Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “NEOPLASM STATISTICS”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 775 records · Page 43Linked to original sources
PubMed2002

Probabilities for a probabilistic network: a case study in oesophageal cancer.

With the help of two experts in gastrointestinal oncology from The Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, a decision-support system is being developed for patient-specific therapy selection for oesophageal cancer. The kernel of the system is a probabilistic network that describes the presentation characteristics of cancer of the oesophagus and the pathophysiological processes of invasion and metastasis. While the construction of the graphical structure of the network was relatively straightforward, probability elicitation with existing methods proved to be a major obstacle. To overcome this obstacle, we designed a new method for eliciting probabilities from experts that combines the ideas of transcribing probabilities as fragments of text and of using a scale with both numerical and verbal anchors for marking assessments. In this paper, we report experiences with our method in eliciting the probabilities required for the oesophagus network. The method allowed us to elicit many probabilities in reasonable time. To gain some insight in the quality of the probabilities obtained, we conducted a preliminary evaluation study of our network, using data from real patients. We found that for 85% of the patients, the network predicted the correct cancer stage.

Decision Support Techniques↗
PubMed2006

An approach to estimating prognosis using fractional polynomials in metastatic renal carcinoma.

We present a prognostic model for metastatic renal cell carcinoma based on fractional polynomials. We retrospectively analysed 425 metastatic renal cell carcinoma patients treated with subcutaneous recombinant cytokine-based home therapies in consecutive trials. In our approach, we categorised a continuous prognostic index produced by the multivariable fractional polynomial (MFP) algorithm, using a strategy in which continuous predictors are kept continuous. The MFP algorithm selected five prognostic factors as significant at the 5% level in a multivariable model: lymph node metastases, liver metastases, bone metastases, age, C-reactive protein and neutrophils. The MFP model allowed us to divide patients into four risk groups achieving median overall survivals of 38 months (low risk), 23 months (low intermediate risk), 15 months (high intermediate risk) and 5.6 months (high risk). Our approach, based on categorising a continuous prognostic index produced by the MFP algorithm, allowed more flexibility in the determination of risk groups than traditional approaches.

Algorithms↗
PubMed2002

Complexities in ETS-domain transcription factor function and regulation: lessons from the TCF (ternary complex factor) subfamily. The Colworth Medal Lecture.

The ETS-domain transcription factor family can be divided into a series of subfamilies. Elk-1 represents the founding member of the ternary complex factor (TCF) subfamily. By focusing on the TCF subfamily, we can demonstrate the complexities that exist in the function and regulation of ETS-domain transcription factors. This article focuses on Elk-1 in detail and summarizes the functions of other TCFs. The key themes covered include the domain structure of the TCFs, the mechanisms of complex formation with serum response factor, regulation of TCFs by mitogen-activated protein kinase cascades, and transcriptional regulatory properties of the TCFs. Finally, the emerging role of the TCFs in vivo is discussed. A picture is developing indicating that, while these proteins exhibit significant sequence and functional conservation, key differences in their structure and regulation are being identified which may relate to unique functions of these proteins in vivo.

Amino Acid Sequence↗
PubMed2000

Cavity theory applied to the dosimetry of systemic radiotherapy of bone metastases.

A two-component model of an osteoblastic metastatic lesion has been developed to determine the absorbed dose delivered to soft tissue during systemic radiotherapy of osseous metastases. Doses to soft tissue from radioisotopes distributed in bone were calculated using Burlin's general cavity theory. A correction term was used to account for the absence of charged particle equilibrium within the metastatic lesion. Radiation doses for 153Sm, 186Re, 89Sr and 32P were calculated for several physiologically realistic lesion structures. Burlin's cavity weighting factor was greatest for higher energy isotopes and it decreased as the soft tissue cavity size increased. The correction for the absence of charged particle equilibrium also decreased with soft tissue pathlength, but increased with average bone pathlengths. Doses to soft tissue cavities ranged from 0.1 to 0.2 Gy MBq(-1) d(-1) for 153Sm to 0.5 to 0.6 Gy MBq(-1) d(-1) for 32P. Using the factors calculated in this work, the dose to soft tissue cavities within bone metastases can be calculated when the dose to adjacent bone has been determined, perhaps by autoradiography or electron paramagnetic resonance dosimetry. The doses calculated with this more accurate model of bone metastases demonstrate errors of 20% to 50% in previous calculations of the average dose to homogeneous metastatic lesions.

