Computation Framework for Lesion Detection and Response Assessment Based Upon Physiological Imaging for Supporting Radiation Therapy of Brain Metastases.

Brain Metastases;Lesion Detection;Response Assessment;Biomedical Engineering;Engineering;Biomedical Engineering

Farjam, RezaHernandez, Luis ;

University of Michigan

关键词: Brain Metastases;    Lesion Detection;    Response Assessment;    Biomedical Engineering;    Engineering;    Biomedical Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/97989/rezaf_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Brain metastases are the most prevalent form of cancer in the central nervous system and up to 45% of cancer patients eventually develop brain metastases during their illness. Selection of whole brain radiotherapy (WBRT) versus stereotactic radiosurgery, two routine treatments for brain metastases, highly depends on the number and size of metastatic lesions in a patient. Our clinical investigations reveal that up to 40% of brain metastases with a diameter <5mm could be missed in a routine clinical diagnosis using contrast-enhanced MRI. Hence, this dissertation initially describes the development of a template-matching based computer-aided detection (CAD) system for automatic detection of small lesions in post-Gd T1-weighted MRI to assist radiological diagnosis. Our results showed a significant improvement in detecting small lesions using the proposed methodology. When a cancer patient is given a treatment, it is very important to assess the tumor response to therapy early. This is traditionally performed by measuring a change in the gross tumor volume. However, changes in tumor physiology, which happen earlier than the volumetric changes, have the potential to provide a better means in prediction of tumor response to therapy and also could be used for therapy guidance. But, there are several challenges in assessment of tumor response to therapy, especially due to the heterogeneous distribution pattern of the physiological parameters in a tumor, image mis-registration issues caused by tumor shrinkage/increase across the time of followups, lack of methodologies combining information from different physiological viewpoints, and etc. Hence, this dissertation mainly focused on development of techniques overcoming these challenges using information from two important aspects of tumor physiology: tumor vascular and cellularity properties derived from dynamic contrast-enhance and diffusion-weighted MRI. Our proposed techniques were evaluated with lesions treated by either WBRT alone or combined with Bortezomib as a radiation sensitizer. We found that changes in both tumor vascular and cellularity properties play an important but different role for predicting tumor response to therapy, depending on the tumor types and underlying treatment. Also, we found that combing the two parameters provides a better tool for response assessment.