%0 Journal Article %T Assessing Breast Cancer Risk with an Artificial Neural Network %J Asian Pacific Journal of Cancer Prevention %I West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter. %Z 1513-7368 %A Sepandi, Mojtaba %A Taghdir, Maryam %A Rezaianzadeh, Abbas %A Rahimikazerooni, Salar %D 2018 %\ 04/01/2018 %V 19 %N 4 %P 1017-1019 %! Assessing Breast Cancer Risk with an Artificial Neural Network %K breast cancer %K Artificial Neural Network %K Risk Assessment %R 10.22034/APJCP.2018.19.4.1017 %X 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 imagingmethods and fine needle aspiration biopsy (FNAB) for cyto-pathological diagnosis. Methods: An artificial neuralnetwork (ANN) technique was used on a retrospectively collected dataset including mammographic results, riskfactors, and clinical findings to accurately predict the probability of breast cancer in individual patients. Area underthe receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictivevalues were used to evaluate discriminative performance. Result: The network incorporating the selected featuresperformed 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: ANNhas potential applications as a decision-support tool to help underperforming practitioners to improve the positivepredictive value of biopsy recommendations. %U https://journal.waocp.org/article_57859_38a35ab9ea6a97db2efc9a184e7c5d34.pdf