Biomedical subjects

Janet O'Sullivan

Publications and source records attributed to Janet O'Sullivan.

3 recordsLinked to original sources

PubMed2006

Tumor hypoxia imaging with [F-18] fluoromisonidazole positron emission tomography in head and neck cancer.

PURPOSE: Advanced head and neck cancer shows hypoxia that results in biological changes to make the tumor cells more aggressive and less responsive to treatment resulting in poor survival. [F-18] fluoromisonidazole (FMISO) positron emission tomography (PET) has the ability to noninvasively quantify regional hypoxia. We investigated the prognostic effect of pretherapy FMISO-PET on survival in head and neck cancer. EXPERIMENTAL DESIGN: Seventy-three patients with head and neck cancer had pretherapy FMISO-PET and 53 also had fluorodeoxyglucose (FDG) PET under a research protocol from April 1994 to April 2004. RESULTS: Significant hypoxia was identified in 58 patients (79%). The mean FMISO tumor/bloodmax (T/Bmax) was 1.6 and the mean hypoxic volume (HV) was 40.2 mL. There were 28 deaths in the follow-up period. Mean FDG standard uptake value (SUV)max was 10.8. The median time for follow-up was 72 weeks. In a univariate analysis, T/Bmax (P=0.002), HV (P=0.04), and the presence of nodes (P=0.01) were strong independent predictors. In a multivariate analysis, including FDG SUVmax, no variable was predictive at P<0.05. When FDG SUVmax was removed from the model (resulting in n=73 with 28 events), nodal status and T/Bmax (or HV) were both highly predictive (P=0.02, 0.006 for node and T/Bmax, respectively; P=0.02 and 0.001 for node and HV, respectively). CONCLUSIONS: Pretherapy FMISO uptake shows a strong trend to be an independent prognostic measure in head and neck cancer.

Aged↗

PubMed2003

Modelling multivariate biomechanical measurements of the spine during a rowing exercise.

OBJECTIVE: To investigate the ability of statistical techniques to detect systematic changes in rowing technique during a rowing session and to discriminate between rowers of different abilities with and without back pain. DESIGN: Statistical techniques were applied to kinematic datasets of elite level rowers, in order to construct an empirical model of the rowing stroke. BACKGROUND: The size and complexity of datasets generated by biomechanical kinematics evaluations has led to opportunities for analysing pathology whilst introducing substantial challenges for statistical analysis. METHODS: Spinal motion and load output of 18 International and National standard competitive rowers were monitored during ergometer rowing sessions. International rower data were used to construct an empirical model of this activity. Linear stroke models were derived using principal components and a generalized cross-validation procedure. Performance characteristics of the identified models were calculated for all rowing groups. The stroke model was applied to distinguishing pattern variations within and between rowers. A multivariate logistic regression analysis was carried out to examine the relationship between stroke model parameters on the incidence of low back pain. RESULTS: 90% of the variability in the data was explained by the first three principal component variables. Stroke models with three basis functions were selected for each variable. The models performed well on the National rowers, providing validation of the models. A 2-variable model showed a significant difference between the rowing stroke characteristics of rowers with and without low back pain (P<0.01). CONCLUSIONS: A parsimonious collection of empirical models effectively describes motion and load characteristics of ergometer rowing. Patterns in rowing technique are found to be strongly associated with the incidence lower back pain. RELEVANCE: Empirical statistical models can be used to track changes in rowing technique, and discriminate between different rowing groups. This may impact rowing training, and rehabilitation.

Biomechanical Phenomena↗

PubMed2002

Factorial design considerations.

PURPOSE: Factorial designs may be proposed to test extra questions within a clinical trial. A common approach to sample size and analysis for factorial trials assumes no statistical interactions and does not adjust for multiple testing. This investigation considered the trade-off between potential gains from testing more questions with fewer patients versus how often a factorial trial might arrive at an incorrect conclusion. METHODS: A simulation study of a 2 x 2 design (observation v chemotherapy v radiation therapy v the combination) was performed under various conditions, including effect of one, both, or neither treatment and absence or presence of statistical interaction (effect of one treatment differed according to the presence of the other). Three analysis approaches were investigated, one assuming no interaction, a second testing first for interaction, and the third testing for interaction as well as adjusting for multiple testing. The approaches were compared with respect to the probability of selecting the correct treatment arm. RESULTS: No one approach was superior. Testing for interaction was beneficial in some settings but detrimental in others. Under some scenarios, the factorial design improved efficiency, but under others, all three approaches resulted in poor probability of selecting the correct treatment arm at the end of the trial. CONCLUSION: Extra efficiency is possible, but it is difficult to predict when favorable conditions exist. If a factorial design is used, potential efficiency gains should be weighed against potential loss of power to arrive at the correct conclusion under possible scenarios of interest.

Clinical Trials as Topic↗