Incorporating Breast Asymmetry Studies into CADx Systems

Breast cancer is one of the global leading causes of death among women, and an early detection is of uttermost importance to reduce mortality rates. Screening mammograms, in which radiologists rely only on their eyesight, are one of the most used early detection methods. However, characteristics, such as the asymmetry between breasts, a feature that could be very difficult to visually quantize, is key to breast cancer detection. Due to the highly heterogeneous and deformable structure of the breast itself, incorporating asymmetry measurements into an automated detection system is still a challenge. In this study, we proposed the use of a bilateral registration algorithm as an effective way to automatically measure mirror asymmetry. Furthermore, this information was fed to a machine learning algorithm to improve the accuracy of the model. In this study, 449 subjects (197 with calcifications, 207 with masses, and 45 healthy subjects) from a public database were used to train and evaluate the proposed methodology. Using this procedure, we were able to independently identify subjects with calcifications (accuracy = 0.825, AUC = 0.882) and masses (accuracy = 0.698, AUC = 0.807) from healthy subjects

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

<div><p>In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.</p></div

Characteristics of the model obtained for PAM50.

<p>(A) A heat map representation of the features associated to risk from PAM50 ROR. The figure shows the features selected by LASSO (vertical axis) and their univariate Spearman coefficient and rank along samples (horizontal axis) ordered by the PAM50 ROR score. The top of the figure includes common clinical indicators. The image data was scaled to z-score to nightlight differences. Orange dots at the right represent features also present in the OncotypeDX model. (B) Comparison of the estimated PAM50 recurrence score with that of the score predicted by the image model in (A). Each dot represents a sample. Colors represent subtypes and filled or open circles represent younger or older patients.</p

Radiogenomics pipeline used in the analysis of association between imaging features and gene signatures in patients with breast cancer.

<p>First, mammograms and tumor biopsy samples were acquired before surgery or treatment. A trained radiologist delimited the lesion region of interest to calculate Image features. For tumor biopsy samples, RNA was extracted and gene expression was measured using microarray technology, then the PAM50 molecular subtype and OncotypeDX recurrence score were measured. Univariate association based on correlation was used to show that image features are associated to signatures. Multivariate analysis was used to fit predictive models using cross-validation strategies and a feature selection algorithm. A similar procedure was used for contralateral images to evaluate whether the associations were tumor-specific.</p

Summary of the 539 features obtained from each region of the mammogram.

<p>Summary of the 539 features obtained from each region of the mammogram.</p

Summary of patients per risk group.

<p>Summary of patients per risk group.</p

Distribution of test correlations in the cross-validation multivariate feature selection.

<p>White boxplots correspond to tumor ROIs whereas grayed boxplots correspond to contralateral ROIs used as controls. Note that contralateral distributions of test predictions are lower than corresponding tumor test predictions.</p