Estimation of sojourn time in chronic disease screening without data on interval cases

Estimation of the sojourn time on the preclinical detectable period in disease screening or transition rates for the natural history of chronic disease usually rely on interval cases (diagnosed between screens). However, to ascertain such cases might be difficult in developing countries due to incomplete registration systems and difficulties in follow-up. To overcome this problem, we propose three Markov models to estimate parameters without using interval cases. A three-state Markov model, a five-state Markov model related to regional lymph node spread, and a five-state Markov model pertaining to tumor size are applied to data on breast cancer screening in female relatives of breast cancer cases in Taiwan. Results based on a three-state Markov model give mean sojourn time (MST) 1.90 (95% CI: 1.18-4.86) years for this high-risk group. Validation of these models on the basis of data on breast cancer screening in the age groups 50-59 and 60-69 years from the Swedish Two-County Trial shows the estimates from a three-state Markov model that does not use interval cases are very close to those from previous Markov models taking interval cancers into account. For the five-state Markov model, a reparameterized procedure using auxiliary information on clinically detected cancers is performed to estimate relevant parameters. A good fit of internal and external validation demonstrates the feasibility of using these models to estimate parameters that have previously required interval cancers. This method can be applied to other screening data in which there are no data on interval cases.     

A bayesian random-effects markov model for tumor progression in women with a family history of breast cancer

SUMMARY: It makes intuitive sense to model the natural history of breast cancer, tumor progression from preclinical screen-detectable phase (PCDP) to clinical disease, as a multistate process, but the multilevel structure of the available data, which generally comes from cluster (family)-based service screening programs, makes the required parameter estimation intractable because there is a correlation between screening rounds in the same individual, and between subjects within clusters (families). There is also residual heterogeneity after adjusting for covariates. We therefore proposed a Bayesian hierarchical multistate Markov model with fixed and random effects and applied it to data from a high-risk group (women with a family history of breast cancer) participating in a family-based screening program for breast cancer. A total of 4867 women attended (representing 4464 families) and by the end of 2002, a total of 130 breast cancer cases were identified. Parameter estimation was carried out using Markov chain Monte Carlo (MCMC) simulation and the Bayesian software package WinBUGS. Models with and without random effects were considered. Our preferred model included a random-effect term for the transition rate from preclinical to clinical disease (sigma(2)(2f)), which was estimated to be 0.50 (95% credible interval = 0.22-1.49). Failure to account for this random effect was shown to lead to bias. The incorporation of covariates into multistate models with random effect not only reduced residual heterogeneity but also improved the convergence of stationary distribution. Our proposed Bayesian hierarchical multistate model is a valuable tool for estimating the rate of transitions between disease states in the natural history of breast cancer (and possibly other conditions). Unlike existing models, it can cope with the correlation structure of multilevel screening data, covariates, and residual (unexplained) variation.     

Budget Impact Analysis of Switching to Digital Mammography in a Population-Based Breast Cancer Screening Program: A Discrete Event Simulation Model

Objective

To assess the budgetary impact of switching from screen-film mammography to full-field digital mammography in a population-based breast cancer screening program.

Methods

A discrete-event simulation model was built to reproduce the breast cancer screening process (biennial mammographic screening of women aged 50 to 69 years) combined with the natural history of breast cancer. The simulation started with 100,000 women and, during a 20-year simulation horizon, new women were dynamically entered according to the aging of the Spanish population. Data on screening were obtained from Spanish breast cancer screening programs. Data on the natural history of breast cancer were based on US data adapted to our population. A budget impact analysis comparing digital with screen-film screening mammography was performed in a sample of 2,000 simulation runs. A sensitivity analysis was performed for crucial screening-related parameters. Distinct scenarios for recall and detection rates were compared.

Results

Statistically significant savings were found for overall costs, treatment costs and the costs of additional tests in the long term. The overall cost saving was 1,115,857€ (95%CI from 932,147 to 1,299,567) in the 10^th year and 2,866,124€ (95%CI from 2,492,610 to 3,239,638) in the 20^th year, representing 4.5% and 8.1% of the overall cost associated with screen-film mammography. The sensitivity analysis showed net savings in the long term.

Conclusions

Switching to digital mammography in a population-based breast cancer screening program saves long-term budget expense, in addition to providing technical advantages. Our results were consistent across distinct scenarios representing the different results obtained in European breast cancer screening programs.     

