Document Type: Research Articles
Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Colorectal Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Objectives: Radiologists face uncertainty in making decisions based on their judgment of breast cancer risk.
Artificial intelligence and machine learning techniques have been widely applied in detection/recognition of cancer.
This study aimed to establish a model to aid radiologists in breast cancer risk estimation. This incorporated imaging
methods and fine needle aspiration biopsy (FNAB) for cyto-pathological diagnosis. Methods: An artificial neural
network (ANN) technique was used on a retrospectively collected dataset including mammographic results, risk
factors, and clinical findings to accurately predict the probability of breast cancer in individual patients. Area under
the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive
values were used to evaluate discriminative performance. Result: The network incorporating the selected features
performed best (AUC = 0.955). Sensitivity and specificity of the ANN were respectively calculated as 0.82 and 0.90.
In addition, negative and positive predictive values were respectively computed as 0.90 and 0.80. Conclusion: ANN
has potential applications as a decision-support tool to help underperforming practitioners to improve the positive
predictive value of biopsy recommendations.