Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network

Purpose: It is estimated that 7% of women in the western world will develop palpable breast cysts in their lifetime. Even though cysts have been correlated with risk of developing breast cancer, many of them are benign and do not require follow-up. We develop a method to discriminate benign solitary cysts from malignant masses in digital mammography. We think a system like this can have merit in the clinic as a decision aid or complementary to specialized modalities. Methods: We employ a deep convolutional neural network (CNN) to classify cyst and mass patches. Deep CNNs have been shown to be powerful classifiers, but need a large amount of training data for which medical problems are often difficult to come by. The key contribution of this paper is that we show good performance can be obtained on a small dataset by pretraining the network on a large dataset of a related task. We subsequently investigate the following: (a) when a mammographic exam is performed, two different views of the same breast are recorded. We investigate the merit of combining the output of the classifier from these two views. (b) We evaluate the importance of the resolution of the patches fed to the network. (c) A method dubbed tissue augmentation is subsequently employed, where we extract normal tissue from normal patches and superimpose this onto the actual samples aiming for a classifier invariant to occluding tissue. (d) We combine the representation extracted using the deep CNN with our previously developed features. Results: We show that using the proposed deep learning method, an area under the ROC curve (AUC) value of 0.80 can be obtained on a set of benign solitary cysts and malignant mass findings recalled in screening. We find that it works significantly better than our previously developed approach by comparing the AUC of the ROC using bootstrapping. By combining views, the results can be further improved, though this difference was not found to be significant. We find no significant difference between using a resolution of 100 versus 200 micron. The proposed tissue augmentations give a small improvement in performance, but this improvement was also not found to be significant. The final system obtained an AUC of 0.80 with 95% confidence interval [0.78, 0.83], calculated using bootstrapping. The system works best for lesions larger than 27 mm where it obtains an AUC value of 0.87. Conclusion: We have presented a computer-aided diagnosis (CADx) method to discriminate cysts from solid lesion in mammography using features from a deep CNN trained on a large set of mass candidates, obtaining an AUC of 0.80 on a set of diagnostic exams recalled from screening. We believe the system shows great potential and comes close to the performance of recently developed spectral mammography. We think the system can be further improved when more data and computational power becomes available. c 2017 American Association of Physicists in Medicin

Effecten van het bevolkingsonderzoek naar borstkanker

Annually, in the Netherlands around 900,000 women between the ages of 50-75 years undergo mammography as part of a population screening into breast cancer. In this way more than 5000 cases of breast cancer are detected (0.6% of women screened); 70% of these malignancies are < stage II, which is prognostically favourable. Due to the early detection and treatment of breast cancer, the breast cancer death risk in those women who participate in the population screening is half that of women who choose not to be screened. The downside of the population screening is that participants are relatively often referred to a hospital for a diagnostic work-up (around 2%), and 70% of them are ultimately found not to have cancer. The positive predictive value of the population screening is 30%. Early discovery also leads to over-diagnosis in patients with breast cancer that without screening would never have manifested itself. Based on computer simulations it has been estimated that in the Netherlands over-diagnosis occurs in 9% of patients in whom breast cancer is detected during a population screenin

Standalone computer-aided detection compared to radiologists' performance for the detection of mammographic masses

Item does not contain fulltextOBJECTIVES: We developed a computer-aided detection (CAD) system aimed at decision support for detection of malignant masses and architectural distortions in mammograms. The effect of this system on radiologists' performance depends strongly on its standalone performance. The purpose of this study was to compare the standalone performance of this CAD system to that of radiologists. METHODS: In a retrospective study, nine certified screening radiologists and three residents read 200 digital screening mammograms without the use of CAD. Performances of the individual readers and of CAD were computed as the true-positive fraction (TPF) at a false-positive fraction of 0.05 and 0.2. Differences were analysed using an independent one-sample t-test. RESULTS: At a false-positive fraction of 0.05, the performance of CAD (TPF?=?0.487) was similar to that of the certified screening radiologists (TPF?=?0.518, P?=?0.17). At a false-positive fraction of 0.2, CAD performance (TPF?=?0.620) was significantly lower than the radiologist performance (TPF?=?0.736, P <0.001). Compared to the residents, CAD performance was similar for all false-positive fractions. CONCLUSIONS: The sensitivity of CAD at a high specificity was comparable to that of human readers. These results show potential for CAD to be used as an independent reader in breast cancer screening. KEY POINTS : � Computer-aided detection (CAD) systems are used to detect malignant masses in mammograms � Current CAD systems operate at low specificity to avoid perceptual oversight � A CAD system has been developed that operates at high specificity � The performance of the CAD system is approaching that of trained radiologists � CAD has the potential to be an independent reader in screening