancer, 2012, 36:183-190.
[4] Lara OD, Wang Y, Asare A, Xu T, Chiu HS, Liu Y, Hu W, Sumazin P, Uppal S, Zhang L, Rauh-Hain JA, Sood AK. Pancancer clinical and molecular analysis of racial disparities[J]. Cancer, 2020, 126:800-807.
[5] Pellizzoni L, E Silva SA, Falavigna A. Multilanguage health record database focused on the active follow-up of patients and adaptable for patient-reported outcomes and clinical research design[J]. Int J Med Inform, 2020, 135:104065.
[6] Drange MR, Fram NR, Herman-Bonert V, Melmed S. Pituitary tumor registry:a novel clinical resource[J]. J Clin Endocrinol Metab, 2000, 85:168-174.
[7] Katznelson L, Kleinberg D, Vance ML, Stavrou S, Pulaski KJ, Schoenfeld DA, Hayden DL, Wright ME, Woodburn CJ, Klibanski A. Hypogonadism in patients with acromegaly:data from the multi-centre acromegaly registry pilot study[J]. Clin Endocrinol (Oxf), 2001, 54:183-188.
[8] Ferrante E, Ferraroni M, Castrignanò T, Menicatti L, Anagni M, Reimondo G, Del Monte P, Bernasconi D, Loli P, Faustini-Fustini M, Borretta G, Terzolo M, Losa M, Morabito A, Spada A, Beck-Peccoz P, Lania AG. Non-functioning pituitary adenoma database:a useful resource to improve the clinical management of pituitary tumors[J]. Eur J Endocrinol, 2006, 155:823-829.
[9] Tresoldi AS, Carosi G, Betella N, Del Sindaco G, Indirli R, Ferrante E, Sala E, Giavoli C, Morenghi E, Locatelli M, Milani D, Mazziotti G, Spada A, Arosio M, Mantovani G, Lania AGA. Clinically nonfunctioning pituitary incidentalomas:characteristics and natural history[J]. Neuroendocrinology, 2020, 110:595-603.
[10] Mazziotti G, Battista C, Maffezzoni F, Chiloiro S, Ferrante E, Prencipe N, Grasso L, Gatto F, Olivetti R, Arosio M, Barale M, Bianchi A, Cellini M, Chiodini I, De Marinis L, Del Sindaco G, Di Somma C, Ferlin A, Ghigo E, Giampietro A, Grottoli S, Lavezzi E, Mantovani G, Morenghi E, Pivonello R, Porcelli T, Procopio M, Pugliese F, Scillitani A, Lania AG. Treatment of acromegalic osteopathy in real-life clinical practice:the BAAC (Bone Active Drugs in Acromegaly) study[J]. J Clin Endocrinol Metab, 2020, 105:dgaa363.
[11] Webb SM, Santos A, Valassi E. The value of a European registry for pituitary adenomas:the example of Cushing's syndrome registry[J]. Ann Endocrinol (Paris), 2012, 73:83-89.
[12] Valassi E, Franz H, Brue T, Feelders RA, Netea-Maier R, Tsagarakis S, Webb SM, Yaneva M, Reincke M, Droste M, Komerdus I, Maiter D, Kastelan D, Chanson P, Pfeifer M, Strasburger CJ, Tóth M, Chabre O, Tabarin A, Krsek M, Fajardo C, Bolanowski M, Santos A, Wass JAH, Trainer PJ; ERCUSYN Study Group. Diagnostic tests for Cushing's syndrome differ from published guidelines:data from ERCUSYN[J]. Eur J Endocrinol, 2017, 176:613-624.
[13] Valassi E, Franz H, Brue T, Feelders RA, Netea-Maier R, Tsagarakis S, Webb SM, Yaneva M, Reincke M, Droste M, Komerdus I, Maiter D, Kastelan D, Chanson P, Pfeifer M, Strasburger CJ, Tóth M, Chabre O, Krsek M, Fajardo C, Bolanowski M, Santos A, Trainer PJ, Wass JAH, Tabarin A; ERCUSYN Study Group. Preoperative medical treatment in Cushing's syndrome:frequency of use and its impact on postoperative assessment:data from ERCUSYN[J]. Eur J Endocrinol, 2018, 178:399-409.
