Assessment of radiation and chemotherapy efficacy for brain cancer patients is traditionally accomplished by measuring changes in tumor size several months after therapy has been administered. The ability to use noninvasive imaging during the early stages of fractionated therapy to determine whether a particular treatment will be effective would provide an opportunity to optimize individual patient management and avoid unnecessary systemic toxicity, expense, and treatment delays. We investigated whether changes in the Brownian motion of water within tumor tissue as quantified by using diffusion MRI could be used as a biomarker for early prediction of treatment response in brain cancer patients. Twenty brain tumor patients were examined by standard and diffusion MRI before initiation of treatment. Additional images were acquired 3 weeks after initiation of chemo-and͞or radiotherapy. Images were coregistered to pretreatment scans, and changes in tumor water diffusion values were calculated and displayed as a functional diffusion map (fDM) for correlation with clinical response. Of the 20 patients imaged during the course of therapy, 6 were classified as having a partial response, 6 as stable disease, and 8 as progressive disease. The fDMs were found to predict patient response at 3 weeks from the start of treatment, revealing that early changes in tumor diffusion values could be used as a prognostic indicator of subsequent volumetric tumor response. Overall, fDM analysis provided an early biomarker for predicting treatment response in brain tumor patients. diffusion MRI ͉ therapeutic response

We review the theoretical background to diffusion tensor imaging (DTI) and some of its commoner clinical applications, such as cerebral ischemia, brain maturation and traumatic brain injury. We also review its potential use in diseases such as epilepsy, multiple sclerosis, and Alzheimer's disease. The value of DTI in the investigation of brain tumors and metabolic disorders is assessed.

The source code for ACC transformation is freely available at http://www.iipl.fudan.edu.cn/demo/accpkg.html.

For users' convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at http://bioinformatics.hitsz.edu.cn/main/~binliu/remote/