Graduation Year


Document Type




Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Computer Science and Engineering

Major Professor

Dmitry Goldgof, Ph.D.

Co-Major Professor

Lawrence O. Hall, Ph.D.

Committee Member

Sudeep Sarkar, Ph.D.

Committee Member

Rangachar Kasturi, Ph.D.

Committee Member

Peter R. Mouton, Ph.D.

Committee Member

Tapas Das, Ph.D.


breast cancer, concordance correlation, ensemble classification, intra-tumor heterogeneity, kinetic maps


Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the breast is a widely used non-invasive approach to gather information about the underlying physiology of breast tumors. Recent studies indicate that breast tumor heterogeneity may reflect the presence of different levels of cellular aggressiveness or habitats within the tumor. This heterogeneity has been correlated to the variations in the contrast enhancement patterns within the tumor apparent on gadolinium-enhanced DCE-MRI. Although pathological and qualitative (based on contrast enhancement patterns) studies suggest the presence of clini- cal and molecular predictive tumor sub-regions, this has not been fully investigated in the quantitative domain.

The new era of cancer imaging emphasizes the use of Radiomics to provide in vivo quan- titative prognostic and predictive imaging biomarkers. Thus Radiomics focuses on apply- ing image analysis techniques to quantify tumor radiographic properties to create mineable databases from radiological images. In this research work, the Radiomics approach was ap- plied to develop a novel computer aided diagnosis (CAD) model for quantifying intratumor heterogeneity not only within the tumor as a whole, but also within tumor habitats with an intent to build predictive models in breast cancer. The process of building these predictive models started with 2-D tumor segmentation followed by habitat extraction (based on vari- ations in contrast patterns and geometry) and textural kinetic feature extraction to quantify habitat heterogeneity. A new correlation based random subspace ensemble framework was developed to evaluate the textural kinetics from the individual tumor habitats. This new

CAD framework was applied to predict two clinical and prognostic factors: Axillary lymph node (ALN) metastases and Estrogen receptor (ER) status. An AUC of more than 0.8 was achieved for classifying breast tumors based on number of ALN involvement. The highest AUC of 0.91 was achieved for classifying tumors with no ALN metastases from tumors with 4 or more ALN metastases. For classifying tumors based on ER status the highest AUC of 0.87 was achieved. These results were acquired by utilizing the textural kinetic features from the tumor habitat with rapid delayed washout. The results presented in this work showed that the heterogeneity within the tumor habitats which showed rapid contrast washout in the delayed phase, correlated with aggressive cellular phenotypes.

This work hypothesizes that successfully quantifying these prognostic factors will prove to be clinically significant as it can improve the diagnostic accuracy. This, in turn, will im- prove the breast cancer treatment paradigm by providing more tailored treatment regimens for aggressive tumors.