The purpose of this study is the evaluation of the variation of performance in terms of sensitivity and specificity of two radiologists with different experience in mammography, with and without the assistance of two different CAD systems. The CAD considered are SecondLookTM (CADx Medical Systems, Canada), and CALMA (Computer Assisted Library in MAmmography). The first is a commercial system, the other is the result of a research project, supported by INFN (Istituto Nazionale di Fisica Nucleare, Italy); their characteristics have already been reported in literature. To compare the results with and without these tools, a dataset composed by 70 images of patients with cancer (biopsy proven) and 120 images of healthy breasts (with a three years follow up) has been collected. All the images have been digitized and analysed by two CAD, then two radiologists with respectively 6 and 2 years of experience in mammography indipendently made their diagnosis without and with, the support of the two CAD systems. In this work sensitivity and specificity variation, the Az area under the ROC curve, are reported. The results show that the use of a CAD allows for a substantial increment in sensitivity and a less pronounced decrement in specificity. The extent of these effects depends on the experience of the readers and is comparable for the two CAD considered.
Digital acquisition of mammographic images is becoming more diffuse in hospitals, as well as off line digitalization of analogical images to allow use of CAD, filing and statistical tools. Radiologists performance in reading digital images are strictly related to the quality of the images displayed on the monitor. We are investigating how different display devices, influence digital images reading.
To reach the goal we are using phantoms for quality controls in mammography. Three different monitors are considered. The first one is the high resolution CRT display used as diagnostic monitor for the GE digital mammograph. The others are a high quality monitor for personal computer and the monitor of a high quality notebook. The phantoms used are the CDMAM 3.2 (a contrast-detail phantom) and the RMI 156 (which contains test objects that represent malignancies and small breast structures). Their images were acquired by the digital mammograph and were then analyzed by two expert radiologists by observing them on the different display devices, adopting the same procedure. The results about the reading of the phantoms and the interpretation of the images with different monitors are presented here.
CALMA (Computer Assisted Library for Mammography), a collaboration among physicists and radiologists, has collected a large database of digitized mammographic images (about 5000) and developed a CAD (Computer Aided Detection) which can be used also for digitization, as archive and to perform statistical analysis. In this work we present the results obtained in the automatic search of microcalcification clusters. Images (18x24 cm2, digitized by a CCD linear scanner with a 85micrometers pitch and 4096 gray levels) are fully characterized: pathological ones have a consistent description with radiologist's diagnosis and histological data; non pathological ones correspond to patients with a follow up of at least three years. The automated microcalcification clusters analysis is made using a hybrid approach containing both algorithms and neural networks by which are extracted the ROIs (Region Of Interest). These ROIs are indicated on the images and a probability of containing a microcalcification cluster is associated to each ROI. The results obtained with this analysis are described in terms of the ROC (Receiver Operating Characteristic) curve, which shows the true positive fraction (sensitivity) as a function of the false positive fraction (1-specificity) obtained varying the threshold level of the ROI selection procedure.