Bladder Cancer Grading from H&E Stained Samples from TCGA Data

Year 2023, Volume: 11 Issue: 2, 549 - 560, 23.06.2023

Ali Değirmenci , İlyas Çankaya , Berrak Gümüşkaya Öcal , Ömer Karal

https://doi.org/10.29109/gujsc.1232028

Cited By: 1

Abstract

Early diagnosis of bladder cancer (BC) is of great importance for the treatment and course of the disease. The most effective method for diagnosis is the examination of the tissue sample, on which various procedures are applied, by the pathologist under a microscope. However, this approach is subjective and may vary depending on the knowledge and experience of the pathologists. To increase objectivity and assist the pathologist, this study presents automated bladder urothelial carcinoma grading from whole slide images (WSI). Performance comparisons are made using 3 different machine learning methods such as naive Bayes, k nearest neighbor and decision tree. Experimental results show that the decision tree method achieves the highest performance with 82% and can be used to assist the pathologist during diagnosis.

Keywords

Bladder cancer, histopathology, machine learning, whole slide image (WSI), computer aided diagnosis (CAD)

TCGA Verilerinden H&E ile Boyanmış Örneklerden Mesane Kanseri Derecelendirmesi

Abstract

Mesane kanserinin (BC) erken teşhisi, hastalığın tedavisi ve seyri için büyük önem taşımaktadır. Teşhis için en etkili yöntem, çeşitli işlemlerin uygulandığı doku örneğinin patolog tarafından mikroskop altında incelenmesidir. Ancak bu yaklaşım subjektiftir ve patologların bilgi ve tecrübesine bağlı olarak değişebilir. Objektifliği artırmak ve patoloğa yardımcı olmak için bu çalışma, tam slayt görüntülerinden (WSI) otomatik mesane ürotelyal karsinom derecelendirmesini sunar. Naive Bayes, k en yakın komşu ve karar ağacı gibi 3 farklı makine öğrenme yöntemi kullanılarak performans karşılaştırması yapılır. Deneysel sonuçlar, karar ağacı yönteminin %82 ile en yüksek performansı elde ettiğini ve tanı sırasında patoloğa yardımcı olmak için kullanılabileceğini göstermektedir.

Keywords

Mesane kanseri, histopatoloji, makine öğrenimi, tam slayt görüntüsü (TSG), bilgisayar destekli teşhis (BDT)

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