A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
By Ashirbani Saha, M. Harowicz, Lars J. Grimm, Connie E. Kim, S. Ghate, R. Walsh, Maciej A. Mazurowski. British Journal of Cancer, 2018
301 citations recorded by the source corpus.
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.
Subfields: Cancer Research, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/e2afc61b0d8bb64fc3dc3978099adadceab41cf1
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Citations in the corpus
- The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, methodology): “Several previous studies utilized radiomics for the prediction of breast cancer subtype or the status of ER, PR, Ki67 and HER2 with US [46], mammography [118], PET/CT [119, 120], and MRI [121, 122].”