Search

Purpose: We assessed the decision-making of individuals pursuing genomic sequencing without a requirement for pretest genetic counselling. We sought to describe the extent to which individuals who decline genetic counselling reported decisional conflict or struggled to make a decision to pursue genomic testing., Methods: We administered a 100-item survey to 3037 individuals who consented to the Return of Actionable Variants Empirical study, a genomic medicine implementation study supported by the National Institutes of Health (USA) eMERGE consortium. The primary outcomes of interest were self-reported decisional conflict about the decision to participate in the study and time required to reach a decision., Results: We received 2895 completed surveys (response rate=95.3%), and of these respondents 97.8% completed the decisional conflict scale in its entirety. A majority of individuals (63%) had minimal or no decisional conflict about the pursuit of genomic sequencing and were able to reach a decision quickly (78%). Multivariable logistic regression analyses identified several characteristics associated with decisional conflict, including lower education, lower health literacy, lower self-efficacy in coping, lack of prior experience with genetic testing, not discussing study participation with a family member or friend, and being male., Conclusion: As genomic sequencing is used more widely, genetic counselling resources may not be sufficient to meet demand. Our results challenge the notion that all individuals need genetic counselling in order to make an informed decision about genomic sequencing., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors., Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620., (© 2017 International Society for Magnetic Resonance in Medicine.)

Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R 2 = 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence. © RSNA, 2016 Online supplemental material is available for this article., Competing Interests: of Conflicts of Interest: H.L. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed no relevant relationships. Other relationships: author receives royalties from U.S. patent 9,208,556. Y.Z. disclosed no relevant relationships. E.S.B. disclosed no relevant relationships. K.D. disclosed no relevant relationships. K.A.H. disclosed no relevant relationships. C.F. disclosed no relevant relationships. S.D.C. disclosed no relevant relationships. G.J.W. disclosed no relevant relationships. E.J.S. disclosed no relevant relationships. J.M.N. disclosed no relevant relationships. M.G. disclosed no relevant relationships. E.H. disclosed no relevant relationships. E.A.M. disclosed no relevant relationships. C.M.P. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author receives payment from Bioclassifier for board membership and consultancy; author receives payments for PAM50 patents; author and institution receive royalties from Bioclassifier; author has stock or stock options in Bioclassifier. Other relationships: disclosed no relevant relationships. Y.J. disclosed no relevant relationships. M.L.G. Activities related to the present article: author received grants from P30 CA14599 and P50-CA58223. Activities not related to the present article: author is a stockholder in R2 technology/Hologic; author receives royalties from Hologic, GE Medical Systems, Median Technologies, Riverain Medical, Mitsubishi, and Toshiba; author is a cofounder and stockholder in Quantitative Insights. Other relationships: disclosed no relevant relationships.

Purpose: To investigate the association between a validated, gene-expression-based, aggressiveness assay, Oncotype Dx RS, and morphological and texture-based image features extracted from magnetic resonance imaging (MRI)., Materials and Methods: This retrospective study received Internal Review Board approval and need for informed consent was waived. Between 2006-2012, we identified breast cancer patients with: 1) ER+, PR+, and HER2- invasive ductal carcinoma (IDC); 2) preoperative breast MRI; and 3) Oncotype Dx RS test results. Extracted features included morphological, histogram, and gray-scale correlation matrix (GLCM)-based texture features computed from tumors contoured on pre- and three postcontrast MR images. Linear regression analysis was performed to investigate the association between Oncotype Dx RS and different clinical, pathologic, and imaging features. P < 0.05 was considered statistically significant., Results: Ninety-five patients with IDC were included with a median Oncotype Dx RS of 16 (range: 0-45). Using stepwise multiple linear regression modeling, two MR-derived image features, kurtosis in the first and third postcontrast images and histologic nuclear grade, were found to be significantly correlated with the Oncotype Dx RS with P = 0.0056, 0.0005, and 0.0105, respectively. The overall model resulted in statistically significant correlation with Oncotype Dx RS with an R-squared value of 0.23 (adjusted R-squared = 0.20; P = 0.0002) and a Spearman's rank correlation coefficient of 0.49 (P < 0.0001)., Conclusion: A model for IDC using imaging and pathology information correlates with Oncotype Dx RS scores, suggesting that image-based features could also predict the likelihood of recurrence and magnitude of chemotherapy benefit., (© 2015 Wiley Periodicals, Inc.)