Document - Radiomics to predict prostate cancer aggressiveness: a preliminary study

2019

Conference article Closed Access

Radiomics to predict prostate cancer aggressiveness: a preliminary study

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

Artificial Intelligence Machine learning Prostate Cancer Medical Imaging Radiomics

Radiomics is encouraging a paradigm shift in oncological diagnostics towards the symbiosis of radiology and Artificial Intelligence (AI) techniques. The aim is to exploit very accurate, robust image processing algorithms and provide quantitative information about the phenotypic differences of cancer traits. By exploring the association between this quantitative information and patients' prognosis, AI algorithms are boosting the power of radiomics in the perspective of precision oncology. However, the choice of the most suitable AI method can determine the success of a radiomic application. The current state-of-the art methods in radiomics aim at extracting statistical features from biomedical images and, then, process them with Machine Learning (ML) techniques. Many works have been reported in the literature presenting various combinations of radiomic features and ML methods. In this preliminary study, we aim to analyse the performance of a radiomic approach to predict prostate cancer (PCa) aggressiveness from multiarametric Magnetic Resonance Imaging (mp-MRI). Clinical mp-MRI data were collected from patients with histology-confirmed PCa and labelled by a team of expert radiologists. Such data were used to extract and select two sets of radiomic features; hence, the classification performances of five classifiers were assessed. This analysis is meant as a preliminary step towards the overall goal of investigating the potential of radiomic-based analyses.

Source: BIBE 2019: 19th annual IEEE International Conference on Bioinformatics and Bioengineering, pp. 972–976, Athens, Greece, 28-30 October 2019

Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141, USA

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@inproceedings{oai:it.cnr:prodotti:419578,
	title = {Radiomics to predict prostate cancer aggressiveness: a preliminary study},
	author = {Germanese D. and Mercatelli L. and Colantonio S. and Miele V. and Pascali M. A. and Caudai C. and Zoppetti N. and Carpi R. and Barucci A. and Bertelli E. and Agostini S.},
	publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141, USA},
	doi = {10.1109/bibe.2019.00181},
	booktitle = {BIBE 2019: 19th annual IEEE International Conference on Bioinformatics and Bioengineering, pp. 972–976, Athens, Greece, 28-30 October 2019},
	year = {2019}
}