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Oncological outcomes of laparoscopic partial nephrectomy are no worse than those after open sparing operation for kidney cancer. However, laparoscopic procedures are accompanied by a longer warm ischemia. In order to reduce the ischemic injury, local ischemia by isolation and temporary clamping of the individual branches of the renal artery was used. From January 2009 to June 2012, 60 laparoscopic partial nephrectomies were performed in the clinic, including 34 resections with temporary clipping of renal artery and 26 resections with temporary clipping of secondary and tertiary segmental and subsegmental branches. Functions of affected kidney were evaluated before and after surgery in both groups of patients. At 3 months after surgery, significantly lower decline of the function of affected kidney was observed in patients who underwent resection with the use of local ischemia compared with patients who received the intervention accompanied by a temporary clamping the renal artery. Partial nephrectomy using local ischemia can significantly expand the indications for laparoscopic and retroperitoneoscopic sparing operation for renal cell cancer.

We explore a connection between the singular value decomposition (SVD) and functional principal component analysis (FPCA) models in high-dimensional brain imaging applications. We formally link right singular vectors to principal scores of FPCA. This, combined with the fact that left singular vectors estimate principal components, allows us to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images. As an example, a FPCA model is fit to high-resolution morphometric (RAVENS) images. The main directions of morphometric variation in brain volumes are identified and discussed., (Copyright © 2011 Elsevier Inc. All rights reserved.)

We propose fast and scalable statistical methods for the analysis of hundreds or thousands of high dimensional vectors observed at multiple visits. The proposed inferential methods do not require loading the entire data set at once in the computer memory and instead use only sequential access to data. This allows deployment of our methodology on low-resource computers where computations can be done in minutes on extremely large data sets. Our methods are motivated by and applied to a study where hundreds of subjects were scanned using Magnetic Resonance Imaging (MRI) at two visits roughly five years apart. The original data possesses over ten billion measurements. The approach can be applied to any type of study where data can be unfolded into a long vector including densely observed functions and images. Supplemental materials are provided with source code for simulations, some technical details and proofs, and additional imaging results of the brain study.