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At least 1,279 records · Page 71Linked to original sources
PubMed1999

Practical considerations in using calculated healthy-tissue complication probabilities for treatment-plan optimization.

PURPOSE: Healthy and neoplastic tissues are generally exposed nonuniformly to ionizing radiation. It is thus useful to develop algorithms that predict the probability of tumor control or normal tissue complication probability (NTCP) for any given spatial pattern of dose delivery. The questions addressed here concern: (a) the sensitivity of the NTCP predictions to the actual model used for extrapolation from uniform irradiation (where some clinical data exist) to nonuniform exposures, (b) its dependence on tissue type, and (c) consequences for treatment-plan optimization. METHODS AND MATERIALS: Two (of several possible) NTCP formulations are used here: the Lyman model and a binomial equation. The effective volume-reduction scheme of Kutcher and Burman is used to obtain the NTCP for an arbitrary distribution of dose. NTCP was calculated for seven organs by postulating a dose distribution of maximum nonuniformity. RESULTS: Both models fit available NTCP data well, but have very different extrapolations for exposures of small tissue volumes and very low values of NTCP (e.g., < 5%) where no data exist. Organs with pronounced volume effects (lung, kidneys) show substantial NTCP differences between the two models. Even in organs where the volume effect is small (e.g., spinal cord, brain), differences in NTCP due to the model selected may still have serious clinical consequences, as an actual example (for the spinal cord) indicates. CONCLUSIONS: NTCP calculations based on extrapolations to volume fractions and/or NTCP levels for which reliable data do not exist depend on the model used to fit the data and the degree of dose nonuniformity. If NTCP is to be used in treatment-plan optimization, the prudent approach is to design plans that reproduce the conditions under which available dose-volume data were taken (e. g., uniform dose distributions).

Algorithms↗
PubMed1989

Criteria for the validity of clinical trials of treatments of cohorts of cancer patients based on the Hardin Jones principle.

With the assumption of the validity of the Hardin Jones principle that the death rate of members of a homogeneous cohort of cancer patients is constant, three criteria for the validity of clinical trials of cancer treatments are formulated. These criteria are satisfied by most published clinical trials, but one trial was found to violate all three, rendering the validity of its reported results uncertain.

Ascorbic Acid↗
PubMed1995

Use of risk projection models for the comparison of mortality from radiation-induced breast cancer in various populations.

The aim of our study is to compare lifetime risk estimates for radiation-induced breast cancer between various populations. Some epidemiological studies, mainly on Japanese A-bomb survivors, but also on North American populations irradiated for medical reasons, provide risk coefficients for excess mortality from breast cancer after exposure to ionizing radiation. For this comparative study, these risk coefficients have been transferred into the Japanese, American, and French populations, using demographic data, risk projection models, and assuming an individual acute exposure. The lifetime risk estimates are similar in the three populations when the additive projection model is used because of the similarity of the life tables of the three countries. However, using the multiplicative model, large differences appear due to the discrepancies in the baseline breast cancer rates between the Japanese and Occidental populations. It is for this reason that transfer of risk coefficients fitted on the Japanese population data must be considered with caution when applied to an Occidental population. Lifetime risk estimates for a given population, the French population for example, are largely dependent on the risk coefficients considered from various epidemiological studies. Nevertheless, for each source of data, they are higher with the multiplicative model than with the additive one. Moreover, it should be noted that data from medical irradiation lead to higher risk estimates when using the additive model, whereas studies of the A-bomb survivors lead to higher risk estimates with the multiplicative model. This comparative study points out the variations of the lifetime risk calculations according to the baseline breast cancer mortality rates of the three populations and with the use of different mathematical projection models.

Adult↗
PubMed2001

Estimation and prediction for cancer screening models using deconvolution and smoothing.

The model that specifies that cancer incidence, I, is the convolution of the preclinical incidence, g, and the density of time in the preclinical phase, f, has frequently been utilized to model data from cancer screening trials and to estimate such quantities as sojourn time, lead time, and sensitivity. When this model is fit to the above data, the parameters of f as well as the parameter(s) governing screening sensitivity must be estimated. Previously, g was either assumed to be equal to clinical incidence or assumed to be a constant or exponential function that also had to be estimated. Here we assume that the underlying incidence, I, in the study population (in the absence of screening) is known. With I known, g then becomes a function of f, which can be solved for using (numerical) deconvolution, thus eliminating the need to estimate g or make assumptions about it. Since numerical deconvolution procedures may be highly unstable, however, we incorporate a smoothing procedure that produces a realistic g function while still closely reproducing the original incidence function I upon convolution with f. We have also added the concept of competing mortality to the convolution model. This, along with the realistic preclinical incidence function described above, results in more accurate estimates of sojourn time and lead time and allows for estimation of quantities related to overdiagnosis, which we define here.

Clinical Trials as Topic↗
PubMed2004

Assessing intervention effects in a school-based nutrition intervention trial: which analytic model is most powerful?

This article compares four mixed-model analyses valid for group-randomized trials (GRTs) involving a nested cohort design with a single pretest and posttest. This study makes estimates of intraclass correlations (ICCs) available to investigators planning GRTs addressing dietary outcomes. It also provides formulae demonstrating the potential benefits to the standard error of the intervention effect (sigma(delta)) from adjustments for both fixed and time-varying covariates and correlations over time. These estimates will allow other researchers using these variables to plan their studies by estimating a priori detectable differences and sample size requirements for any of the four analytic options. These methods are demonstrated using data from the Teens Eating for Energy and Nutrition at School study. Mixed-model analyses of covariance proved to be the most powerful analysis in that data set. The formulae may be applied to any dependent variable in any GRT given corresponding information for those variables on the parameters that define the formulae.

