Ductal in-situ carcinoma (DCIS) is a non-invasive proliferation that lacks the ability to metastasize. Over the past four decades, DCIS diagnoses have increased ten-fold, with treatments nearly as aggressive as those for small low-grade invasive breast cancer. In this study, we evaluate the potential of identifying intrinsic imaging phenotype of DCIS using radiomic signatures from breast DCE-MRI. The rationale is that such phenotypes may capture aspects of the heterogeneity of DCIS that can aid in identifying indolent from aggressive disease to better stratify patients for improved disease management. An initial analysis was performed on eighty- two DCIS cases from the ECOG-ACRIN E4112 trial. The Cancer Phenomics Toolkit (CapTK) was used to extract a total of 95 3-D radiomic features from each primary lesion volume in pre-treatment, pre-operative breast DCE-MRI images. Features were first filtered for robustness across the heterogeneous clinical sites of DCE-MRI acquisition and features deemed non-robust (59) were discarded. Dimensionality reduction was performed with the remaining thirty-six features via principle component analysis (PCA). Unsupervised hierarchical clustering of the resulting five principal components (PCs) capturing 85% of the original feature variance was applied. Two significant intrinsic DCIS radiomic phenotypes were identified (p<0.001). Our hypothesis is that DCIS imaging biomarkers could improve prognostic ability more reliably than biopsy alone. These findings will be further explored in the expanded analysis of ECOG-ACRIN E4112 trial.
Convolutional neural networks (CNN) are increasingly used for image classification tasks. In general, the architectures of these networks are set ad hoc with little justification for selecting various components, such as the number of layers, layer depth, and convolution settings. In this work, we develop a structured approach to explore and select architectures that provide optimal classification performance. This was developed with an IRB-approved data set with 9,216 2-D maximum intensity projection (MIP) MRI breast images, containing breast cancer malignant or benign classes. This data set was divided into 7,372 training, 922 validation, and 922 test images. The architecture search method employs a genetic algorithm to find optimal ResNet-based classification architectures through crossover and mutation. Each generation, new model architectures are created from the genetic algorithm and trained with supervised machine learning. This search method identifies the model with the highest area under the ROC curve (AUC). The genetic algorithm produced an optimal model architecture which classifies malignancy in images with 76% accuracy and achieves an AUC score of .83. This approach offers a rational framework for automatic architecture exploration, potentially leading to a set of more efficient and generalizable CNN-based classifiers.
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