The aim of this review was to provide an overview of the main concepts in machine learning(ML)and to analyze the ML applications in the imaging of pituitary adenomas.After describing the clinical,pathological and imag...The aim of this review was to provide an overview of the main concepts in machine learning(ML)and to analyze the ML applications in the imaging of pituitary adenomas.After describing the clinical,pathological and imaging features of pituitary tumors,we defined the difference between ML and classical rule-based algorithms,we illustrated the fundamental ML techniques:supervised,unsupervised and reinforcement learning and explained the characteristic of deep learning,a ML approach employing networks inspired by brain’s structure.Pretreatment assessment and neurosurgical outcome prediction were the potential ML applications using magnetic resonance imaging.Regarding pre-treatment assessment,ML methods were used to have information about tumor consistency,predict cavernous sinus invasion and high proliferative index,discriminate null cell adenomas,which respond to neo-adjuvant radiotherapy from other subtypes,predict somatostatin analogues response and visual pathway injury.Regarding neurosurgical outcome prediction,the following applications were discussed:Gross total resection prediction,evaluation of Cushing disease recurrence after transsphenoidal surgery and prediction of cerebrospinal fluid fistula’s formation after surgery.Although clinical applicability requires more replicability,generalizability and validation,results are promising,and ML software can be a potential power to facilitate better clinical decision making in pituitary tumor patients.展开更多
文摘The aim of this review was to provide an overview of the main concepts in machine learning(ML)and to analyze the ML applications in the imaging of pituitary adenomas.After describing the clinical,pathological and imaging features of pituitary tumors,we defined the difference between ML and classical rule-based algorithms,we illustrated the fundamental ML techniques:supervised,unsupervised and reinforcement learning and explained the characteristic of deep learning,a ML approach employing networks inspired by brain’s structure.Pretreatment assessment and neurosurgical outcome prediction were the potential ML applications using magnetic resonance imaging.Regarding pre-treatment assessment,ML methods were used to have information about tumor consistency,predict cavernous sinus invasion and high proliferative index,discriminate null cell adenomas,which respond to neo-adjuvant radiotherapy from other subtypes,predict somatostatin analogues response and visual pathway injury.Regarding neurosurgical outcome prediction,the following applications were discussed:Gross total resection prediction,evaluation of Cushing disease recurrence after transsphenoidal surgery and prediction of cerebrospinal fluid fistula’s formation after surgery.Although clinical applicability requires more replicability,generalizability and validation,results are promising,and ML software can be a potential power to facilitate better clinical decision making in pituitary tumor patients.
