Medical Informatics

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DOI :10.26650/B/ET07.2021.003.11   IUP :10.26650/B/ET07.2021.003.11    Full Text (PDF)

Radiomics Definition, History, Applications, and Workflow

Merve Gülbiz Dağoğlu Kartal, Sena Azamat

Medical imaging is one of the crucial techniques that are used to reveal accurate diagnosis and tailor appropriate management. Radiomics depends on the hypothesis that quantitative information obtained from the anatomic and functional data that make up the image reflects the underlying pathophysiology. Initial studies about radiomics were mostly conducted in the field of oncology since it has been discovered that tumor heterogeneity is the fundamental factor that hinders success in this field. Biopsy taken invasively to obtain a limited number of samples does not always reflect the features of an entire volume of the tumor. In contrast, the ability to assess the entire volume non-invasively with objective data is an important tool to determine an efficient treatment plan specific to every patient and to predict prognosis. In this context, radiomics is promising to be used in precision medicine. The radiomics process is based on the extraction of huge data from imaging features and correlating them with clinical or genetic data. Basic steps in workflow include: (a) obtaining the image, (b) determining the region of interest and segmentation, (c) extracting features, and (d) using extracted features to obtain a database. Each step has its own limitations and challenges. However, the fundamental problem regarding every step is the heterogeneity of the data input. The potential of radiomics, which is still not mature enough, should not be overlooked and multicentral prospective studies with a large series should be conducted to find solutions to implement radiomic data in routine practice.


Keywords: Radiomics, artificial intelligence, medical imaging, precision medicine, data mining
DOI :10.26650/B/ET07.2021.003.11   IUP :10.26650/B/ET07.2021.003.11    Full Text (PDF)

Radyomiks Tanımı, Tarihçesi, Kullanım Alanları Ve İş Akışı

Merve Gülbiz Dağoğlu Kartal, Sena Azamat

Radyomiks, tıbbi görüntüleme hastalıkların tanı ve tedavisine yön veren en önemli faktörlerden biridir. Radyomiks, radyolojik görüntüyü oluşturan anatomik ve fonksiyonel verilerden elde olunacak gözle ayırt edilemeyen niceliksel verilerin bir dokunun patofizyolojisini oluşturduğu görüşüne dayanmaktadır. Radyomiksle ilgili çalışmalar ilk önce onkoloji alanında başlamıştır. Bunun temel nedeni bu alanda başarıyı engelleyen en önemli faktörlerden birinin tümör heterojenitesi olduğunun anlaşılmasıdır. Buna göre girişimsel biyopsiyle sınırlı sayıda örnek almak yerine tüm hacim içinde heterojeniteyi objektif verilerle girişim gerektirmeyecek bir şekilde değerlendirebilmek her hasta için o hastaya özel ve en etkin tedaviyi belirlemek ve prognozu öngörmek açısından önemli bir silahtır. Bu anlamda radyomiks, kişiselleştirilmiş tıp için ümit vaad etmektedir. Radiyomiks görüntü özelliklerinden büyük miktarda verinin çıkarılmasını ve bunların klinik veriler ya da genetik profillerle korelasyonu esasına dayanır. İş akışında temel adımlar (a) Tıbbı görüntülerin elde edilmesi (b) Ilgilenilen hacimleri (klinik olarak anlamlı değer içerebilecekleri) tanımlamak ve bölüklemek, (c) Bölüklenmiş hacimlerde özellikleri çıkarmak ve nitelemek, (d) Çıkartılan özellikleri veri tabanı doldurmak için kullanmak olarak sıralanabilir. Her bir basamakta hala sınırlılıklar ve uygulama zorlukları mevcuttur. Ancak her basamağı içine alan temel sorun standart veri girdisi olmamasıdır. Henüz yeterli olgunluğa ulaşmamış olan radyomiksin vaad ettiği potansiyel göz ardı edilmemeli çok merkezli ve geniş olgu sayıları içeren prospektif çalışmalarla günlük uygulamaya eklemenin yolları aranmalıdır.


Keywords: Radyomiks, yapay zeka, tıbbi görüntüleme, kişiselleştirilmiş tıp, veri madenciliği

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