[14] Valassi E, Feelders R, Maiter D, Chanson P, Yaneva M, Reincke M, Krsek M, Tóth M, Webb SM, Santos A, Paiva I, Komerdus I, Droste M, Tabarin A, Strasburger CJ, Franz H, Trainer PJ, Newell-Price J, Wass JA, Papakokkinou E, Ragnarsson O; ERCUSYN Study Group. Worse health-related quality of life at long-term follow-up in patients with Cushing's disease than patients with cortisol producing adenoma:data from the ERCUSYN[J]. Clin Endocrinol (Oxf), 2018, 88:787-798.
[15] Valassi E, Tabarin A, Brue T, Feelders RA, Reincke M, Netea-Maier R, Tóth M, Zacharieva S, Webb SM, Tsagarakis S, Chanson P, Pfeiffer M, Droste M, Komerdus I, Kastelan D, Maiter D, Chabre O, Franz H, Santos A, Strasburger CJ, Trainer PJ, Newell-Price J, Ragnarsson O. High mortality within 90 days of diagnosis in patients with Cushing's syndrome:results from the ERCUSYN registry[J]. Eur J Endocrinol, 2019, 181:461-472.
[16] Petrossians P, Tichomirowa MA, Stevenaert A, Martin D, Daly AF, Beckers A. The Liege Acromegaly Survey (LAS):a new software tool for the study of acromegaly[J]. Ann Endocrinol (Paris), 2012, 73:190-201.
[17] Petrossians P, Daly AF, Natchev E, Maione L, Blijdorp K, Sahnoun-Fathallah M, Auriemma R, Diallo AM, Hulting AL, Ferone D, Hana V Jr, Filipponi S, Sievers C, Nogueira C, Fajardo-Montañana C, Carvalho D, Hana V, Stalla GK, Jaffrain-Réa ML, Delemer B, Colao A, Brue T, Neggers SJCMM, Zacharieva S, Chanson P, Beckers A. Acromegaly at diagnosis in 3173 patients from the Liege Acromegaly Survey (LAS) database[J]. Endocr Relat Cancer, 2017, 24:505-518.
[18] Hayashi Y, Kita D, Watanabe T, Fukui I, Sasagawa Y, Oishi M, Tachibana O, Ueda F, Nakada M. Prediction of postoperative diabetes insipidus using morphological hyperintensity patterns in the pituitary stalk on magnetic resonance imaging after transsphenoidal surgery for sellar tumors[J]. Pituitary, 2016, 19:552-559.
[19] Himes BT, Bhargav AG, Brown DA, Kaufmann TJ, Bancos I, Van Gompel JJ. Does pituitary compression/empty sella syndrome contribute to MRI-negative Cushing's disease:a single-institution experience[J]? Neurosurg Focus, 2020, 48:E3.
[20] Chiloiro S, Giampietro A, Bianchi A, Tartaglione T, Capobianco A, Anile C, De Marinis L. Diagnosis of endocrine disease:primary empty sella:a comprehensive review[J]. Eur J Endocrinol, 2017, 177:R275-285.
[21] Feng M, Yang CX, Liu XH, Bao XJ, Deng K, Yao Y, Xing B, Lu L, Zhu HJ. Inferior petrosal sinus sampling predicting the sides of pituitary adenoma of Cushing's disease and analysis of influencing factors[J]. Zhonghua Shen Jing Wai Ke Za Zhi, 2016, 32:776-780.[冯铭, 杨程显, 刘小海, 包新杰, 邓侃, 姚勇, 幸兵, 卢琳, 朱惠娟. 岩下窦静脉取血判断库欣病肿瘤侧别及影响因素分析[J]. 中华神经外科杂志, 2016, 32:776-780.]
[22] Liu Y, Liu X, Hong X, Liu P, Bao X, Yao Y, Xing B, Li Y, Huang Y, Zhu H, Lu L, Wang R, Feng M. Prediction of recurrence after transsphenoidal surgery for Cushing's disease:the use of machine learning algorithms[J]. Neuroendocrinology, 2019, 108:201-210.
[23] Fan Y, Li Y, Li Y, Feng S, Bao X, Feng M, Wang R. Development and assessment of machine learning algorithms for predicting remission after transsphenoidal surgery among patients with acromegaly[J]. Endocrine, 2020, 67:412-422.
