چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده | نفر چندم مقاله |
|---|---|
| محیا نقی پور علمداری | اول |
| منا فاضل قاضیانی | چهارم |
| عنوان | متن |
|---|---|
| خلاصه مقاله | Lung cancer is one of the most common cancers in the world, accounting for the first place in men (16.7%) and third in women (8.7%). During the past decades there have been many advances in the field of recognition and assessment of the cancer, such as medical imaging innovated with new hardware, new imaging agents and standardized protocols, allows the field to move towards quantitative imaging methods such as radiomics. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified by human eyes. In radiomics images are more than pictures, they are data. Radiomics differs from computer-aided diagnosis and detection (CAD) systems. While both use medical images from computed tomography(CT) and magnetic resonance(MRI), CAD systems are designed to detect or diagnose a disease. Radiomics, on the other hand, extracts and stores features for hypothesis testing and to develop decision support tools. Radiomics is intended to supply imaging biomarkers for cancer detection, diagnosis, prognosis, treatment prediction, and monitoring even better than TNM staging. Different imaging modalities are used for radiomics analysis such as CT scan. CT images have a powerful application that exhibit strong contrast reflecting differences in the intensity of a tumor, intra tumor texture and tumor shape of lung cancer. CT provides a picture of the Whole lung nodule to consider cancer heterogeneity.We can infer phenotypes or gene–protein signatures, possibly containing prognostic information, from the quantitative analysis of medical image data. More specifically, it has been demonstrated that major differences in protein expression patterns within a tumor can be correlated to radiographic findings such as contrast-enhanced and non-enhanced regions based on CT data. This may have a clinical impact as imaging is ‘personalized medicine’, where treatment is increasingly tailored on the basis of specific characteristics of the patient and their disease of course with low cost. In this review, we will look through the steps of radiomics in combination with different biological experiments in oncology, specifically addressing potential applications of CT image in lung cancer and focusing on technical issues |
| کلمات کلیدی | Radiomics, Computed tomography, oncology, lung cancer, personalize medicine. |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| Abstract (2).pdf | 1399/04/18 | 5490440 | دانلود |
