The overarching goal of this project is to improve the accuracy in diagnosing cancer metastases in the brain through the development of a novel computer-aided diagnosis (CAD) technique. In today?s cancer treatment, it is often not the primary cancer but the metastasized cancer that causes fatality. Many cancer, including lung, kidney, ovarian, and breast cancer, and melanoma, have a tendency metastasizing to the brain and the number of brain metastases is as high as 170,000 a year in the US alone. Therefore, accurate diagnosis of brain metastases is of utmost importance in saving lives and improving patient?s well-being. Magnetic resonance imaging (MRI) is the most widely used modality to scan brain for potential metastases but diagnosing metastases is a very challenging task that has a considerable rate of false-negatives. The first difficulty in diagnosing metastases is that, at early stage, metastases are asymptomatic. The second difficulty is that metastases manifest as weak signal intensity changes on MRI and their appearance is often highly similar to normal brain structures, such as small blood vessels, meaning that one must visualize in his/her mind whether an observed object is a metastasis or a blood vessel. Missing a metastasis has a severe consequence as the patient will not be called for further treatment. The benefit of accurate diagnosis of metastases, on the other hand, can have a significant benefit to the patient as treatment like stereotactic radiosurgery (SRS) can completely eliminate the metastasized tumor in many cases and extend patient?s life span by three to four years in most cases. CAD can play a key role in improving the accuracy in diagnosing brain metastases by identifying abnormal signal intensity changes and mark them for radiologists to examine. In this process, CAD will function as an aid tool to complement human?s expertise in interpreting brain MRI. However, despite the importance of finding and treating brain metastases, there currently is lacking a CAD approach to this problem. Many existing computational techniques on brain MRI were tailored to MRI data acquired in a research setting that often involves many other MRI techniques such as DWI, DTI, and functional MRI. But in clinics only anatomic MRI like T1- and T2-weighted MRI are used to scan a patient, therefore, a CAD approach must be tailored to the clinical setting to assist radiologists in reading the brain MRI. In this project we propose a CAD design that is based on novel computational techniques and integrated with routine clinical MRI acquisition. The CAD design features minimum user intervention and parameter selection, high robustness, and user-friendliness. We will also take advantage of the availability of graphics processing unit (GPU) in implementation to speed up the computations. We expect the proposed CAD approach will improve the accuracy of diagnosing brain metastases, and in turn, save lives and benefit patients? well-being.
We plan to develop novel computer-aided diagnosis (CAD) techniques to assist clinical detection of cancer metastases in the brain on magnetic resonance imaging (MRI). MRI is the most popular imaging modality used to scan for potential brain metastases in cancer patients, yet detecting brain metastases is a very difficult and error-prone process as many metastases manifest as very weak intensity changes in the MRI. The proposed CAD techniques will increase the accuracy of clinical diagnosis for early detection of brain metastases and therefore allow prompt treatment of the disease to save lives and improve patient?s well-being.
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| Qin, Lei; Li, Xiang; Li, Angie et al. (2018) Clinical Validation of Automatable Gaussian Normalized CBV in Brain Tumor Analysis: Superior Reproducibility and Slightly Better Association with Survival than Current Standard Manual Normal Appearing White Matter Normalization. Transl Oncol 11:1398-1405 |
