Biomedical subjects

Satoru Hayasaka

Publications and source records attributed to Satoru Hayasaka.

7 recordsLinked to original sources

PubMed2004

Nonstationary cluster-size inference with random field and permutation methods.

Because of their increased sensitivity to spatially extended signals, cluster-size tests are widely used to detect changes and activations in brain images. However, when images are nonstationary, the cluster-size distribution varies depending on local smoothness. Clusters tend to be large in smooth regions, resulting in increased false positives, while in rough regions, clusters tend to be small, resulting in decreased sensitivity. Worsley et al. proposed a random field theory (RFT) method that adjusts cluster sizes according to local roughness of images [Worsley, K.J., 2002. Nonstationary FWHM and its effect on statistical inference of fMRI data. Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan. Available on CD-ROM in NeuroImage 16 (2) 779-780; Hum. Brain Mapp. 8 (1999) 98]. In this paper, we implement this method in a permutation test framework, which requires very few assumptions, is known to be exact [J. Cereb. Blood Flow Metab. 16 (1996) 7] and is robust [NeuroImage 20 (2003) 2343]. We compared our method to stationary permutation, stationary RFT, and nonstationary RFT methods. Using simulated data, we found that our permutation test performs well under any setting examined, whereas the nonstationary RFT test performs well only for smooth images under high df. We also found that the stationary RFT test becomes anticonservative under nonstationarity, while both nonstationary RFT and permutation tests remain valid under nonstationarity. On a real PET data set we found that, though the nonstationary tests have reduced sensitivity due to smoothness estimation variability, these tests have better sensitivity for clusters in rough regions compared to stationary cluster-size tests. We include a detailed and consolidated description of Worsley nonstationary RFT cluster-size test.

Brain↗

PubMed2004

Combining voxel intensity and cluster extent with permutation test framework.

In a massively univariate analysis of brain image data, statistical inference is typically based on intensity or spatial extent of signals. Voxel intensity-based tests provide great sensitivity for high intensity signals, whereas cluster extent-based tests are sensitive to spatially extended signals. To benefit from the strength of both, the intensity and extent information needs to be combined. Various ways of combining voxel intensity and cluster extent are possible, and a few such combining methods have been proposed. Poline et al.'s [NeuroImage 16 (1997) 83] minimum P value approach is sensitive to signals whose either intensity or extent is significant. Bullmore et al.'s [IEEE Trans. Med. Imag. 18 (1999) 32] cluster mass method can detect signals whose intensity and extent are sufficiently large, even when they are not significant by intensity or extent alone. In this work, we study such combined inference methods using combining functions (Pesarin, F., 2001. Multivariate Permutation Tests. Wiley, New York) and permutation framework [Holmes et al., J. Cereb. Blood Flow Metab. 16 (1996) 7], which allow us to examine different ways of combining voxel intensity and cluster extent information without knowing their distribution. We also attempt to calibrate combined inference by using weighted combining functions, which adjust the test according to signals of interest. Furthermore, we propose meta-combining, a combining function of combining functions, which integrates strengths of multiple combining functions into a single statistic. We found that combined tests are able to detect signals that are not detected by voxel or cluster size test alone. We also found that the weighted combining functions can calibrate the combined test according to the signals of interest, emphasizing either intensity or extent as appropriate. Though not necessarily more sensitive than individual combining functions, the meta-combining function is sensitive to all types of signals and thus can be used as a single test summarizing all the combining functions.

Artifacts↗

PubMed2004

MRI for preoperative staging of renal cell carcinoma using the 1997 TNM classification: comparison with surgical and pathologic staging.

OBJECTIVE: The purpose of our study was to determine the accuracy of MRI for preoperative staging of renal cell carcinoma using the 1997 TNM classification. MATERIALS AND METHODS: We conducted a retrospective review of MRI performed in 40 consecutive patients with 42 renal cell carcinomas before radical (n = 35) or partial (n = 4) nephrectomy or exploratory laparotomy (n = 3). The interval between imaging and surgery ranged from 1 to 59 days (mean, 17.9 days). Imaging was performed with T1- and T2-weighted, dynamic gadolinium-enhanced, and time-of-flight sequences. MRI and surgical-pathologic staging was performed using the 1997 TNM staging system. MRI staging was compared with surgical-pathologic staging as the gold standard. Agreement between the two staging methods was assessed using the kappa statistic. RESULTS: Agreement between MRI and surgical-pathologic staging was good for T staging (kappa = 0.72 and 0.78 for reviewers 1 and 2 respectively), poor for N staging (kappa = 0.13, both reviewers), good for M staging (kappa = 0.66, both reviewers), and excellent for the assessment of venous involvement (kappa = 0.93, both reviewers). MRI overestimated the T stage in five patients and the N stage in five and underestimated the T stage in three, the N stage in four, the M stage in one, and the extent of venous thrombosis in two patients. CONCLUSION: MRI is a reliable method for preoperative staging of renal cell carcinoma using the 1997 TNM classification, in particular for assessing venous involvement.

