Radonc/BME Medical Image and
Computational Analysis Laboratory
Imaging for metastatic breast cancer early detection.
Investigators: W. O'Dell, C. Takita, K. Daily, C. Heldermon, J. Lightsey.

Novel applications of conformal stereotactic radiation therapy championed by our clinical colleagues have renewed the clinical motivation for early detection of metastatic cancer to lung, liver, brain and other critical organs. This is especially relevant for patients who by their advanced stage of disease at initial diagnosis are at the greatest risk for developing metastases within the next 2-3 years. We have 2 active surveillance imaging studies for high-risk breast cancer survivors. The first study is funded by the Ocala Royal Dames for Cancer Research, and details of the study can be found here (ORD protocol link).

The second study opened in April 2016 and is funded through the Florida Academic Cancer Center Alliance (FACCA). A summary of the project on the FACCA site is here (research summary), and details of the clinical protocol can be found here (FACCA protocol link).

We have developed a 3D template matching approach to detect and size small metastatic tumors that is objective and more accurate than expert humans and existing commercial CAD systems. The details of the tumor detection and sizing are below.

Figure 1
[A] CT image slice through the chest of a patient with 5 lung mets, 2 of which are apparent at this slice location (arrows).
[B] Schematic of one of our 3D nodule appearance models (aka 'templates'). The variable intensities are at pixel ar due to partial volume effects at this image resolution, slice thickness, and the dimensions of the model. [C] Map of the correlation values of our 3D templates to the image features in this 3D CT dataset, but shown only on this slice. The 2 brightest pxiels correspond to the 2 real tumors.

Figure 2. Representative T1-weighted MR images of patients with mets to the brain.

Future Work
The studies listed above will uncover new information on when and where breast cancer metastases appear and how fast they grow in other organs in the body. Our long-term goal is to use this information to design the optimal surveillance imaging protocol to incorporate into a nationwide clinical trial to test the efficacy of proactive imaging to improve the survival and health outcomes of breast cancer patients at elevated risk for developing future metastases.

Related Publications
  1. O'Dell WG
    Automatic segmentation of tumor-laden lung volumes from the LIDC database
    Proceedings of SPIE Medical Imaging, San Diego CA, Feb. 2012
  2. Ambrosini R, Wang P, O'Dell WG
    Computer-Aided Detection of Metastatic Brain Tumors Using Automated 3-D Template Matching
    Journal of Magnetic Resonance Imaging, (January) 31(1):85-93, 2010
  3. Wang P., DeNunzio A., Okunieff P., O'Dell W.G.
    Metastatic lung tumor early detection using 3D template matching
    Medical Physics, (March) 34(3):915-922, 2007
  4. Ambrosini R, O'Dell WG
    Realistic simulated lung nodule dataset for testing CAD detection and sizing.
    Proceedings of SPIE Medical Imaging, San Diego, CA, Feb 2010
  5. Ambrosini R, Wang P, O'Dell WG
    Volume change determination of metastatic lung tumors in CT images using 3-D template matching.
    Proceedings of SPIE Medical Imaging [#7260-112], Orlando, FL, Feb 2009

Related Presentions
  1. Ambrosini R, O'Dell WG
    Monitoring and Validating Metastatic Tumor Growth in Lung CT
    51st Annual AAPM Meeting (WE-B-201B-4, oral presentation), Philadelphia PA, July 2010
  2. Ambrosini R, Wang P, Victor J, Sobe N, O'Dell WG
    Automatic Detection and Sizing of Metastatic Brain Tumors Using 3D Template Matching
    47th Annual AAPM Meeting (SU-FF-I-2), Seattle WA, July 2005