Does extirpation of the primary breast tumor give boost to growth of metastases? Evidence revealed by mathematical modeling

A comprehensive mechanistic model of cancer natural history was utilized to obtain an explicit formula for the distribution of volumes of detectable metastases in a given secondary site at any time post-diagnosis. This model provided an excellent fit to the volumes of n = 31, 20 and 15 bone metastases observed in three breast cancer patients 8 years, 5.5 years and 9 months after primary diagnosis, respectively. The model with optimal parameters allowed us to reconstruct the individual natural history of cancer for the first patient. This gave definitive answers, for the patient in question, to the following three questions of major importance in clinical oncology: (1) How early an event is metastatic dissemination of breast cancer? (2) How long is the metastasis latency time? and (3) Does extirpation of the primary breast tumor accelerate the growth of metastases? Specifically, according to the model applied to the first patient, (1) inception of the first metastasis occurred 29.5 years prior to the primary diagnosis; (2) the expected metastasis latency time was about 79.5 years; and (3) resection of the primary tumor was followed by a 32-fold increase in the rate of metastasis growth. The model and our conclusions were validated by the results for the two other patients.     

Distinguishing effects on tumor multiplicity and growth rate in chemoprevention experiments

In some types of cancer chemoprevention experiments and short-term carcinogenicity bioassays, the data consist of the number of observed tumors per animal and the times at which these tumors were first detected. In such studies, there is interest in distinguishing between treatment effects on the number of tumors induced by a known carcinogen and treatment effects on the tumor growth rate. Since animals may die before all induced tumors reach a detectable size, separation of these effects can be difficult. This paper describes a flexible parametric model for data of this type. Under our model, the tumor detection times are realizations of a delayed Poisson process that is characterized by the age-specific tumor induction rate and a random latency interval between tumor induction and detection. The model accommodates distinct treatment and animal-specific effects on the number of induced tumors (multiplicity) and the time to tumor detection (growth rate). A Gibbs sampler is developed for estimation of the posterior distributions of the parameters. The methods are illustrated through application to data from a breast cancer chemoprevention experiment.     

A Monte Carlo analysis of breast screening randomized trials

PurposeTo analyze breast screening randomized trials with a Monte Carlo simulation tool.MethodsA simulation tool previously developed to simulate breast screening programmes was adapted for that purpose. The history of women participating in the trials was simulated, including a model for survival after local treatment of invasive cancers. Distributions of time gained due to screening detection against symptomatic detection and the overall screening sensitivity were used as inputs. Several randomized controlled trials were simulated. Except for the age range of women involved, all simulations used the same population characteristics and this permitted to analyze their external validity. The relative risks obtained were compared to those quoted for the trials, whose internal validity was addressed by further investigating the reasons of the disagreements observed.ResultsThe Monte Carlo simulations produce results that are in good agreement with most of the randomized trials analyzed, thus indicating their methodological quality and external validity. A reduction of the breast cancer mortality around 20% appears to be a reasonable value according to the results of the trials that are methodologically correct. Discrepancies observed with Canada I and II trials may be attributed to a low mammography quality and some methodological problems. Kopparberg trial appears to show a low methodological quality.ConclusionMonte Carlo simulations are a powerful tool to investigate breast screening controlled randomized trials, helping to establish those whose results are reliable enough to be extrapolated to other populations and to design the trial strategies and, eventually, adapting them during their development.     

Probability models to facilitate a declaration that an exotic insect species has not yet invaded an area

A procedure is presented to facilitate a declaration that an area has not yet been invaded by a specific exotic insect pest following a trapping campaign to detect the pest species. For this we use a probability model to assess null trapping results and also a growth model to help verify that pests were not present at a given time in the past. The probability model is developed to calculate the probability of negative trapping results if in fact there were insects present, and then the hypothesis that insects are present can be rejected. The model depends on knowledge of the efficiency of the traps and also the area of attractiveness of the traps. If an incipient and undetected population does become established, then natural growth should eventually make it apparent. Using a growth model, the rate of increase of an insect population starting from one gravid female insect is calculated. For both the probability model and the growth model, the conclusion that no invaders were present relates to some period in the past, the lag being defined by the time interval during the trapping activity or the time taken for one fertilized female to produce a population detectable by trapping. If no insects are observed after a suitable waiting period, then a conclusion can be drawn that none were present. The methodology is applied to hypothetical insects with discrete or continuous reproduction.     

