Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker which correlates with pituitary adenoma aggressiveness. Aim of our study was to assess the accuracy of machine learning analysis of texture-derived parameters from pituitary adenomas preoperative MRI for the prediction of ki-67 proliferation index class.
Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning / Ugga, L.; Cuocolo, R.; Solari, D.; Guadagno, E.; D'Amico, A.; Somma, T.; Cappabianca, P.; Del Basso De Caro, M; Cavallo, L. M.; Brunetti, A.. - In: NEURORADIOLOGY. - ISSN 0028-3940. - 61:12(2019), pp. 1365-1373. [10.1007/s00234-019-02266-1]
Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning
Ugga L.;Cuocolo R.
;Solari D.;Guadagno E.;Somma T.;Cappabianca P.;Del Basso De Caro M;Cavallo L. M.;Brunetti A.
2019
Abstract
Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker which correlates with pituitary adenoma aggressiveness. Aim of our study was to assess the accuracy of machine learning analysis of texture-derived parameters from pituitary adenomas preoperative MRI for the prediction of ki-67 proliferation index class.| File | Dimensione | Formato | |
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