This study demonstrates that a variant of a Siamese neural network architecture is more effective at classifying highdimensional radiomic features (extracted from T2 MRI images) than traditional models, such as a Support Vector Machine or Discriminant Analysis. Ninety-nine female patients, between the ages of 20 and 48, were imaged with T2 MRI. Using biopsy pathology, the patients were separated into two groups: those with breast cancer (N=55) and those with GLM (N=44). Lesions were segmented by a trained radiologist and the ROIs were used for radiomic feature extraction. The radiomic features include 536 published features from Aerts et al., along with 20 features recurrent quantification analysis features. A Student T-Test was used to select features found to be statistically significant between the two patient groups. These features were then used to train a Siamese neural network. The label given to test features was the label of whichever class the test features with the highest percentile similarity within the training group. Within the two highest-dimensional feature sets, the Siamese network produced an AUC of 0.853 and 0.894, respectively. This is compared to best non-Siamese model, Discriminant Analysis, which produced an AUC of 0.823 and 0.836 for the two respective feature sets. However, when it came to the lower-dimensional recurrent features and the top-20 most significant features from Aerts et al., the Siamese network performed on-par or worse than the competing models. The proposed Siamese neural network architecture can outperform competing other models in high-dimensional, low-sample size spaces with regards to tabular data.
Breast cancer in young women is commonly aggressive, in part because the proportion of high-grade, triple-negative (TN) tumor is too high. There are certain limitations in the detection of biopsies or surgical specimens which only select part of tumor sample tissue and ignore the possible heterogeneity of tumors. In clinical practice, MRI is used for the diagnosis of breast cancer. MRI-based radiomics is a developing approach that may provide not only the diagnostic value for breast cancer but also the predictive or prognostic associations between the images and biological characteristics. In this work, we used radiomics methods to analyze MR images of breast cancer in 53 young women, and correlated the radiomics data with molecular subtypes. The results indicated a significant difference between TN type and non-TN type of breast cancer in young women on the radiomics features based on T2-weighted MR images. This may be helpful for the identification of TN type and guiding the therapeutic strategies.
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