A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features

Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship. We analysed a set of 922 patients with invasive breast...

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Vydáno v:	British journal of cancer Ročník 119; číslo 4; s. 508 - 516
Hlavní autoři:	Saha, Ashirbani, Harowicz, Michael R, Grimm, Lars J, Kim, Connie E, Ghate, Sujata V, Walsh, Ruth, Mazurowski, Maciej A
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Nature Publishing Group 14.08.2018
Témata:	Adult Aged Aged, 80 and over Area Under Curve Biomarkers, Tumor - genetics Biomarkers, Tumor - metabolism Breast cancer Breast Neoplasms - diagnostic imaging Breast Neoplasms - genetics Breast Neoplasms - metabolism Breast Neoplasms - pathology Epidermal growth factor Female Genomics - methods Humans Invasiveness Learning algorithms Machine Learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Middle Aged Phenotypes Progesterone Receptor, ErbB-2 - metabolism Receptors, Estrogen - metabolism Receptors, Progesterone - metabolism Tumors Young Adult
ISSN:	0007-0920, 1532-1827, 1532-1827
On-line přístup:	Získat plný text
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Shrnutí:	Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship. We analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as well as a tumour proliferation marker (Ki-67). Trained models were evaluated on the set of the remaining 461 patients. Multivariate models were predictive of Luminal A subtype with AUC = 0.697 (95% CI: 0.647-0.746, p < .0001), triple negative breast cancer with AUC = 0.654 (95% CI: 0.589-0.727, p < .0001), ER status with AUC = 0.649 (95% CI: 0.591-0.705, p < .001), and PR status with AUC = 0.622 (95% CI: 0.569-0.674, p < .0001). Associations between individual features and subtypes we also found. There is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0007-0920 1532-1827 1532-1827
DOI:	10.1038/s41416-018-0185-8