Algorithms↗
PubMed2004

In vivo mammary tumourigenesis in the Sprague-Dawley rat and microdosimetric correlates.

Standard methods for risk assessments resulting from human exposures to mixed radiation fields in Space consisting of different particle types and energies rely upon quality factors. These are generally defined as a function of linear energy transfer (LET) and are assumed to be proportional to the risk. In this approach, it is further assumed that the risks for single exposures from each of the radiation types add linearly. Although risks of cancer from acute exposures to photon radiations have been measured in humans, quality factors for protons and ions of heavier atomic mass are generally inferred from animal and/or cellular data. Because only a small amount of data exists for such particles, this group has been examining tumourigenesis initiated by energetic protons and iron ions. In this study, 741 female Sprague-Dawley rats were irradiated or sham irradiated at approximately 60 days of age with 250 MeV protons, 1 GeV/nucleon iron ions or both protons and iron ions. The results suggest that the risk of mammary tumours in the rats sequentially irradiated with 1 GeV/nucleon 56Fe ions and 250 MeV protons is less than additive. These data in conjunction with earlier results further suggest that risk assessments in terms of only mean LETs of the primary cosmic rays may be insufficient to accurately evaluate the relative risks of each type of particle in a radiation field of mixed radiation qualities.

Animals↗
PubMed2002

Analysis of radon-induced lung cancer risk by a stochastic state-vector model of radiation carcinogenesis.

A biologically based state-vector model (SVM) of radiation carcinogenesis has been extended to incorporate stochasticity of cellular transitions and specific in vivo irradiation conditions in the lungs. Dose-rate-dependent cellular transitions related to the formation of double-stranded DNA breaks, repair of breaks, interactions (translocations) between breaks, fixation of breaks, cellular inactivation, stimulated mitosis and promotion through loss of intercellular communication are simulated by Monte Carlo methods. The stochastic SVM has been applied to the analysis of lung cancer incidence in uranium miners exposed to alpha-emitting radon progeny. When incorporating in vivo features of cell differentiation, stimulated cell division and heterogeneity of cellular doses into the model, excellent agreement between epidemiological data and modelling results could be obtained. At low doses, the model predicts a nonlinear dose-response relationship; e.g., computed lung cancerrisk at 20WLM is about half of current lung cancer estimates based on the linear hypothesis. The model also predicts a slight dose rate effect; e.g., at a cumulative exposure of 20 WLM, calculated lung cancer incidence for an exposure rate 0.27 WLM/year (assuming an exposure time of 73 years) is smaller by a factor of 1.2 than that for an exposure rate of 10 WLM/year.

Air Pollutants, Occupational↗
PubMed2004

The bystander effect model of Brenner and Sachs fitted to lung cancer data in 11 cohorts of underground miners, and equivalence of fit of a linear relative risk model with adjustment for attained age and age at exposure.

Bystander effects following exposure to alpha-particles have been observed in many experimental systems, and imply that linearly extrapolating low dose risks from high dose data might materially underestimate risk. Brenner and Sachs (2002 Int. J. Radiat. Biol. 78 593-604; 2003 Health Phys. 85 103-8) have recently proposed a model of the bystander effect which they use to explain the inverse dose rate effect observed for lung cancer in underground miners exposed to radon daughters. In this paper we fit the model of the bystander effect proposed by Brenner and Sachs to 11 cohorts of underground miners, taking account of the covariance structure of the data and the period of latency between the development of the first pre-malignant cell and clinically overt cancer. We also fitted a simple linear relative risk model, with adjustment for age at exposure and attained age. The methods that we use for fitting both models are different from those used by Brenner and Sachs, in particular taking account of the covariance structure, which they did not, and omitting certain unjustifiable adjustments to the miner data. The fit of the original model of Brenner and Sachs (with 0 y period of latency) is generally poor, although it is much improved by assuming a 5 or 6 y period of latency from the first appearance of a pre-malignant cell to cancer. The fit of this latter model is equivalent to that of a linear relative risk model with adjustment for age at exposure and attained age. In particular, both models are capable of describing the observed inverse dose rate effect in this data set.

Air Pollutants, Radioactive↗