[24] Fan Y, Liu Z, Hou B, Li L, Liu X, Liu Z, Wang R, Lin Y, Feng F, Tian J, Feng M. Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma[J]. Eur J Radiol, 2019, 121:108647.
[25] Fan Y, Jiang S, Hua M, Feng S, Feng M, Wang R. Machine learning-based radiomics predicts radiotherapeutic response in patients with acromegaly[J]. Front Endocrinol (Lausanne), 2019, 10:588.
[26] Wei R, Jiang C, Gao J, Xu P, Zhang D, Sun Z, Liu X, Deng K, Bao X, Sun G, Yao Y, Lu L, Zhu H, Wang R, Feng M. Deep-learning approach to automatic identification of facial anomalies in endocrine disorders[J]. Neuroendocrinology, 2020, 110:328-337.
[27] Qiao N, Shen M, He W, He M, Zhang Z, Ye H, Li Y, Shou X, Li S, Jiang C, Wang Y, Zhao Y. Machine learning in predicting early remission in patients after surgical treatment of acromegaly:a multicenter study[J]. Pituitary, 2021, 24:53-61.
[28] Nadezhdina EY, Rebrova OY, Grigoriev AY, Ivaschenko OV, Azizyan VN, Melnichenko GA, Dedov Ⅱ. Prediction of recurrence and remission within 3 years in patients with Cushing disease after successful transnasal adenomectomy[J]. Pituitary, 2019, 22:574-580.
[29] Pivonello R, De Leo M, Cozzolino A, Colao A. The treatment of Cushing's disease[J]. Endocr Rev, 2015, 36:385-486.
[30] Little AS, Gardner PA, Fernandez-Miranda JC, Chicoine MR, Barkhoudarian G, Prevedello DM, Yuen KCJ, Kelly DF; TRANSSPHER Study Group. Pituitary gland recovery following fully endoscopic transsphenoidal surgery for nonfunctioning pituitary adenoma:results of a prospective multicenter study[J]. J Neurosurg, 2019:1-7.
[31] El-Asmar N, El-Sibai K, Al-Aridi R, Selman WR, Arafah BM. Postoperative sellar hematoma after pituitary surgery:clinical and biochemical characteristics[J]. Eur J Endocrinol, 2016, 174:573-582.
[32] Zhu X, Wang Y, Zhao X, Jiang C, Zhang Q, Jiang W, Wang Y, Chen H, Shou X, Zhao Y, Li Y, Li S, Ye H. Incidence of pituitary apoplexy and its risk factors in Chinese people:a database study of patients with pituitary adenoma[J]. PLoS One, 2015, 10:e0139088.
[33] Araujo-Castro M, Pascual-Corrales E, Acitores Cancela A, García Duque S, Ley Urzaiz L, Rodríguez Berrocal V. Status and clinical and radiological predictive factors of presurgical anterior pituitary function in pituitary adenomas:study of 232 patients[J]. Endocrine, 2020, 70:584-592.
[34] Staartjes VE, Stricker S, Muscas G, Maldaner N, Holzmann D, Burkhardt JK, Seifert B, Schmid C, Serra C, Regli L. Intraoperative unfolding and postoperative pruning of the pituitary gland after transsphenoidal surgery for pituitary adenoma:a volumetric and endocrinological evaluation[J]. Endocrine, 2019, 63:231-239.
[35] Nishioka H, Inoshita N. New WHO classification of pituitary adenomas (4th edition):assessment of pituitary transcription factors and the prognostic histological factors[J]. Brain Tumor Pathol, 2018, 35:57-61.
[36] Drummond J, Roncaroli F, Grossman AB, Korbonits M. Clinical and pathological aspects of silent pituitary adenomas[J]. J Clin Endocrinol Metab, 2019, 104:2473-2489.
[37] Mete O, Cintosun A, Pressman I, Asa SL. Epidemiology and biomarker profile of pituitary adenohypophysial tumors[J]. Mod Pathol, 2018, 31:900-909.
[38] Hasanov R, Aydo?an B?, Kiremitçi S, Erden E, Güllü S. The prognostic roles of the Ki-67 proliferation index, P53 expression, mitotic index, and radiological tumor invasion in pituitary adenomas[J]. Endocr Pathol, 2019, 30:49-55.
