MRI-based radiogenomics analysis for predicting genetic alterations in oncogenic signalling pathways in invasive breast carcinoma. Issue 7 (July 2020)

• Record Type: Journal Article
• Authors: Lin, P.; Liu, W.K.; Li, X.; Wan, D.; Qin, H.; Li, Q.; Chen, G.; He, Y.; Yang, H.
• Is Part Of: Clinical radiology. Volume 75:Issue 7(2020)
• Page Start: 561.e1
• Page End: 561.e11
• Publication Date: 2020-07
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2020.02.011
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : AIM: To investigate the effect of radiomics in the assessment of alterations in canonical cancer pathways in breast cancer. MATERIALS AND METHODS: Eighty-eight biopsy-proven breast cancer cases were included in the present study. Radiomics features were extracted from T1-weighted sagittal dynamic contrast-enhanced magnetic resonance imaging (MRI) images. Radiomics signatures were developed to predict genetic alterations in the cell cycle, Myc, PI3K, RTK/RAS, and p53 signalling pathways by using hypothesis testing combined with least absolute shrinkage and selection operator (LASSO) regression analysis. The predictive powers of the models were examined by the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: A total of 5, 234 radiomics features were obtained from MRI images based on the tumour region of interest. Hypothesis tests screened 250, 229, 156, 785, and 319 radiomics features that were differentially displayed between cell cycle, Myc, PI3K, RTK/RAS, and p53 alterations and no alteration status. According to the LASSO algorithm, 11, 12, 12, 15, and 13 features were identified for the construction of the radiomics signatures to predict cell cycle, Myc, PI3K, RTK/RAS, and p53 alterations, with AUC values of 0.933, 0.926, 0.956, 0.940, and 0.886, respectively. The cell cycle radiomics score correlated closely with the RTK/RAS and p53 radiomics scores. These signatures were also dysregulated in patients with different oestrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 statuses. CONCLUSION: MRI-based radiogenomics analysis exhibits excellent performance in predicting genetic pathways alterations, thus providing a novel approach for non-invasively obtaining genetic-level molecular characteristics of tumours. Highlights: Radiogenomics analysis elucidates the relationships between MRI features and the molecular characteristics of breast cancer. MRI-based radiomics shows a favourable performance for the prediction of oncogenic signalling pathway alterations. The radiomics signatures hold potential in the individualized prediction of combined therapy.