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PubMed2004

Preliminary assessment of magnetic resonance spectroscopic imaging in predicting treatment outcome in patients with prostate cancer at high risk for relapse.

The purpose of the study was to determine whether 3D proton magnetic resonance spectroscopic imaging (MRSI) can predict treatment outcome in high risk patients with prostate cancer. Endorectal magnetic resonance imaging (MRI) and 1H-MRSI were performed in 16 patients with prostate cancer who were considered high risk because of clinical stage T3-4, Gleason score>/=8, and/or prostate-specific antigen (PSA) level>20 ng/mL. Patients were treated with chemotherapy/hormone therapy, underwent radical prostatectomy (RP) or radiation therapy, and were followed for PSA relapse (follow-up, 19-43 months). The ratio of choline plus creatine to citrate was used to localize peripheral zone cancer. An MRSI risk score on a scale of 0-3 was derived from the volume and degree of metabolic abnormality. Magnetic resonance spectroscopic imaging risk score, MRI tumor/node (TN) stage, clinical stage, Gleason score, and PSA were used as predictors of pathologic stage in patients treated with RP (n=10) and PSA relapse in all patients. Magnetic resonance imaging TN stage (P<0.01) and MRSI risk score (P<0.05) correlated with pathologic stage, but clinical stage did not (P=0.35). Magnetic resonance imaging TN stage was the only significant predictor of PSA relapse in the univariate analysis (P<0.05). Although the MRSI risk score did not reach significance (P=0.13), 6 patients with a score<0.9 were relapse-free, whereas 7 of 10 patients with a score>0.9 relapsed. Magnetic resonance imaging and MRSI risk assessments agreed in 15 of 16 patients. These preliminary results suggest that tumor metabolic assessment may indicate treatment outcome in high-risk patients with prostate cancer. Although MRSI did not provide added prognostic value to MRI in this small number of patients, MRSI might increase the confidence of the clinician in assessing risk on MRI by contributing supporting metabolic data.

Disease-Free Survival↗

PubMed2003

Estimation of single-trial multicomponent ERPs: differentially variable component analysis (dVCA).

A Bayesian inference framework for estimating the parameters of single-trial, multicomponent, event-related potentials is presented. Single-trial recordings are modeled as the linear combination of ongoing activity and multicomponent waveforms that are relatively phase-locked to certain sensory or motor events. Each component is assumed to have a trial-invariant waveform with trial-dependent amplitude scaling factors and latency shifts. A Maximum a Posteriori solution of this model is implemented via an iterative algorithm from which the component's waveform, single-trial amplitude scaling factors and latency shifts are estimated. Multiple components can be derived from a single-channel recording based on their differential variability, an aspect in contrast with other component analysis techniques (e.g., independent component analysis) where the number of components estimated is equal to or smaller than the number of recording channels. Furthermore, we show that, by subtracting out the estimated single-trial components from each of the single-trial recordings, one can estimate the ongoing activity, thus providing additional information concerning task-related brain dynamics. We test this approach, which we name differentially variable component analysis (dVCA), on simulated data and apply it to an experimental dataset consisting of intracortically recorded local field potentials from monkeys performing a visuomotor pattern discrimination task.

Algorithms↗