[39] Portovedo S, Gaido N, de Almeida Nunes B, Nascimento AG, Rocha A, Magalhães M, Nascimento GC, Pires de Carvalho D, Soares P, Takiya C, Faria MDS, Miranda-Alves L. Differential expression of HMGA1 and HMGA2 in pituitary neuroendocrine tumors[J]. Mol Cell Endocrinol, 2019, 490:80-87.
[40] Tamura R, Ohara K, Morimoto Y, Kosugi K, Oishi Y, Sato M, Yoshida K, Toda M. PITX2 expression in non-functional pituitary neuroendocrine tumor with cavernous sinus invasion[J]. Endocr Pathol, 2019, 30:81-89.
[41] Liu X, Feng M, Dai C, Bao X, Deng K, Yao Y, Wang R. Expression of EGFR in pituitary corticotroph adenomas and its relationship with tumor behavior[J]. Front Endocrinol (Lausanne), 2019, 10:785.
[42] Raverot G, Burman P, McCormack A, Heaney A, Petersenn S, Popovic V, Trouillas J, Dekkers OM; European Society of Endocrinology. European Society of Endocrinology Clinical Practice Guidelines for the management of aggressive pituitary tumours and carcinomas[J]. Eur J Endocrinol, 2018, 178:G1-24.
[43] Bengtsson D, Schrøder HD, Andersen M, Maiter D, Berinder K, Feldt Rasmussen U, Rasmussen ÅK, Johannsson G, Hoybye C, van der Lely AJ, Petersson M, Ragnarsson O, Burman P. Long-term outcome and MGMT as a predictive marker in 24 patients with atypical pituitary adenomas and pituitary carcinomas given treatment with temozolomide[J]. J Clin Endocrinol Metab, 2015, 100:1689-1698.
[44] Kontogeorgos G, Thodou E, Koutourousiou M, Kaltsas G, Seretis A. MGMT immunohistochemistry in pituitary tumors:controversies with clinical implications[J]. Pituitary, 2019, 22:614-619.
[45] Hirohata T, Asano K, Ogawa Y, Takano S, Amano K, Isozaki O, Iwai Y, Sakata K, Fukuhara N, Nishioka H, Yamada S, Fujio S, Arita K, Takano K, Tominaga A, Hizuka N, Ikeda H, Osamura RY, Tahara S, Ishii Y, Kawamata T, Shimatsu A, Teramoto A, Matsuno A. DNA mismatch repair protein (MSH6) correlated with the responses of atypical pituitary adenomas and pituitary carcinomas to temozolomide:the national cooperative study by the Japan Society for Hypothalamic and Pituitary Tumors[J]. J Clin Endocrinol Metab, 2013, 98:1130-1136.
[46] Liu XH, Wang RZ, Dai CX. Clinical significance of European Society of Endocrinology Clinical Practice Guidelines for the management of aggressive pituitary tumours and carcinomas[J]. Zhonghua Yi Xue Za Zhi, 2018, 98:1537-1539.
[47] Lasolle H, Cortet C, Castinetti F, Cloix L, Caron P, Delemer B, Desailloud R, Jublanc C, Lebrun-Frenay C, Sadoul JL, Taillandier L, Batisse-Lignier M, Bonnet F, Bourcigaux N, Bresson D, Chabre O, Chanson P, Garcia C, Haissaguerre M, Reznik Y, Borot S, Villa C, Vasiljevic A, Gaillard S, Jouanneau E, Assié G, Raverot G. Temozolomide treatment can improve overall survival in aggressive pituitary tumors and pituitary carcinomas[J]. Eur J Endocrinol, 2017, 176:769-777.
[48] Wang Y, Li J, Tohti M, Hu Y, Wang S, Li W, Lu Z, Ma C. The expression profile of Dopamine D2 receptor, MGMT and VEGF in different histological subtypes of pituitary adenomas:a study of 197 cases and indications for the medical therapy[J]. J Exp Clin Cancer Res, 2014, 33:56.
[49] Saha A, Tso S, Rabski J, Sadeghian A, Cusimano MD. Machine learning applications in imaging analysis for patients with pituitary tumors:a review of the current literature and future directions[J]. Pituitary, 2020, 23:273-293.
