Document - May radiomic data predict prostate cancer aggressiveness?

2019

Conference article Open Access

May radiomic data predict prostate cancer aggressiveness?

Germanese D., Colantonio S., Caudai C., Pascali M. A., Barucci A., Zoppetti N., Agostini S., Bertelli E., Mercatelli L., Miele V., Carpi R.

Prostate Cancer Machine Learning Radiomics

Radiomics can quantify tumor phenotypic characteristics non-invasively by defining a signature correlated with biological information. Thanks to algorithms derived from computer vision to extract features from images, and machine learning methods to mine data, Radiomics is the perfect case study of application of Artificial Intelligence in the context of precision medicine. In this study we investigated the association between radiomic features extracted from multi-parametric magnetic resonance imaging (mp-MRI)of prostate cancer (PCa) and the tumor histologic subtypes (using Gleason Score) using machine learning algorithms, in order to identify which of the mp-MRI derived radiomic features can distinguish high and low risk PCa.

Source: CAIP 2019 - International Conference on Computer Analysis of Images and Patterns, pp. 65–75, Salerno, Italy, 6 September, 2019

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@inproceedings{oai:it.cnr:prodotti:412986,
	title = {May radiomic data predict prostate cancer aggressiveness?},
	author = {Germanese D. and Colantonio S. and Caudai C. and Pascali M. A. and Barucci A. and Zoppetti N. and Agostini S. and Bertelli E. and Mercatelli L. and Miele V. and Carpi R.},
	doi = {10.1007/978-3-030-29930-9_7},
	booktitle = {CAIP 2019 - International Conference on Computer Analysis of Images and Patterns, pp. 65–75, Salerno, Italy, 6 September, 2019},
	year = {2019}
}