27 February 2018 Detecting mammographically occult cancer in women with dense breasts using Radon Cumulative Distribution Transform: a preliminary analysis
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Abstract
We propose using novel imaging biomarkers for detecting mammographically-occult (MO) cancer in women with dense breast tissue. MO cancer indicates visually occluded, or very subtle, cancer that radiologists fail to recognize as a sign of cancer. We used the Radon Cumulative Distribution Transform (RCDT) as a novel image transformation to project the difference between left and right mammograms into a space, increasing the detectability of occult cancer. We used a dataset of 617 screening full-field digital mammograms (FFDMs) of 238 women with dense breast tissue. Among 238 women, 173 were normal with 2 – 4 consecutive screening mammograms, 552 normal mammograms in total, and the remaining 65 women had an MO cancer with a negative screening mammogram. We used Principal Component Analysis (PCA) to find representative patterns in normal mammograms in the RCDT space. We projected all mammograms to the space constructed by the first 30 eigenvectors of the RCDT of normal cases. Under 10-fold crossvalidation, we conducted quantitative feature analysis to classify normal mammograms and mammograms with MO cancer. We used receiver operating characteristic (ROC) analysis to evaluate the classifier’s output using the area under the ROC curve (AUC) as the figure of merit. Four eigenvectors were selected via a feature selection method. The mean and standard deviation of the AUC of the trained classifier on the test set were 0.74 and 0.08, respectively. In conclusion, we utilized imaging biomarkers to highlight differences between left and right mammograms to detect MO cancer using novel imaging transformation.
Conference Presentation
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Juhun Lee, Juhun Lee, Robert M. Nishikawa, Robert M. Nishikawa, Gustavo K. Rohde, Gustavo K. Rohde, } "Detecting mammographically occult cancer in women with dense breasts using Radon Cumulative Distribution Transform: a preliminary analysis", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057508 (27 February 2018); doi: 10.1117/12.2293541; https://doi.org/10.1117/12.2293541
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