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Cited by 31Radiomics and Machine Learning in Medical ImagingBRCA gene mutations in cancerAI in cancer detection

Germline BRCA 1-2 status prediction through ovarian ultrasound images radiogenomics: A hypothesis generating study (PROBE study)

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Nero, Camilla, Ciccarone, Francesca, Boldrini, Luca, Lenkowicz, Jacopo, Paris, Ida, Capoluongo, Ettore Domenico, Testa, Antonia Carla, Fagotti, Anna +2 more·2020·Scientific Reports
DOI 10.1038/s41598-020-73505-2PMC PMC7536234
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Abstract

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.

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Research works printed but not linked: 2

  1. [15]Nelson, H.D., Fu, R., Goddard, K. et al.Risk Assessment, Genetic Counseling, and Genetic Testing for BRCA-Related Cancer: Systematic Review to Update the U.S. Preventive Services Task Force Recommendation. Report No.: 12-05164-EF-1. (Agency for Healthcare Research and Quality (US), Rockville, 2013). 24432435
  2. [50]ACOG Committee Opinion, number 755 Vol. 132, No. 4, October 2018 (Replaces Committee Opinion No. 534, August 2012)

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Authors
Nero, C., Ciccarone, F., Boldrini, L., Lenkowicz, J., Paris, I., Capoluongo, E. D., Testa, A. C., Fagotti, A., Valentini, V., & Scambia, G.
Affiliations
  • Università Cattolica del Sacro Cuore (IT)
  • Agostino Gemelli University Polyclinic (IT)
  • University of Naples Federico II (IT)
Corresponding author
Nero, Camilla
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2020-10-05
Volume
10
Issue
1
Pages
16511-16511
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-020-73505-2
PMC
PMC7536234
OpenAlex
W3089516664
Cited by
31
References
52
Citations by year
2026: 2 · 2025: 6 · 2024: 8 · 2023: 3 · 2022: 6 · 2021: 6
FWCI
1.83
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, other-oa)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
radiogenomics, ovarian ultrasonography, automated machine learning, support vector machine, decision tree ensembles, logistic regression, hereditary breast and ovarian cancer
MeSH terms
Machine Learning, Adult, Aged, Algorithms, Female, Forecasting, Germ Cells, Humans, Middle Aged, Ovary, Pilot Projects, Retrospective Studies, Ultrasonography, BRCA1 Protein, BRCA2 Protein, Support Vector Machine

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