An automated prognosis system for estrogen hormone status assessment in breast cancer tissue samples

Fatih SARIKOÇ; Adem KALINLI; Hülya AKGÜN; Figen ÖZTÜRK

Makale detayları

An automated prognosis system for estrogen hormone status assessment in breast cancer tissue samples

Yazarlar:

Fatih SARIKOÇ | Adem KALINLI | Hülya AKGÜN | Figen ÖZTÜRK

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 4, pp.1199-1221

Belge Tipi:

Makale

Yayın Tarihi:

3/2013

Belge Dili:

Türkçe

Document ID (Org No.):

2000743283

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Estrogen receptor (ER) status evaluation is a widely applied  method in the prognosis of breast cancer.  However, testing for the  existence of the ER biomarker in a patient's tumor sample mainly  depends on the subjective decisions of the doctors.  The aim of this  paper is to introduce the usage of a machine learning tool, functional  trees (FTs), to attain an ER prognosis of the disease via an objective  decision model.  For this aim, 27 image files, each of which came from  a biopsy sample of an invasive ductal carcinoma patient, were scanned  and captured by a light microscope.  From these images, 5150 nuclei  were segmented with image processing methods.  Several attributes,  including statistical, wavelet, cooccurrence matrix, and Laws' texture  features, were calculated inside the border area of each nucleus.  A  FT was trained over the feature dataset using a 10-fold  cross-validation and then the obtained model was tested on a separate  dataset.  The assessment results of the model were compared with those  of 2 experts.  Consequently, the weighted kappa coefficient indicated  a very good agreement (k= 0.899 and k= 0.927, P < 0.001) and  the Spearman's rank order correlation showed a high level of  correlation (r = 0.963 and r= 0.943, P < 0.001) between the  results of the FT and those of the observers.  The Wilcoxon test  revealed that there was no significant difference between the results  of the experts and the model (P = 0.051 and P = 0.316).  Finally, it  was concluded from the results that the FT could be used as a tool to  support the decision of doctors by indicating consistent outputs and  hence contribute to the objectiveness and reproducibility of the  assessment results.

 

Estrogen receptor (ER) status evaluation is a widely applied  method in the prognosis of breast cancer.  However, testing for the  existence of the ER biomarker in a patient's tumor sample mainly  depends on the subjective decisions of the doctors.  The aim of this  paper is to introduce the usage of a machine learning tool, functional  trees (FTs), to attain an ER prognosis of the disease via an objective  decision model.  For this aim, 27 image files, each of which came from  a biopsy sample of an invasive ductal carcinoma patient, were scanned  and captured by a light microscope.  From these images, 5150 nuclei  were segmented with image processing methods.  Several attributes,  including statistical, wavelet, cooccurrence matrix, and Laws' texture  features, were calculated inside the border area of each nucleus.  A  FT was trained over the feature dataset using a 10-fold  cross-validation and then the obtained model was tested on a separate  dataset.  The assessment results of the model were compared with those  of 2 experts.  Consequently, the weighted kappa coefficient indicated  a very good agreement (k= 0.899 and k= 0.927, P < 0.001) and  the Spearman's rank order correlation showed a high level of  correlation (r = 0.963 and r= 0.943, P < 0.001) between the  results of the FT and those of the observers.  The Wilcoxon test  revealed that there was no significant difference between the results  of the experts and the model (P = 0.051 and P = 0.316).  Finally, it  was concluded from the results that the FT could be used as a tool to  support the decision of doctors by indicating consistent outputs and  hence contribute to the objectiveness and reproducibility of the  assessment results.

Makale detayları

An automated prognosis system for estrogen hormone status assessment in breast cancer tissue samples

Yazarlar: Fatih SARIKOÇ | Adem KALINLI | Hülya AKGÜN | Figen ÖZTÜRK

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 4, pp.1199-1221

Belge Tipi: Makale

Yayın Tarihi: 3/2013

Belge Dili: Türkçe

Document ID (Org No.): 2000743283

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Fatih SARIKOÇ | Adem KALINLI | Hülya AKGÜN | Figen ÖZTÜRK

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 4, pp.1199-1221

Belge Tipi:

Makale

Yayın Tarihi:

3/2013

Belge Dili:

Türkçe

Document ID (Org No.):

2000743283

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632