A Review About Radiomics with Artificial Intelligence Methods in Breast Cancer Diagnosis and Prognosis

Abstract

According to the 2020 World Health Organization report on cancer disease, breast cancer is one of the most frequent cancer types. Early diagnosis and treatment are important for higher survival rates and better quality of life. In the diagnosis of breast cancer, radiomics is a new and popular research topic. Radiomics works on the numerical features of images obtained by radiographic imaging modalities such as magnetic resonance imaging (MRI), mammography, ultrasound (US) or positron emission tomography / computed tomography (PET / CT). Radiogenomics tries to associate the radiomics data with genomics data which may improve diagnosis, prognosis and prediction of cancer. Multidimensional large datasets are obtained by extracting large amount of candidative features from the radiographic images. Feature selection is an important task to remove non-valuable features to improve classification accuracy. Using feature selection methods, most valuable features are obtained and classified by using machine learning or deep learning methods to diagnose and prognose cancer. Recently many researchers have studied to develop radiomics models using quantitative “-omics” data with the rapid development of the use of artificial intelligence methods in the field of medicine. This review aims to provide a literature survey of radiomics and radiogenomics models with machine learning and deep learning methods in breast cancer diagnosis and prognosis. We review research papers published between 2012 and 2020 and represent the radiographic imaging modalities, radiomic feature extraction methods, radiomic feature selection methods and classification methods using machine learning and deep learning algortihms. Finally, we discuss the challenges and propose some future research directions of breast cancer radiomics models. 

Keywords

• Breast Cancer
• Deep Learning
• Machine Learning
• Radiogenomics
• Radiomics

Meme Kanseri Teşhis ve Prognozunda Radiomics ile Yapay Zeka Yöntemleri Kullanımı Hakkında Bir İnceleme

Öz

Dünya Sağlık Örgütünün 2020 kanser hastalığı raporuna göre meme kanseri en sık görülen kanser türlerinden biridir. Erken teşhis ve tedavi daha yüksek yaşam şansı ve daha iyi bir yaşam kalitesi için önemlidir. Radiomics, meme kanseri tanısında ve prognozunda yeni ve popüler bir araştırma alanıdır. Manyetik rezonans görüntüleme (MR), mamografi, ultrason (US) ve pozitron emüsyon tomografi / bilgisayarlı tomografi (PET / CT) gibi radyografik görüntüleme yöntemleri ile elde edilen görüntülerin sayısal özellikleri üzerinde çalışır. Radiogenomics ise radiomics veriyi kanserin teşhisi, prognozu ve tahmini destekleyecek çok sayıda genomics veri ile ilişkilendirmeye çalışır. Elde edilen radyografik görüntülerden büyük miktarda kandidatif veri çıkarılarak çok boyutlu büyük veri setleri elde edilir. Nitelik seçimi sınıflandırma doğruluğunu artırmak için değersiz nitelikleri veri setinden çıkaran önemli bir işlevdir. Nitelik seçim yöntemleri ile ayrılan değerli nitelikler makine öğrenme ya da derin öğrenme yöntemleri ile kanserin yakalanması, teşhisi, prognozun değerlendirilmesi amacıyla sınıflandırılır. Son yıllarda birçok araştırmacı yapay zeka yöntemlerinin tıp alanında kullanımının hızla gelişmesiyle bu kantitatif -omics verileri ile radiomics modeller geliştirmek amacıyla çok sayıda çalışma yapmışlardır. Bu inceleme, meme kanseri tanı ve prognozunda makine öğrenme ve derin öğrenme yöntemleri ile kullanılan radiomics ve radiogenomics modelleri hakkında literatür taraması yapmayı amaçlamaktadır. 2012 ile 2020 arasındaki araştırma makaleleri incelenmiş ve radyografik görüntüleme yöntemleri, radiomic nitelik çıkarma yöntemleri, nitelik seçme yöntemleri ve makine öğrenme ve derin öğrenme algoritmalarını kullanarak sınıflandırma yöntemleri sunulmuştur. Son olarak, meme kanseri radiomics modellerinin zorluklarını tartışıyor ve geleceteki bazı araştırma konularını öneriyoruz.

Anahtar Kelimeler

• Derin Öğrenme
• Makine Öğrenme
• Meme kanseri
• Radiogenomics
• Radiomics

Kaynakça

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