[50] Vitale G, Tortora F, Baldelli R, Cocchiara F, Paragliola RM, Sbardella E, Simeoli C, Caranci F, Pivonello R, Colao A; A.B.C. Group. Pituitary magnetic resonance imaging in Cushing's disease[J]. Endocrine, 2017, 55:691-696.
[51] Ikeda H, Abe T, Watanabe K. Usefulness of composite methionine-positron emission tomography/3.0-tesla magnetic resonance imaging to detect the localization and extent of early-stage Cushing adenoma[J]. J Neurosurg, 2010, 112:750-755.
[52] Niu J, Zhang S, Ma S, Diao J, Zhou W, Tian J, Zang Y, Jia W. Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images[J]. Eur Radiol, 2019, 29:1625-1634.
[53] Zeynalova A, Kocak B, Durmaz ES, Comunoglu N, Ozcan K, Ozcan G, Turk O, Tanriover N, Kocer N, Kizilkilic O, Islak C. Preoperative evaluation of tumour consistency in pituitary macroadenomas:a machine learning-based histogram analysis on conventional T2-weighted MRI[J]. Neuroradiology, 2019, 61:767-774.
[54] Zhang Y, Ko CC, Chen JH, Chang KT, Chen TY, Lim SW, Tsui YK, Su MY. Radiomics approach for prediction of recurrence in non-functioning pituitary macroadenomas[J]. Front Oncol, 2020, 10:590083.
[55] Kim M, Kim HS, Kim HJ, Park JE, Park SY, Kim YH, Kim SJ, Lee J, Lebel MR. Thin-slice pituitary MRI with deep learning-based reconstruction:diagnostic performance in a postoperative setting[J]. Radiology, 2021, 298:114-122.
[56] Alhambra-Expósito MR, Ibáñez-Costa A, Moreno-Moreno P, Rivero-Cortés E, Vázquez-Borrego MC, Blanco-Acevedo C, Toledano-Delgado Á, Lombardo-Galera MS, Vallejo-Casas JA, Gahete MD, Castaño JP, Gálvez MA, Luque RM. Association between radiological parameters and clinical and molecular characteristics in human somatotropinomas[J]. Sci Rep, 2018, 8:6173.
[57] Kocak B, Durmaz ES, Kadioglu P, Polat Korkmaz O, Comunoglu N, Tanriover N, Kocer N, Islak C, Kizilkilic O. Predicting response to somatostatin analogues in acromegaly:machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI[J]. Eur Radiol, 2019, 29:2731-2739.
[58] Dogansen SC, Yalin GY, Tanrikulu S, Tekin S, Nizam N, Bilgic B, Sencer S, Yarman S. Clinicopathological significance of baseline T2-weighted signal intensity in functional pituitary adenomas[J]. Pituitary, 2018, 21:347-354.
[59] Kosilek RP, Frohner R, Würtz RP, Berr CM, Schopohl J, Reincke M, Schneider HJ. Diagnostic use of facial image analysis software in endocrine and genetic disorders:review, current results and future perspectives[J]. Eur J Endocrinol, 2015, 173:M39-44.
[60] Meng T, Guo X, Lian W, Deng K, Gao L, Wang Z, Huang J, Wang X, Long X, Xing B. Identifying facial features and predicting patients of acromegaly using three-dimensional imaging techniques and machine learning[J]. Front Endocrinol (Lausanne), 2020, 11:492.
[61] Kong X, Gong S, Su L, Howard N, Kong Y. Automatic detection of acromegaly from facial photographs using machine learning methods[J]. EBioMedicine, 2018, 27:94-102.
[62] Kong Y, Kong X, He C, Liu C, Wang L, Su L, Gao J, Guo Q, Cheng R. Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning[J]. J Hematol Oncol, 2020, 13:88.
[63] Psaty BM, Breckenridge AM. Mini-sentinel and regulatory science-big data rendered fit and functional[J]. N Engl J Med, 2014, 370:2165-2167.
[64] Kush R, Goldman M. Fostering responsible data sharing through standards[J]. N Engl J Med, 2014, 370:2163-2165.
[65] Bando H. The current status and problems confronted in delivering precision medicine in Japan and Europe[J]. Curr Probl Cancer, 2017, 41:166-175. |