Adolescent↗

PubMed2003

Validating cluster size inference: random field and permutation methods.

Cluster size tests used in analyses of brain images can have more sensitivity compared to intensity based tests. The random field (RF) theory has been widely used in implementation of such tests, however the behavior of such tests is not well understood, especially when the RF assumptions are in doubt. In this paper, we carried out a simulation study of cluster size tests under varying smoothness, thresholds, and degrees of freedom, comparing RF performance to that of the permutation test, which is known to be exact. For Gaussian images, we find that the RF methods are generally conservative, especially for low smoothness and low threshold. For t images, the RF tests are found to be conservative at lower thresholds and do not perform well unless the threshold is high and images are sufficiently smooth. The permutation test performs well for any settings though the discreteness in cluster size must be accounted for. We make specific recommendations on when permutation tests are to be preferred to RF tests.

Algorithms↗

PubMed2003

Controlling the familywise error rate in functional neuroimaging: a comparative review.

Functional neuroimaging data embodies a massive multiple testing problem, where 100,000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three approaches to thresholding images of test statistics: Bonferroni, random field and the permutation test. Owing to recent developments, improved Bonferroni procedures, such as Hochberg's methods, are now applicable to dependent data. Continuous random field methods use the smoothness of the image to adapt to the severity of the multiple testing problem. Also, increased computing power has made both permutation and bootstrap methods applicable to functional neuroimaging. We evaluate these approaches on t images using simulations and a collection of real datasets. We find that Bonferroni-related tests offer little improvement over Bonferroni, while the permutation method offers substantial improvement over the random field method for low smoothness and low degrees of freedom. We also show the limitations of trying to find an equivalent number of independent tests for an image of correlated test statistics.

Brain↗

PubMed2002

Gene-expression profiles predict survival of patients with lung adenocarcinoma.

Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two equivalent but independent training and testing sets, or 'leave-one-out' cross-validation analysis with all tumors, a risk index based on the top 50 genes identified low-risk and high-risk stage I lung adenocarcinomas, which differed significantly with respect to survival. This risk index was then validated using an independent sample of lung adenocarcinomas that predicted high- and low-risk groups. This index included genes not previously associated with survival. The identification of a set of genes that predict survival in early-stage lung adenocarcinoma allows delineation of a high-risk group that may benefit from adjuvant therapy.

Adenocarcinoma↗

PubMed2002

The regulation of prostate cancer cell adhesion to human bone marrow endothelial cell monolayers by androgen dihydrotestosterone and cytokines.

A previous study from our laboratory suggested that prostate cancer metastasis to bone may be mediated, in part, by preferential adhesion to human bone marrow endothelial (HBME) cells. Tumor cell adhesion to endothelial cells may be modulated by the effect of cytokines on cell adhesion molecules (CAMs). Tumor necrosis factor-alpha (TNF-alpha) regulates VCAM expression on the endothelium and this effect is enhanced by dihydrotestosterone (DHT). Transforming growth factor-beta (TGF-beta) stimulates the expression of alpha2beta1 integrin on PC-3 cells. The current study investigated the effects of the above cytokines and DHT (singularly and in various combinations) upon HBME and prostate cancer cell expression of VCAM, alpha2 integrin subunit, and beta1 integrin subunit by flow cytometry. We also monitored the effects of the above treatments on PC-3 cell adhesion to HBME monolayers. The data demonstrate that none of the treatments significantly altered the expression of selected CAMs on HBME cell and neoplastic prostate cell lines. The treatment of HBME monolayers with various combinations of cytokines and DHT prior to performing adhesion assays with PC-3 demonstrates that treatments containing TGF-beta reduced PC-3 cell adhesion to HBME monolayers by 32% or greater (P < 0.05). The reduction in PC-3 cell adhesion to TGF-beta-treated HBME monolayers was dose dependent. Interestingly, LNCaP cells but not PC-3 cells treated with TGF-beta had a reduced ability to adhere to untreated HBME monolayers. These results suggest that TGF-beta may reduce tumor cell adhesion to bone marrow microvascular endothelium, in vivo. The biological significance of this observation is discussed.

Adenocarcinoma↗