چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده ثبت کننده مقاله | داود خضرلو |
| مرحله جاری مقاله | تایید نهایی |
| دانشکده/مرکز مربوطه | دانشکده پیراپزشکی |
| کد مقاله | 82808 |
| عنوان فارسی مقاله | رادیومیکس: زمینه ای در حال توسعه برای پیش آگهی و تشخیص بهتر بیماران متاستاز ریه |
| عنوان لاتین مقاله | Radiomics: a developing field for better prognosis and diagnosis of lung metastasis patients |
| نوع ارائه | پوستر |
| عنوان کنگره / همایش | کنگره بین المللی رویکردهای نوین سبک زندگی، پیشگیری و درمان سرطان |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | Iran (Islamic Republic) |
| شهر محل برگزاری کنگره/ همایش | آمل |
| سال انتشار/ ارائه شمسی | 1402 |
| سال انتشار/ارائه میلادی | 2023 |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1402/07/13 الی 1402/07/15 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | https://clpt.ir/2023 |
| آدرس علمی (Affiliation) نویسنده متقاضی | Department of Radiology, Faculty of Allied Medical Sciences, Tabriz University of Medical Science, Tabriz, Iran |
| نویسنده | نفر چندم مقاله |
|---|---|
| داود خضرلو | دوم |
| عنوان | متن |
|---|---|
| خلاصه مقاله | Introduction: About 20-54% of metastatic patients experience lung metastasis (LM) as the second commonest site for metastasis in cancer patients. The most common extra-thoracic cancers which lead to LM are breast, colorectal, renal, uterine cancer, leiomyosarcoma, and head and neck carcinoma. The most important challenge is to differentiate the primary or benign lung lesions from LM. Common diagnostic methods for the issue are multidetector computed tomography (MDCT) and 18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Despite the multiple advantages of these two imaging modalities, they could confront false positive or false negative results. On the other hand, biopsy as the gold standard to distinguish between primitive lung tumors or LM is highly invasive and not applicable to all cases. Radiomics is an active area of research to extract quantitative data from diagnostic images, which can serve as useful imaging biomarkers for more effective and customized patient care. Our purpose is to review the current applications of radiomics for lesion characterization, treatment planning, and prognostic assessment in patients with LM. Methods: The keywords of “radiomics”, “metastasis OR metastases”, “machine learning”, and “lung OR pulmonary” were entered into scientific databases of Google scholar, Scopus, PubMed, and Elsevier. Finally, 8 fully relevant papers (publication year: 2018-2022) were extracted and reviewed. Results: The number of patients evaluated in the 8 studies was variable between 51 and 769. The studies could be categorized into 1. Studies distinguishing histological subtypes of the LM (n = 6), 2. Studies evaluating the mutational status of the LM (n = 1), and 3. Studies evaluating the prognostic ability of radiomics (n = 1). In all papers, the imaging modality was CT or 18F-FDG PET/CT. The segmentation process of the metastatic lesions was conducted manually or semi-automatically using artificial intelligence approaches. In all papers, the machine learning methods’ performance to distinguish histological subtypes or evaluate the mutational status and prognostic ability was variable (poor performance with AUC=0.57 to excellent performance with AUC= 0.98). Conclusion: Despite the potential of overcoming the conventional imaging methods for LM patient management, radiomics is not still well adopted clinically. This could be due to several factors such as the standardization of imaging parameters and radiomics features definitions. |
| کلمات کلیدی | “Lung metastasis”, “Radiomics”, “Machine learning” |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| Abstract-1.pdf | 1403/01/05 | 515076 | دانلود |
| Abstract_Book_5_.pdf | 1403/01/05 | 1911642 | دانلود |
| article 2-poster-certiciation.pdf | 1402/07/22 | 1263564 | دانلود |