Immune and nonimmune mechanisms mediate the mental stress-induced tumor growth in a xenograft model of breast cancer

Excess mental stress may harm health, and even accelerate cancer initiation and progression. One fourth of breast cancer patients suffer mental stress including anxiety, sadness, or depression, which negatively affect prognosis and survival. However, the regulatory mechanism is yet to be determined. Herein, we applied unpredictable stress stimuli to the breast tumor-bearing mice to establish a xenograft model of breast cancer suffering mental stress, followed by behavioral tests, tumor growth tracking, immune analysis, miRNA screening, and tumor cell proliferation analysis as well. As a result, increased stress hormone levels in serum, decreased percentage of T and NK cells in both blood and tumor samples and accelerated tumor growth in vivo were observed in the mice exposed to mental stress. Promoted cell proliferation was observed in both primary tumor cells derived from the stressed mice and 4T1 breast cancer cells treated with stress hormone corticosterone. In addition, a subset of miRNAs including miR-326, 346, 493, 595, 615, and 665 were identified through a miRNA screening with downregulation in tumors of the stressed mice. CCND1 was identified as a common target gene of miR-346 and miR-493, the top two most significantly downregulated miRNAs by stress exposure. The stress-miRNA-CCND1 signaling regulation of the tumor cell proliferation was further validated in 4T1 cells treated with corticosterone in vitro. GO terms and KEGG pathways analyses on the target genes of miR-346 and miR-493 revealed their involvement in the regulation of human cancer and neuron system, indicating the importance of non-coding genome in mediating the mental stress-induced cancer regulation. In conclusion, this study not only explored immune and nonimmune mechanisms through which mental stress exposure contributes to tumor growth in breast cancer, but also suggested a new therapeutic strategy for cancer patients suffering mental stress.Subject terms: Breast cancer, Disease model, miRNAs     

Assessment of lead-time bias in estimates of relative survival for breast cancer

Relative survival ratios (RSRs) can be useful for evaluating the impact of changes in cancer care on the prognosis of cancer patients or for comparing the prognosis for different subgroups of patients, but their use is problematic for cancer sites where screening has been introduced due to the potential of lead-time bias. Lead-time is survival time that is added to a patient's survival time because of an earlier diagnosis irrespective of a possibly postponed time of death. In the presence of screening it is difficult to disentangle how much of an observed improvement in survival is real and how much is due to lead-time bias. Even so, RSRs are often presented for breast cancer, a site where screening has led to early diagnosis, with the assumption that the lead-time bias is small. We describe a simulation-based framework for studying the lead-time bias due to mammography screening on RSRs of breast cancer based on a natural history model developed in a Swedish setting. We have performed simulations, using this framework, under different assumptions for screening sensitivity and breast cancer survival with the aim of estimating the lead-time bias. Screening every second year among ages 40–75 was introduced assuming that screening had no effect on survival, except for lead-time bias. Relative survival was estimated both with and without screening to enable quantification of the lead-time bias. Scenarios with low, moderate and high breast cancer survival, and low, moderate and high screening sensitivity were simulated, and the lead-time bias assessed in all scenarios.     

Is the three year breast screening interval too long? Occurrence of interval cancers in NHS breast screening programme's north western region.

OBJECTIVE--To report the detection rate of interval cancers in women screened by the NHS breast screening programme. DESIGN--Detection of interval cancers by computer linkage of records held by the screening centres in the North Western Regional Health Authority with breast cancer registrations at the regional cancer registry. SETTING--North Western Regional Health Authority. SUBJECTS--137,421 women screened between 1 March 1988 and 31 March 1992 who had a negative screening result. RESULTS--297 invasive interval cancers were detected. The rate of detection of interval cancers expressed as a proportion of the underlying incidence was 31% in the first 12 months after screening, 52% between 12 and 24 months, and 82% between 24 and 36 months. CONCLUSION--The incidence of interval cancers in the third year after breast screening approaches that which would have been expected in the absence of screening and suggests that the three year interval between screens is too long.     

When is screening effective in reducing the death rate?

Periodic screening programs for the early detection of chronic diseases such as cancer and heart disease may not always lead to a reduction in the number of deaths from the disease. Some improvement is usually possible with the use of a more sensitive detection test, or by lowering the age for the first screening examination, or by decreasing the period between examinations. However, the extent of the reduction in deaths that is attainable with these changes is limited by the underlying biological behavior of the disease as well as by the rate at which the disease is detected without the screening examination, i.e., by the “natural history” of the disease. The effectiveness of a periodic screening program is derived as a function of this natural history, the period between examinations, the sensitivity of the detection test, the age at the first examination, and the age distribution of the rate of the disease initiation in the screened population. A detailed discussion is given of how these results might be used to estimate the effectiveness of any planned periodic screening program. The analysis of a simple example suggests that for some diseases decreasing the interval between screening examinations may not lead to a significant lowering of the death rate, i.e., there may be a natural lower bound on the screening period.     

Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts

Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.     

Mapping tumor-suppressor genes with multipoint statistics from copy-number-variation data

Array-based comparative genomic hybridization (arrayCGH) is a microarray-based comparative genomic hybridization technique that has been used to compare tumor genomes with normal genomes, thus providing rapid genomic assays of tumor genomes in terms of copy-number variations of those chromosomal segments that have been gained or lost. When properly interpreted, these assays are likely to shed important light on genes and mechanisms involved in the initiation and progression of cancer. Specifically, chromosomal segments, deleted in one or both copies of the diploid genomes of a group of patients with cancer, point to locations of tumor-suppressor genes (TSGs) implicated in the cancer. In this study, we focused on automatic methods for reliable detection of such genes and their locations, and we devised an efficient statistical algorithm to map TSGs, using a novel multipoint statistical score function. The proposed algorithm estimates the location of TSGs by analyzing segmental deletions (hemi- or homozygous) in the genomes of patients with cancer and the spatial relation of the deleted segments to any specific genomic interval. The algorithm assigns, to an interval of consecutive probes, a multipoint score that parsimoniously captures the underlying biology. It also computes a P value for every putative TSG by using concepts from the theory of scan statistics. Furthermore, it can identify smaller sets of predictive probes that can be used as biomarkers for diagnosis and therapeutics. We validated our method using different simulated artificial data sets and one real data set, and we report encouraging results. We discuss how, with suitable modifications to the underlying statistical model, this algorithm can be applied generally to a wider class of problems (e.g., detection of oncogenes).     

Nonparametric estimation of asymptomatic duration from a randomized prospective cancer screening trial

We propose a nonparametric estimation of preclinical duration distribution in cancer based on data from a randomized early detection trial. In cancer screening studies, the preclinical duration of a disease is of great interest for better understanding the natural history of the disease, and for developing optimal screening strategies. To estimate the sojourn time distribution nonparametrically, we first estimate the distribution of the age at onset of preclinical disease nonparametrically using data from the screening arm in a randomized screening trial, and the distribution for the age at onset of clinical disease from the control arm of the randomized screening trial. Finally, by using deconvolution the two estimated distributions lead to a nonparametric estimate of the distribution for the gap time between the onset of preclinical disease and the onset of clinical disease. We illustrate the methodology using data from a randomized breast cancer screening trial.     

Origins and evolution of cell phenotypes in breast tumors

This study presents a stochastic model that correlates genomic instability with tumor formation. The model describes the time- and space-variant volumetric concentrations of cancer cells of various phenotypes in a breast tumor. The cells of epithelial origin in the cancerous breast tissue are classified into four different phenotypes, normal epithelial cells and the grade 1, grade 2 and grade 3 cancer cell types with increasing potential for growth and invasion. Equations governing the time course of volumetric concentrations of cell phenotypes are derived by using the principle of conservation of mass. Cell migration into and from the stroma is taken into account. The transformations between cell phenotypes are due to genetic inheritance and chromosome aberrations. These transformations are assumed to be stochastic functions of the local cell concentration. The simulations of the model for planar geometry replicate the shapes of human breast tumors and capture the time history of tumor growth in animal models. Simulations point to transformation of tumor cell population from heterogeneous compositions to a single phenotype at advanced stages of invasive tumors. Systematic variations of model parameters in the computations indicate the important roles the migration capacity, proliferation rate, and phenotype transition probability play in tumor growth. The model developed provides realistic simulations for standard breast cancer therapies and can be used in the optimization studies of chemotherapy, radiotherapy, hormone therapy and emerging individualized therapies for cancer.     

