Germline BRCA 1-2 status prediction through ovarian ultrasound images radiogenomics: a hypothesis generating study (PROBE study)
| Autor: | Ettore Capoluongo, Vincenzo Valentini, Anna Fagotti, Giovanni Scambia, Francesca Ciccarone, Ida Paris, Luca Boldrini, Camilla Nero, Jacopo Lenkowicz, Antonia Carla Testa |
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| Přispěvatelé: | Nero, C., Ciccarone, F., Boldrini, L., Lenkowicz, J., Paris, I., Capoluongo, E. D., Testa, A. C., Fagotti, A., Valentini, V., Scambia, G. |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adult
Support Vector Machine BRCA Radiogenomics Decision tree lcsh:Medicine Feature selection Pilot Projects Logistic regression Machine learning computer.software_genre Germline Article 030218 nuclear medicine & medical imaging Cancer prevention Set (abstract data type) Machine Learning 03 medical and health sciences 0302 clinical medicine Ultrasound Medicine Humans lcsh:Science Cancer genetics Aged Retrospective Studies Ultrasonography BRCA2 Protein Univariate analysis Multidisciplinary business.industry BRCA1 Protein lcsh:R Medical genetics Ovary Middle Aged Support vector machine Settore MED/40 - GINECOLOGIA E OSTETRICIA Germ Cells Ovarian 030220 oncology & carcinogenesis lcsh:Q Female Artificial intelligence business computer Algorithms Forecasting |
| Zdroj: | Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020) |
| ISSN: | 2045-2322 |
| Popis: | Radiogenomics is a specific application of radiomics where imaging features are linked to genomic profiles. We aim to develop a radiogenomics model based on ovarian US images for predicting germline BRCA1/2 gene status in women with healthy ovaries. From January 2013 to December 2017 a total of 255 patients addressed to germline BRCA1/2 testing and pelvic US documenting normal ovaries, were retrospectively included. Feature selection for univariate analysis was carried out via correlation analysis. Multivariable analysis for classification of germline BRCA1/2 status was then carried out via logistic regression, support vector machine, ensemble of decision trees and automated machine learning pipelines. Data were split into a training (75%) and a testing (25%) set. The four strategies obtained a similar performance in terms of accuracy on the testing set (from 0.54 of logistic regression to 0.64 of the auto-machine learning pipeline). Data coming from one of the tested US machine showed generally higher performances, particularly with the auto-machine learning pipeline (testing set specificity 0.87, negative predictive value 0.73, accuracy value 0.72 and 0.79 on training set). The study shows that a radiogenomics model on machine learning techniques is feasible and potentially useful for predicting gBRCA1/2 status in women with healthy ovaries. |
| Databáze: | OpenAIRE |
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