Lorenzo Ugga1, Renato Cuocolo2, Domenico Solari3, Elia Guadagno4, Alessandra D'Amico1, Teresa Somma3, Paolo Cappabianca3, Maria Laura Del Basso de Caro4, Luigi Maria Cavallo3, Arturo Brunetti1. 1. Department of Advanced Biomedical Sciences, University of Naples "Federico II", via Sergio Pansini 5, 80131, Naples, Italy. 2. Department of Advanced Biomedical Sciences, University of Naples "Federico II", via Sergio Pansini 5, 80131, Naples, Italy. renato.cuocolo@unina.it. 3. Department of Neurosciences, Reproductive and Odontostomatological Sciences, Division of Neurosurgery, University of Naples "Federico II", Naples, Italy. 4. Department of Advanced Biomedical Sciences, Pathology Section, University of Naples "Federico II", Naples, Italy.
Abstract
PURPOSE: 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. METHODS: A total of 89 patients who underwent an endoscopic endonasal procedure for pituitary adenoma removal with available ki-67 labeling index were included. From T2w MR images, 1128 quantitative imaging features were extracted. To select the most informative features, different supervised feature selection methods were employed. Subsequently, a k-nearest neighbors (k-NN) classifier was employed to predict macroadenoma high or low proliferation index. Algorithm validation was performed with a train-test approach. RESULTS: Of the 12 subsets derived from feature selection, the best performing one was constituted by the 4 highest correlating parameters at Pearson's test. These all showed very good (ICC ≥ 0.85) inter-observer reproducibility. The overall accuracy of the k-NN in the test group was of 91.67% (33/36) of correctly classified patients. CONCLUSIONS: Machine learning analysis of texture-derived parameters from preoperative T2 MRI has proven to be effective for the prediction of pituitary macroadenomas ki-67 proliferation index class. This might aid the surgical strategy making a more accurate preoperative lesion classification and allow for a more focused and cost-effective follow-up and long-term management.
PURPOSE: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. METHODS: A total of 89 patients who underwent an endoscopic endonasal procedure for pituitary adenoma removal with available ki-67 labeling index were included. From T2w MR images, 1128 quantitative imaging features were extracted. To select the most informative features, different supervised feature selection methods were employed. Subsequently, a k-nearest neighbors (k-NN) classifier was employed to predict macroadenoma high or low proliferation index. Algorithm validation was performed with a train-test approach. RESULTS: Of the 12 subsets derived from feature selection, the best performing one was constituted by the 4 highest correlating parameters at Pearson's test. These all showed very good (ICC ≥ 0.85) inter-observer reproducibility. The overall accuracy of the k-NN in the test group was of 91.67% (33/36) of correctly classified patients. CONCLUSIONS: Machine learning analysis of texture-derived parameters from preoperative T2 MRI has proven to be effective for the prediction of pituitary macroadenomas ki-67 proliferation index class. This might aid the surgical strategy making a more accurate preoperative lesion classification and allow for a more focused and cost-effective follow-up and long-term management.
Entities:
Keywords:
Machine learning; Magnetic resonance imaging; Pituitary adenoma
Authors: Matteo de Notaris; Domenico Solari; Luigi M Cavallo; Alfonso Iodice D'Enza; Joaquim Enseñat; Joan Berenguer; Enrique Ferrer; Alberto Prats-Galino; Paolo Cappabianca Journal: J Neurosurg Date: 2011-12-23 Impact factor: 5.115