Real-time tracking of parameters of lung mechanics: emphasis on algorithm tuning

We consider the problem of tracking rapid changes in the viscous and elastic properties of the respiratory system by using mouth flow and transpulmonary pressure data measured during mechanical ventilation. A recursive least-squares algorithm with adjustable compensator is used for online estimation of an R-C model of the breathing mechanics. Specific simulation experiments are presented to provide guidelines to select suitable values for the key variable, which controls the compromise between tracking ability and noise sensitivity. The results obtained confirm the critical role of the optimum tuning in relation to the noise level. Experimental results obtained from data measured on mechanically-ventilated dogs, in which respiratory distress syndrome was intravenously induced by oleic acid, demonstrate that the tuned algorithm is able to track appropriately both the viscous and elastic properties of lung mechanics. Parameter estimates are consistent with those obtained by standard and robust offine algorithms and their time course is in qualitative agreement with known physiopathological behaviour.     

An age dependent stochastic model of periodic screening: Basic properties

An age dependent stochastic model for the periodic screening of a progressive chronic disease is developed in this paper by combining results from previous modeling efforts. The basic concepts used are the random variables describing the disease free state and preclinical state sojourn times and the age at screening or observation, as well as generations of individuals defined according to time of entry into the preclinical state. At discrete time points, the model characterizes the density functions for individuals who are healthy, have preclinical disease, or have clinical disease. The joint density functions of age and sojourn times for cases detected by a periodic screening program and for cases which surface clinically between screens are derived as functions of screening interval, false negative rate, and disease natural history.     

On estimating the growth function of tumors

The growth rate of a cancerous tumor as a function of its age is a subject of intellectual and practical importance, as it influences both the effectiveness of proposed screening programs and the strategy of treatment. Obtaining direct evidence on the growth rate is quite difficult, owing to the ethical necessity to intervene when cancer is confirmed. The reasonable assumption that there is a common growth function of age and that probability of detection of a tumor in a short time period is proportional to its size allow the growth function to be inferred from data on sizes at detection. These results can be generalized to allow for individual variation in the rate of traversal of the common growth function. An estimator for the growth function from data on size at detection is obtained. Simulations indicate that it performs reasonably. Application of this estimator to data on a large series of cases of breast cancer at U.T. M. D. Anderson Hospital indicates that the growth function in the range of sizes seen at detection, can be adequately described by exponential growth, with rather large individual-to-individual variations in growth rate.     

Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma

PurposeWe have developed a new method to track tumor position using fluoroscopic images, and evaluated it using hepatocellular carcinoma case data.MethodsOur method consists of a training stage and a tracking stage. In the training stage, the model data for the positional relationship between the diaphragm and the tumor are calculated using four-dimensional computed tomography (4DCT) data. The diaphragm is detected along a straight line, which was chosen to avoid 4DCT artifact. In the tracking stage, the tumor position on the fluoroscopic images is calculated by applying the model to the diaphragm. Using data from seven liver cases, we evaluated four metrics: diaphragm edge detection error, modeling error, patient setup error, and tumor tracking error. We measured tumor tracking error for the 15 fluoroscopic sequences from the cases and recorded the computation time.ResultsThe mean positional error in diaphragm tracking was 0.57 ± 0.62 mm. The mean positional error in tumor tracking in three-dimensional (3D) space was 0.63 ± 0.30 mm by modeling error, and 0.81–2.37 mm with 1–2 mm setup error. The mean positional error in tumor tracking in the fluoroscopy sequences was 1.30 ± 0.54 mm and the mean computation time was 69.0 ± 4.6 ms and 23.2 ± 1.3 ms per frame for the training and tracking stages, respectively.ConclusionsOur markerless tracking method successfully estimated tumor positions. We believe our results will be useful in increasing treatment accuracy for liver cases.     

Lycorine inhibits breast cancer growth and metastasis via inducing apoptosis and blocking Src/FAK-involved pathway

Breast cancer is the most commonly diagnosed cancer type worldwide among women and more than 90% of patients die from tumor metastasis. Lycorine, a natural alkaloid, has been widely reported possessing potential efficacy against cancer proliferation and metastasis. In our study, the anti-tumor potency on breast cancer was evaluated in vitro and in vivo for the first time. Our results indicated that lycorine inhibited breast cancer cells growth, migration and invasion as well as induced their apoptosis.In in vivo study, lycorine not only suppressed breast tumor growth in xenograft models and inhibited breast tumor metastasis in MDA-MB-231 tail vein model. More importantly, we found lycorine had less toxicity than first-line chemotherapy drug paclitaxel at the same effective dose in vivo. Furthermore, on mechanism, lycorine inhibited tumor cell migration and invasion via blocking the Src/FAK(focal adhesion kinase)-involved pathway. In conclusion, our study implied lycorine was a potential candidate for the treatment of breast cancer by inhibition of tumor growth and metastasis.