Adolescent↗
PubMed2003

A simple method for analyzing data from a randomized trial with a missing binary outcome.

BACKGROUND: Many randomized trials involve missing binary outcomes. Although many previous adjustments for missing binary outcomes have been proposed, none of these makes explicit use of randomization to bound the bias when the data are not missing at random. METHODS: We propose a novel approach that uses the randomization distribution to compute the anticipated maximum bias when missing at random does not hold due to an unobserved binary covariate (implying that missingness depends on outcome and treatment group). The anticipated maximum bias equals the product of two factors: (a) the anticipated maximum bias if there were complete confounding of the unobserved covariate with treatment group among subjects with an observed outcome and (b) an upper bound factor that depends only on the fraction missing in each randomization group. If less than 15% of subjects are missing in each group, the upper bound factor is less than.18. RESULTS: We illustrated the methodology using data from the Polyp Prevention Trial. We anticipated a maximum bias under complete confounding of.25. With only 7% and 9% missing in each arm, the upper bound factor, after adjusting for age and sex, was.10. The anticipated maximum bias of.25 x.10 =.025 would not have affected the conclusion of no treatment effect. CONCLUSION: This approach is easy to implement and is particularly informative when less than 15% of subjects are missing in each arm.

Adenoma↗
PubMed2004

Semi-supervised methods to predict patient survival from gene expression data.

An important goal of DNA microarray research is to develop tools to diagnose cancer more accurately based on the genetic profile of a tumor. There are several existing techniques in the literature for performing this type of diagnosis. Unfortunately, most of these techniques assume that different subtypes of cancer are already known to exist. Their utility is limited when such subtypes have not been previously identified. Although methods for identifying such subtypes exist, these methods do not work well for all datasets. It would be desirable to develop a procedure to find such subtypes that is applicable in a wide variety of circumstances. Even if no information is known about possible subtypes of a certain form of cancer, clinical information about the patients, such as their survival time, is often available. In this study, we develop some procedures that utilize both the gene expression data and the clinical data to identify subtypes of cancer and use this knowledge to diagnose future patients. These procedures were successfully applied to several publicly available datasets. We present diagnostic procedures that accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. This has the potential to be a powerful tool for diagnosing and treating cancer.

Breast Neoplasms↗
PubMed2001

Impact of cancer predisposition and radiosensitivity on the population risk of radiation-induced cancers.

This paper provides a brief overview of the current evidence for cancer predisposition and for an increased sensitivity of individuals carrying such predisposing mutations to cancers induced by ionizing radiations. We also discuss the use of a Mendelian one-locus, two-allele autosomal dominant model for predicting the impact of cancer predisposition and increased radiosensitivity on the risk of radiation-induced cancers in the population and in relatives of affected individuals using breast cancer due to BRCA1 mutations as an example. The main conclusions are the following: (1) The relative risk ratio of the risks of radiation-induced cancer in a heterogeneous population which has subgroups of normal and cancer-predisposed individuals to the risks in a homogeneous population (i.e., one which does not have these subgroups) increases with increasing dose; however, the dose dependence of the RR decreases at higher doses because of the fact that at high doses, the radiation risk to a homogeneous population will already be high. (2) The attributable risk (the proportion of cancers attributable to increased cancer susceptibility and increased radiosensitivity) follows a similar pattern. (3) When the proportion of cancers due to the susceptible genotypes is small (< 10%), as is likely to be the case for breast cancers in non-Ashkenazi Jewish women, the increases in risk ratios and attributable risks are small, and become marked only when there are very large increases in cancer susceptibility (> 1000-fold) and radiosensitivity (> 100-fold) in the susceptible group. (4) When the proportion of cancers due to the susceptible genotypes is appreciable (> or = 10%), as may be the case for breast cancers in Ashkenazi Jewish women, there may be significant increases in the risk ratios and attributable risk for comparatively moderate increases in cancer susceptibility (> 10-fold) and radiosensitivity (> 100-fold) in the susceptible subpopulation. (5) The ratio of the risk of radiation-induced cancer in relatives to that in unrelated individuals in the population increases with the biological relatedness of the relative, being higher for close than for distant relatives; however, even when the mutant BRCA1 gene frequency and the proportion of breast cancers due to these mutations are high, as in Ashkenazi Jewish women, for values of predisposition strength and radiosensitivity differential < 10, the increase in breast cancer risks is only marginal, even for first-degree relatives.

Alleles↗
PubMed2002

Relative survival of patients with subsequent cancer.

BACKGROUND: With the increasing numbers of patients with multiple primary cancers, survival from subsequent cancers is of growing interest. The majority of the analyses on the subject so far have, however, suffered from methodological difficulties. METHODS: A new model is now proposed for estimating relative survival of patients with subsequent primary cancer. The model is an extension to that proposed earlier by Estève et al. for estimating relative survival using individual patient data. The model is illustrated with real data on patients with one or two primary breast cancers and used in comparing the excess hazards between first and subsequent breast cancer. RESULTS: For patients with multiple cancers, the traditional analysis of relative survival can be made cancer-specific. The excess hazards are different between the first and subsequent breast cancer: The excess hazard of the subsequent breast cancer tends to decrease with increasing age when compared to the corresponding hazard of the first breast cancer. CONCLUSIONS: Relative survival of patients with subsequent cancer can be modelled facilitating studies on different hypothesis on the excess hazards of a first and subsequent cancer.

Adult↗