چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده | نفر چندم مقاله |
|---|---|
| داود خضرلو | اول |
| یونس سلیمانی کیچی قلعه سی | دوم |
| عنوان | متن |
|---|---|
| خلاصه مقاله | Introduction: Extraction of clinical and genomic information through mathematical and statistical features from medical images is called radiomics and radiogenomics respectively. Ki67 labeling biomarker is a predictive and prognostic index of breast cancer. Biomarkers are determined by immunohistology tests and biopsy, which are aggressive and have systematic and random errors. Also, biomarkers are heterogeneously distributed and their pattern change during treatment. In breast cancer, radiogenomics is a type of non-invasive histopathologic sampling extracted from MRI images and applied to the following scopes: differentiation of benign masses from malignant, pathologic staging, molecular and genomic subtyping and prognosis in terms of invasiveness .The purpose of this study was to investigate whether MRI-Radiogenomic can predict Ki 67 expression. Material and methods: The keywords of 'Breast Cancer', 'Radiomics', 'Radiogenomics' and “MRI” were entered in the scientific databases of Google scholar, Scopus, PubMed, and Elsevier. About 10 fully relevant articles were extracted and reviewed. Then the correlation between MRI-based radiogenomics and Ki67 expression were obtained and assessed. Results: All papers indicated that T2-weighted MRI and T2-weighted DCE-MRI images were suitable for Ki67 biomarker analysis. Among morphological, gray scale and textural-based features, the texture features had highest correlation with Ki67. So that tumors with high Ki67 level shows high texture features. The DCE-MRI has a high sensitivity but low specificity for Ki67 detection. Also, it is a time-consuming scan and features depends on time patterns. Nevertheless T2-weighted scan is fast. Studies showed that Area under ROC curve (AUC) was about 0.7-0.78 in both T2-weighted DCE-MRI and T2-weighted MRI images. Conclusion: The results of our study showed that radiogenomics-based quantitative analysis of MR images, can significantly help to prediction of Ki67 expression. |
| کلمات کلیدی | Key words: Breast Cancer', 'Radiomics', 'Radiogenomics' , “MRI” |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| 2-breast MRI Radiomics.pdf | 1399/02/10 | 781460 | دانلود |
| breast cancer-eBOOK.pdf | 1399/02/10 | 15237890 | دانلود |
