BACKGROUND: An artificial neural network (ANN) was developed to predict breast cancer from mammographic findings. This network was evaluated in a retrospective study. METHODS: For a set of patients who were scheduled for biopsy, radiologists interpreted the mammograms and provided data on eight mammographic findings as part of the standard mammographic workup. These findings were encoded as features for an ANN. Results of biopsies were taken as truth in the diagnosis of malignancy. The ANN was trained and evaluated using a jackknife sampling on a set of 260 patient records. Performance of the network was evaluated in terms of sensitivity and specificity over a range of decision thresholds and was expressed as a receiver operating characteristic curve. RESULTS: The ANN performed more accurately than the radiologists (P < 0.08) with a relative sensitivity of 1.0 and specificity of 0.59. CONCLUSIONS: An ANN can be trained to predict malignancy from mammographic findings with a high degree of accuracy.
BACKGROUND: An artificial neural network (ANN) was developed to predict breast cancer from mammographic findings. This network was evaluated in a retrospective study. METHODS: For a set of patients who were scheduled for biopsy, radiologists interpreted the mammograms and provided data on eight mammographic findings as part of the standard mammographic workup. These findings were encoded as features for an ANN. Results of biopsies were taken as truth in the diagnosis of malignancy. The ANN was trained and evaluated using a jackknife sampling on a set of 260 patient records. Performance of the network was evaluated in terms of sensitivity and specificity over a range of decision thresholds and was expressed as a receiver operating characteristic curve. RESULTS: The ANN performed more accurately than the radiologists (P < 0.08) with a relative sensitivity of 1.0 and specificity of 0.59. CONCLUSIONS: An ANN can be trained to predict malignancy from mammographic findings with a high degree of accuracy.
Authors: Walker H Land; John J Heine; Tom Raway; Alda Mizaku; Nataliya Kovalchuk; Jack Y Yang; Mary Qu Yang Journal: Int J Funct Inform Personal Med Date: 2008-01
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Authors: Turgay Ayer; Oguzhan Alagoz; Jagpreet Chhatwal; Jude W Shavlik; Charles E Kahn; Elizabeth S Burnside Journal: Cancer Date: 2010-07-15 Impact factor: 6.860