Machine learning-based clinical outcome prediction in surgery for acromegaly

[1] Dineen R(2017)Acromegaly QJM Mon. J. Assoc. Physicians 110 411-420

[2] Stewart PM(2017)The surgical treatment of acromegaly Pituitary 20 76-83

[3] Sherlock M(2014)Factors associated with biochemical remission after microscopic transsphenoidal surgery for acromegaly J. Neurol. Surg. B Skull Base 75 47-52

[4] Buchfelder M(2021)Predictors for remission after transsphenoidal surgery in acromegaly: a Dutch Multicenter Study J. Clin. Endocrinol. Metab. 106 1783-1792

[5] Schlaffer S-M(2021)A Pituitary Society update to acromegaly management guidelines Pituitary 24 1-13

[6] Sun H(2016)Predicting the future — big data, machine learning, and clinical medicine N. Engl. J. Med. 375 1216-1219

[7] Brzana J(2018)Machine learning and neurosurgical outcome prediction: a systematic review World Neurosurg. 109 476-486.e1

[8] Yedinak CG(2018)Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study Neurosurg. Focus 45 E12-1303

[9] Gultekin SH(2012)Prevention of intraoperative cerebrospinal fluid leaks by lumbar cerebrospinal fluid drainage during surgery for pituitary macroadenomas: clinical article J. Neurosurg. 116 1299-63

[10] Delashaw JB(2015)Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement Ann. Intern. Med. 162 55-835

[1] Dineen R(2017)Acromegaly QJM Mon. J. Assoc. Physicians 110 411-420

[2] Stewart PM(2017)The surgical treatment of acromegaly Pituitary 20 76-83

[3] Sherlock M(2014)Factors associated with biochemical remission after microscopic transsphenoidal surgery for acromegaly J. Neurol. Surg. B Skull Base 75 47-52

[4] Buchfelder M(2021)Predictors for remission after transsphenoidal surgery in acromegaly: a Dutch Multicenter Study J. Clin. Endocrinol. Metab. 106 1783-1792

[5] Schlaffer S-M(2021)A Pituitary Society update to acromegaly management guidelines Pituitary 24 1-13

[6] Sun H(2016)Predicting the future — big data, machine learning, and clinical medicine N. Engl. J. Med. 375 1216-1219

[7] Brzana J(2018)Machine learning and neurosurgical outcome prediction: a systematic review World Neurosurg. 109 476-486.e1

[8] Yedinak CG(2018)Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study Neurosurg. Focus 45 E12-1303

[9] Gultekin SH(2012)Prevention of intraoperative cerebrospinal fluid leaks by lumbar cerebrospinal fluid drainage during surgery for pituitary macroadenomas: clinical article J. Neurosurg. 116 1299-63

[10] Delashaw JB(2015)Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement Ann. Intern. Med. 162 55-835