Our approach includes analysis of a comprehensive set of 529 imaging features from each of the patients, development and evaluation of a multivariate model for prediction of the ODX score.Īxial breast DCE-MR images that were acquired by 1.5T or 3T scanners in the prone position were collected for all patients. The purpose of our study is to validate the association of imaging features with ODX in an independent cohort of 261 patients. While these studies showed promise for this radiogenomic association, the patient population in each of these studies was restricted to under 100 subjects, a limited number of imaging characteristics were explored, and no independent test set was used to validate the findings. ( Wan et al., 2016), 96 patients with low and high risk ODX scores were included and features of textural kinetics were explored to differentiate between high and low risk groups. Sutton et al.( Sutton et al., 2015) explored morphological and texture features from a central slice of tumor in addition to clinical features (age, menopausal status, histologic and nuclear grades, pathological tumor size and lymph node status) and developed a regression model to compute a surrogate ODX for 95 invasive ductal carcinoma patients having ODX scores in the range (0–45). In ( Li et al., 2016c), computer-extracted features quantifying size, shape, margin properties, enhancement texture and kinetic assessment from 3D tumor volume from 84 patients (from four institutions) were considered to discriminate between patients with low to medium risk versus high risk. In ( Ashraf et al., 2014), features from the time-intensity enhancement curve of tumor were delineated on a central representative slice of 56 breast cancer patients from a single institution and were shown to distinguish between high versus low and intermediate risk patients. Some of the radiogenomic studies ( Wan et al., 2016, Ashraf et al., 2014, Li et al., 2016a, Sutton et al., 2015) showed initial data on the association of imaging features with ODX, illustrating a potential avenue of a surrogate noninvasive test. Also, multiple studies ( Uematsu et al., 2009, Li et al., 2016a, Li et al., 2016b, Ashraf et al., 2014, Blaschke and Abe, 2015, Wan et al., 2016, Fan et al., 2017, Wu et al., 2017, Sutton et al., 2015), most of which focused on tumor intrinsic molecular subtype, are present in the literature. Specifically several of DCE-MRI-based features have been shown to be associated with recurrence-free survival in breast cancer ( Mazurowski et al., 2015, Kim et al., 2017). These analyses are referred to as radiomics ( Gillies et al., 2016) and radiogenomics( Mazurowski, 2015). Recently, an increased research focus has been placed on using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict patient outcomes and tumor genomics. These surrogate measures require the immunohistochemical (IHC) analysis of the tumor sample. Therefore, several surrogate measures of ODX were explored in previous studies ( Klein et al., 2013, Gage et al., 2015, Tang et al., 2010). However, there are limitations to obtaining ODX scores (from tumor specimens) as discussed in ( Harowicz et al., 2017), since it is an expensive and time-consuming procedure. Any ODX score between low and high is considered intermediate ( Paik et al., 2004). The 10-year risk of distant recurrence is estimated by the ODX score on a scale of 0 to 100 and considered low if the ODX score is less than 18, and high if ODX score is greater than or equal than 31. Of the variety of multi-gene assays, the 21-gene expression assay Oncotype DX (ODX) (Genomic Health, Redwood City, CA) test is recommended by the current guidelines ( 2006, Paik et al., 2004, Paik et al., 2006, Coates et al., 2015) for a specific group of female patients (early-stage, hormone receptor-positive, node-negative, and HER2-negative disease with tumor size equal or greater than 1cm) and is often used in clinical practice for assessing chemotherapy benefit and the risk of distant recurrence. In breast cancer related literature, prior studies ( Paik et al., 2004, 2006, Paik et al., 2006, Nielsen et al., 2010, Dowsett et al., 2013, Wittner et al., 2008) have shown that multi-assay gene expression profiling is associated with patient outcomes and response to specific systemic therapies.
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