Breast parenchymal density is considered a strong indicator of breast cancer risk and therefore useful for preventive tasks. Measurement of breast density is often qualitative and requires the subjective judgment of radiologists. Here we explore an automatic breast composition classification workflow based on convolutional neural networks for feature extraction in combination with a support vector machines classifier. This is compared to the assessments of seven experienced radiologists. The experiments yielded an average kappa value of 0.58 when using the mode of the radiologists’ classifications as ground truth. Individual radiologist performance against this ground truth yielded kappa values between 0.56 and 0.79.
Data from GLOBOCAN show that around 4,000 Peruvian women are diagnosed with breast cancer every year. From these new cases, the clinical presentation of 36% corresponded to advanced stages (III and IV). Therefore, there is an urgent need to strengthen current screening and early detection strategies. The American College of Radiology (ACR) breast density classification is a risk assessment and quality assurance tool in mammography to standardize and facilitate report to non-radiologists. In our sample of Peruvian women, we found that 45.3% of women have a breast density classified as ACR II, 32.3% as ACR I, 19.7% as ACR III and only 2.7% as ACR IV. Also, premenopausal women are more likely to have breast density types ACR III and IV than postmenopausal women. These results show certain similarity to other populations showing that most breast densities are classified as ACR I and II, but shows a unique distribution when taking into account all four ACR types. Our results are consistent with epidemiological evidence suggesting that the Peruvian population may have a different stratification of risk based on its particular genetic and/or ethnic background. The present work will aid to develop novel strategies for screening and early detection of breast malignancies.
Breast parenchymal density is considered a strong indicator of cancer risk. However, measures of breast density are often qualitative and require the subjective judgment of radiologists. This work proposes a supervised algorithm to automatically assign a BI-RADS breast density score to a digital mammogram. The algorithm applies principal component analysis to the histograms of a training dataset of digital mammograms to create four different spaces, one for each BI-RADS category. Scoring is achieved by projecting the histogram of the image to be classified onto the four spaces and assigning it to the closest class. In order to validate the algorithm, a training set of 86 images and a separate testing database of 964 images were built. All mammograms were acquired in the craniocaudal view from female patients without any visible pathology. Eight experienced radiologists categorized the mammograms according to a BIRADS score and the mode of their evaluations was considered as ground truth. Results show better agreement between the algorithm and ground truth for the training set (kappa=0.74) than for the test set (kappa=0.44) which suggests the method may be used for BI-RADS classification but a better training is required.
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