Kanserli dokuların mikroskop görüntülerinde kanser kök hücre oranın otomatik olarak belirlenmesi ve klinikte kullanılacak yazılım geliştirilmesi

Download

TVRVM01EVTE.pdf

Date

2016

Author

Demir, Gündüz Çiğdem
Güzelcan, Akhan Ece
Arslan, Tunç Musa
Yorulmaz, Onur
Fayetörbay, Rümeysa
Erşahin, Tülin
Mohammadvand, Navid
Örsçelik, Gökçe Simge
Üner, Ayşegül
Çetin, Enis Ahmet
Oğuz, Oğuzhan
Doğan, Deniz
Koyuncu, Fahrettin Can
Badawı, Diaa
Atalay, Rengül

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

381
views

865
downloads

Kanser anormal hücrelerin kontrolsüz çoğalması ve yayılması olarak tanımlanan karmaşık bir hastalıktır. Bu nedenle kanser dokusu farklı özellikleri olan-ek popülasyon denilen-hücre gruplarını içerir. Bu hücrelerden olan kanser kök hücreleri normal kök hücreleri gibi kendini yenileme ve farklılaşma özelliklerini taşırken normal kök hücrelerinin aksine homeostatik kontrolleri olmayan, yani farklılaşma özelliklerini dengeleyip, çevresel sinyallere göre programlama özellikleri olmayan hücrelerdir. Bu özellikleri dolayısıyla da son yıllarda kanser kök hücre popülasyonu kanserin kötü prognozundan sorumlu hücreler olarak tanımlanmaktadırlar. Bu bağlamda kanser tedavisinde kanser kök hücre oran bilgisi, hastaya verilecek tedavinin planlanması amacıyla kullanılabilir. Hekimler kanserli dokunun kanser kök hücre popülasyon onarına göre henüz kanser metastaz yapmamış bile olsa bireye özgü tedavi yaklaşımı izlenebilir. Gerçekleştirilen bu proje kapsamında, kanserli parafin bloklara gömülü doku biyopsi örneklerinde, kanser kök hücre tanımlama öngörü aracını geliştirdik. Kovaryans matrisine dayalı sınıflandırıcılara ve Ana Bileşenler Analizi (PCA) algoritması sonucunda üç sınıf H&E boyanmış karaciğer kanseri dokularının sınıflandırma probleminde 76.0% imge sınıflandırma başarısı elde edilmiştir. Ana Bileşenler Analizi kanser hücresi belirleme probleminde %90’nın üstünde başarı ve kovaryans matrisine dayalı sınıflandırıcılar ile üç sınıf ayrıştırma probleminde %90’a yakın başarı sağlanmıştır. Kanserli hastalarda bireye ve hedefe yönelik tedavi yol haritasının belirlenmesine destek olabilecek ve hastalığın tedavisinde ve prognozunda iyileştirme sağlayabilecek yazılımı hekimlerin kullanıma sunduk. CANSTEM yazılıma http://users.metu.edu.tr/rengul/canstem.html adresinden ulaşılabilir.

Subject Keywords

Kanser kök hücre, Makine ile öğrenme, İmmunohistokimya, Tedavi, Prognoz

URI

https://app.trdizin.gov.tr/publication/project/detail/TVRVM01EVTE
https://hdl.handle.net/11511/49402

Collections

Graduate School of Informatics, Project and Design

Suggestions

OpenMETU
Core

Effects of novel compound derived from vitamin e on prostate cancer cell line Ergen, Nuri; Severcan, Feride; Department of Biology (2016) Cancer is a complex disease characterized by uncontrolled cell proliferation, invasion and metastasis. It is known that vitamins having high antioxidant capacity, like vitamin A, C and E, play important roles in adjuvant therapy and as cancer protective agents. Vitamin E is a group of fat soluble antioxidant nutrients consisting of tocopherols and tocotrienols. Various research has been performed on the utility of vitamin E derivatives on cancer treatment. Energy metabolism of cells alters during cancer pro...
Analysis of 3'UTR shortening events in breast cancer Baloğlu, Onur; Can, Tolga; Department of Bioinformatics (2013) Cancer is the collective term used to describe a diverse group of diseases that share certain hallmarks, which in turn enables the affected cells to sustain an uncontrolled cell growth. Despite the increasing efforts and advances in cancer therapies, cancers are still responsible for approximately 10% of all the deaths worldwide. Furthermore, the increase in the average human lifespan will further contribute to the cancer incidences. This brings the necessity to focus our efforts on early detection and effe...
Analyses and modeling of ovarian cancer microarray data Karakelle, Barış S; Purutçuoğlu Gazi, Vilda; Department of Biomedical Engineering (2019) Ovarian cancer is one of the common cancer types among other oncological diseases. The major causes of this cancer can be listed as age, obesity, hormone therapy, material inheritance and contraceptive pills. Due to its generality and importance, many researches have been conducted from distinct labs about this illness and its plausible causes have been intensively investigated either inmicroarray studies, where just part of the related genes are detected, or in thepairwise correlation analyses between the ...
Cell-surface interactions in a breast cancer model Antmen, Ezgi; Hasırcı, Vasıf Nejat; Demirci, Utkan; Department of Biotechnology (2017) Breast cancer, is one of the most commonly diagnosed cancers, has a high mortality rate. One in every eight women (12.3%) develops breast cancer at some stage of their lives and this is the cause of about 15% of cancer deaths in women and 3% of total deaths. It is therefore important to study the behavior of breast cancer cells. Measurement of the mechanical properties of cancer cells leads to new insights such as that cancer cells are softer than healthy cells. Also, metastatic cancer cells were found to b...
Model comparison for gynecological cancer datasets and selection of threshold value Bahçivancı, Başak; Purutçuoğlu Gazi, Vilda; Department of Statistics (2019) Cancer is a very common system’s disease with its structural and functional complexities caused by high dimension and serious correlation of genes as well as sparsity of gene interactions. Hereby, different mathematical models have been suggested in the literature to unravel these challenges. Among many alternates, in this study we use the Gaussian graphical model, Gaussian copula graphical model and loop-based multivariate adaptive regression splines with/without interaction models due to their advantages ...

Citation Formats

G. Ç. Demir et al., “Kanserli dokuların mikroskop görüntülerinde kanser kök hücre oranın otomatik olarak belirlenmesi ve klinikte kullanılacak yazılım geliştirilmesi,” 2016. Accessed: 00, 2020. [Online]. Available: https://app.trdizin.gov.tr/publication/project/detail/TVRVM01EVTE.