title: A use of a neural network to evaluate contrast enhancement curves in breast magnetic resonance images.
abstract: For the diagnosis of breast cancer using magnetic resonance imaging (MRI), one of the most important parameters is the analysis of contrast enhancement. A three-dimensional MR sequence is applied before and five times after bolus injection of paramagnetic contrast medium (Gd-DTPA). The dynamics of absorption are described by a time/intensity enhancement curve, which reports the mean intensity of the MR signal in a small region of interest (ROI) for about 8 minutes after contrast injection. The aim of our study was to use an artificial neural network to automatically classify the enhancement curves as "benign" or "malignant." We used a classic feed-forward back-propagation neural network, with three layers: five input nodes, two hidden nodes, and one output node. The network has been trained with 26 pathologic curves (10 invasive carcinoma [K], two carcinoma-in-situ [DCIS], and 14 benign lesion [B]). The trained network has been tested with 58 curves (36 K, one DCIS, 21 B). The network was able to correctly identify the test curves with a sensitivity of 76% and a specificity of 90%. For comparison, the same set of curves was analyzed separately by two radiologists (a breast MR expert and a resident radiologist). The first correctly interpreted the curves with a sensitivity of 76% and a specificity of 90%, while the second scored 59% for sensitivity and 90% for specificity. These results demonstrate that a trained neural network recognizes the pathologic curves at least as well as an expert radiologist. This algorithm can help the radiologist attain rapid and affordable screening of a large number of ROIs. A complete automatic computer-aided diagnosis support system should find a number of potentially interesting ROIs and automatically analyze the enhancement curves for each ROI by neural networks, reporting to the radiologist only the potentially pathologic ROIs for a more accurate, manual, repeated evaluation.
authors.affiliation: Department of Images for Diagnosis and Therapy, National Cancer Institute, Milan, Italy. Vergnaghi@istitutotumori.mi.it
authors.forename: D
authors.initials: D
authors.lastname: Vergnaghi
authors.forename: A
authors.initials: A
authors.lastname: Monti
authors.forename: E
authors.initials: E
authors.lastname: Setti
authors.forename: R
authors.initials: R
authors.lastname: Musumeci
mesh_terms: D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis*; D003287:Contrast Media; D005260:Female; D019786:Gadolinium DTPA; D006801:Humans; D008279:Magnetic Resonance Imaging; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA