http://nova.newcastle.edu.au/vital/access/services/Feed ${session.getAttribute("locale")} 5 http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:12305 Background and purpose: Temporal distributions in PSA levels following therapeutic intervention for prostate cancer are known to contain characteristics prognostic of disease progression. An algorithm was developed for extracting such characteristics, specifically in the context of previous observations following radiotherapy. Material and methods: Segmented regression methods were investigated for characterising the ‘signatures’ in log(PSA) descent patterns between intervention and nadir. Results: The segmented regression method can automatically identify and parameterise features in a PSA distribution including failure points and doubling time patterns following nadir. The method has previously been applied to the analysis of descent patterns on a large clinical data series (Radiother Oncol 2009;90:382–8). Batch-processing of data using the method for all patients in a clinical trial would establish population parameter values and ranges. Subsequent application to an individual patient’s PSA data would determine which resulting prognostic group they fall into. Conclusions: As more complete and higher-resolution PSA progression datasets become available, techniques such as presented here will allow flexible definition of the characteristics being examined and rapid extraction of parameters for correlation with clinical progression data. 2012-12-18T22:42:50.539Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:7230 Background: We sought to determine whether inter-patient variations in pattern of PSA changes after radiation exist and, if so, are they prognostically significant. Methods: In the Trans-Tasman Radiation Oncology Group (TROG) 96.01 randomized controlled trial, patients with T2b,c,3,4 N0 prostate cancer (PC) were randomised to 0, 3 or 6 months maximal androgen deprivation prior to 66 Gy to the prostate and seminal vesicles (XRT). Patterns of anatomical site of failure were one of the trial endpoints. Serial serum PSA’s were mandated at all follow-up visits. Pattern recognition software was developed to characterize PSA response “signatures” (PRS) after therapy in individual patients. Results: By 2000, 270 eligible patients were randomised to radiation alone. Individual patient PSA values were observed to descend after radiation according to one of two characteristic “signatures”: single exponential (PRS Type 1), non-exponential (PRS Type 2). Compared to PRS Type 1, men with PRS Type 2 (50% of the group) had lower PSA nadir (nPSA) levels (p < .0001), longer doubling times on relapse (p = .006) and significantly lower rates of local (hazard ratio [HR]: 0.47, 95% confidence interval [0.30–0.75], p = .0014) and distant failure (HR: 0.25[0.13–0.46], p < .0001), death due to PC (HR: 0.20[0.10–0.42], p < .0001) and death due to any cause (HR: 0.37 [0.23–0.60], p < .0001). PRS retained its powerful prognostic significance in Cox models that incorporated all key pre-treatment covariates and nPSA. Conclusions: PRS reflect the presence of tumor phenotypes that vary substantially in their clinical behavior and response to XRT. Molecular characterization is now necessary. 2011-02-17T00:10:05.636Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:281 The technologies available to identify anatomical structures (including radiotherapy target and normal tissue 'volumes'), and to deliver dose accurately to these volumes, have improved significantly in the past decade. However, the ability of clinicians to identify volumes accurately and consistently in patients still suffers from uncertainties that arise from human error, inadequate training, lack of consensus on the derivation of volumes and inadequate characterisation of the accuracy and specificity of imaging technologies. Inadequate volume definition of a target can result in treatment failure and, consequently, disease progression; excessive volume may also lead to unnecessary patient injury. This is a serious problem in routine clinical care. In the context of large multi-centre clinical trials, uncertainty and inconsistency in tissue-volume reporting will be carried through to the analysis of treatment effect on outcome, which will subsequently influence the treatment of future patients. Strategies need to be set in place to ensure that the abilities and consistency of clinicians in defining volumes are aligned with the ability of new technologies to present volumetric information. This review seeks to define the concept of volumetric uncertainty and propose a conceptual model that has these errors evaluated and responded to separately. Specifically, we will explore the major causes, consequences of, and possible remediation of volumetric uncertainty, from the point of view of a multidisciplinary radiotherapy clinical environment. 2010-04-27T06:02:24.049Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:239 PurposeTo investigate the variability of prostate implant quality indices between three different methods of calculating the post-implant dose distribution.Methods and materialsIn a study of 9 permanent prostate implant patients, post-implant dosimetry was carried out using three methods of identifying seed positions within the prostate volume: (1) prostate volumes defined by transrectal ultrasound (TRUS) immediately following implant were registered with shift-film defined seed positions, (2) seeds were identified directly from the post-implant TRUS images, and (3) CT was used to define seed positions and prostate volumes from images acquired at 41-65 days post-implant. For each method, the volume of prostate receiving 90%, 100%, and 150% of the prescribed dose (V90, V100, V150) and the dose delivered to 90% of the prostate volume (D90) were calculated.ResultsPost-implant TRUS volumes were within 15% of the preimplant TRUS volumes in 8 of the 9 patients investigated. The post-implant CT volume was within 15% of the preimplant (TRUS) volume in only 3 of the 9 cases. The value of the dosimetry parameters was dependent on the method used and varied by 5-25% for V90, 5-30% for V100, 42-134% for V150, and 9-60% for D90. No simple relationship was found between change in volume and the resultant change in dosimetry parameter. Differences in dosimetry parameters due to source localization uncertainties was found to be small (1) and (2).ConclusionsThere are many uncertainties in the calculation of parameters that are commonly used to describe the quality of a permanent prostate implant. Differences in the parameters calculated were most likely a result of a combination of factors including uncertainties in delineating the prostate with different imaging modalities, differences in source identification techniques, and intraobserver variability. 2010-04-27T05:56:12.962Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:519 This study examines differences in tumour cellular response using clonogenic cell survival between uniform and non-uniform irradiation. Cells were irradiated with a 6 MV x-ray intensity-modulated beam, in a. single large flask (i.e. intercellular communication is possible) or in three small flasks (i.e. intercellular communication is inhibited across the dose gradient). For non-small-cell lung cancer and melanoma cell lines, the dose response over the entire cell culture was significantly different between freely communicating cell cultures and those with inhibited communication across the dose non-uniformity. Communicating cells exhibited poorer survival in the low dose region of the field but improved survival in the high dose region. In general, the response to non-uniform irradiation appeared to 'average out' over the entire cell culture. This was not seen when intercellular communication was inhibited. The results add strength to the body of evidence regarding bystander effects and the inter-dependence of cellular response. 2010-04-27T05:38:12.043Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3423 In this paper, we examine the effect of treatment parameters in a model used to evaluate permanent prostate implants. The model considers the prostate to be composed of 12 sub-sections, each sub-section is assigned a cell density based on the probability of finding cancer foci in that sub-section. Wasted dose as a result of the dose rate from the implant falling below a level adequate to counteract repopulation was found to vary by 2–16% over the range of radiosensitivity and repopulation rates considered. Within the model, applied to five dose distributions, the uncertainty in the tumour control probability (TCP) values calculated for each sub-section as a result of differences in the model parameters, was found to be less than 12% in most cases for the good quality implants. The difference in TCP values was much larger for the poor quality implant. Substituting a heterogeneous distribution of α for a single mean value resulted in generally lower TCP values though introducing a cut-off value with a Gaussian distribution had a profound effect on the calculated values. Despite uncertainties in the parameters, the model was able to identify sub-sections at risk of local recurrence but as a result of these uncertainties, the TCP values can only be considered in the relative rather than absolute sense. 2010-04-27T05:01:14.802Z ]]>