[Home ] [Archive]    
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: Volume 1, Issue 4 (3-2004) ::
Int J Radiat Res 2004, 1(4): 217-228 Back to browse issues page
Comparison of logistic regression and neural network models in predicting the outcome of biopsy in breast cancer from MRI findings
P. Abdolmaleki, M. Yarmohammadi, M. Gity
Abstract:   (16941 Views)

Background: We designed an algorithmic model based on the logistic regression analysis and a non-algorithmic model based on the Artificial Neural Network (ANN).

Materials and methods: The ability of these models was compared together in clinical application to differentiate malignant from benign breast tumors in a study group of 161 patients' records. Each patient’s record consisted of 6 subjective features extracted from MRI appearance. These findings were encoded as features for an ANN as well as a logistic regression model (LRM) to predict biopsy outcome. After both models had been trained perfectly on samples (n=100), the validation samples (n=61) were presented to the trained network as well as the established LRMs. Finally, the diagnostic performance of models were compared to that of the radiologist in terms of sensitiv­ity, specificity and accuracy, using receiver operating characteristic curve (ROC) analysis.

Results: The average output of the ANN yielded a perfect sensitivity (98%) and high accuracy (90%) similar to that one of an expert radiologist (96% and 92%) while specificity was smaller than that (67% verses 80%). The output of the LRM using significant features showed improvement in specificity from 60% for the LRM using all features to 93% for the reduced logistic regression model, keeping the accuracy around 90%.

Conclusion: Results show that ANN and LRM prove the relationship between extracted morphological features and biopsy results. Using statistically significant variables reduced LRM outperformed of ANN with remarkable specificity while keeping high sensitivity is achieved. Iran . J. Radiat. Res., 2004 1(4): 217-228

Keywords: neural networks, logistic regression model, ROC curves
Full-Text [PDF 130 kb]   (5774 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
Add your comments about this article
Your username or Email:


XML     Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

P. Abdolmaleki, M. Yarmohammadi, M. Gity. Comparison of logistic regression and neural network models in predicting the outcome of biopsy in breast cancer from MRI findings. Int J Radiat Res. 2004; 1 (4) :217-228
URL: http://ijrr.com/article-1-33-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 1, Issue 4 (3-2004) Back to browse issues page
International Journal of Radiation Research
Persian site map - English site map - Created in 0.04 seconds with 32 queries by YEKTAWEB 4419