Markov models of breast tumor progression: some age-specific results

Estimating Age-Specific Mean Sojourn Time of Breast Cancer and Sensitivity of Mammographic Screening by Breast Density among Korean Women

PURPOSE

High breast cancer incidence and dense breast prevalence among women in forties are specific to Asian. This study examined the natural history of breast cancer among Korean women.

MATERIALS AND METHODS

We applied a three-state Markov model (i.e., healthy, preclinical, and clinical state) to fit the natural history of breast cancer to data in the Korean National Cancer Screening Program. Breast cancer was ascertained by linkage to the Korean Central Cancer Registry. Disease-progression rates (i.e., transition rates between three states), mean sojourn time (MST) and mammographic sensitivity were estimated across 10-year age groups and levels of breast density determined by the Breast Imaging, Reporting and Data System.

RESULTS

Overall prevalence of dense breast was 53.9%. Transition rate from healthy to preclinical state, indicating the preclinical incidence of breast cancer, was higher among women in forties (0.0019; 95% confidence interval [CI], 0.0017 to 0.0021) and fifties (0.0020; 95% CI, 0.0017 to 0.0022), than women in sixties (0.0014; 95% CI, 0.0012 to 0.0017). The MSTs, in which the tumor is asymptomatic but detectable by screening, were also fastest among younger age groups, estimated as 1.98 years (95% CI, 1.67 to 2.33), 2.49 years (95% CI, 1.92 to 3.22), and 3.07 years (95% CI, 2.11 to 4.46) for women in forties, fifties, and sixties, respectively. Having dense breasts increased the likelihood of the preclinical cancer risk (1.96 to 2.35 times) and decreased the duration of MST (1.53 to 2.02 times).

CONCLUSION

This study estimated Korean-specific natural history parameters of breast cancer that would be utilized for establishing optimal screening strategies in countries with higher dense breast prevalence.

Multistate models for the natural history of cancer progression

BACKGROUND

Multistate models can be effectively used to characterise the natural history of cancer. Inference from such models has previously been useful for setting screening policies.

METHODS

We introduce the basic elements of multistate models and the challenges of applying these models to cancer data. Through simulation studies, we examine (1) the impact of assuming time-homogeneous Markov transition intensities when the intensities depend on the time since entry to the current state (i.e., the process is time-inhomogenous semi-Markov) and (2) the effect on precancer risk estimation when observation times depend on an unmodelled intermediate disease state.

RESULTS

In the settings we examined, we found that misspecifying a time-inhomogenous semi-Markov process as a time-homogeneous Markov process resulted in biased estimates of the mean sojourn times. When screen-detection of the intermediate disease leads to more frequent future screening assessments, there was minimal bias induced compared to when screen-detection of the intermediate disease leads to less frequent screening.

CONCLUSIONS

Multistate models are useful for estimating parameters governing the process dynamics in cancer such as transition rates, sojourn time distributions, and absolute and relative risks. As with most statistical models, to avoid incorrect inference, care should be given to use the appropriate specifications and assumptions.

Mammography Screening and Research Evidence: The Swedish Contribution

Sweden has made a contribution to mammography screening research considerably disproportionate to its size. With 0.13% of the world's population, Sweden contributed almost half of the randomized trial evidence on mammography screening. In addition to the fundamental efficacy of screening in preventing deaths from breast cancer, Swedish researchers have produced a considerable volume of research evaluating mammography screening in the routine healthcare services setting and illuminating issues of breast cancer progression and natural history. The basic result that screening prevents deaths from breast cancer has informed policy and led to substantial preservation of life internationally.

Effectiveness of Population-Based Service Screening with Mammography for Women Aged 70-74 Years in Sweden

BACKGROUND

Consensus has been reached on the effectiveness of inviting women aged 50 to 69 years to mammography screening, but for older women, the evidence is scarce. The aim of this study was to estimate the marginal effectiveness of inviting women to mammography screening with an upper age limit of 74 years versus stopping at age 69 using data from the Swedish service-screening program.

METHODS

A cohort design was used to compare the breast cancer mortality in the period 1986 to 2012 between geographic areas and periods where women were invited to screening up to the age of 74 years (study group) with those where women were invited up to age 69 (control group). The study group and the control group were compared using the incidence-based breast cancer mortality rate ratio where only breast cancer deaths in cases diagnosed at 70 to 74 years of age were counted.

RESULTS

After 20 years of follow-up, there were 1,040 and 1,173 breast cancer deaths in the study and the control group, respectively. The breast cancer mortality rate ratio for women invited up to age 74 versus women invited up to age 69 was 0.80 [95% confidence interval (CI): 0.75-0.85] after bias adjustments. The corresponding rate ratio for participating women was 0.73 (95% CI: 0.66-0.81).

CONCLUSIONS

Continuing to screen women up to 74 years of age is effective compared with stopping screening at 69 years.

IMPACT

This large long-term study will add to the knowledge of the effect of mammography screening for women 70 to 74 years.

Tailored optimal posttreatment surveillance for cancer recurrence

A substantial rise in the number of cancer survivors has led to urgent management questions regarding effective posttreatment imaging-based surveillance strategies for cancer recurrence. Current surveillance guidelines provided by a number of professional societies all warn against overly aggressive surveillance, especially for low-risk patients, but all fail to provide more specific directions to accommodate underlying heterogeneity of cancer recurrence. Therefore it is imperative to develop data-driven strategies that can tailor the surveillance schedules to recurrence risk in this era of stricter insurance regulations, provider shortages, and rising costs of health care. Due to a lack of statistical methods for optimizing surveillance scheduling in presence of competing risks, we propose a general approach that uses an intuitive loss function for optimization of early detection of recurrence before death. The proposed strategies can tailor to patient risks of recurrence, in terms of both intensity and amount of surveillance. Using general three-state Markov models, our method is flexible and includes earlier works as special cases. We illustrate our method in both simulation studies and an application to breast cancer surveillance.

Breast cancer screening with mammography in women aged 40-49 years: Impact of length of screening interval on effectiveness of the program

Objectives

To estimate the impact on the effectiveness of Swedish breast cancer screening program in women aged 40–49 years of shortening the screening interval from 21 months to 18 or 12 months.

Methods

The reduction in breast cancer mortality among participants in screening with mammography was previously estimated in the Swedish SCReening of Young women (SCRY) study to be 29%. The expected increased effectiveness with a hypothetical shorter screening interval than the average of 21 months in SCRY was calculated using data about the women who died from breast cancer even though they participated in the SCRY program.

Results

During the study period, 547 women who participated in the index screening round died from breast cancer. Shortening the screening interval to 18 months led to an improved effectiveness of 0.7–3.9% considering interval cancers only and of 1.3–7.6% considering screening-detected cancers only, and for both interval and screening-detected cancers the improvement was 1.9–11.5% when the assumed mortality reduction for the deceased cases varied from 5% to 30%. Shortening the screening interval to 12 months increased the effectiveness by 1.6–9.8% for interval cancers and by 2.9–17.4% for both interval and screening-detected cancers.

Conclusion

Shortening the screening interval for women aged 40–49 years to 18 or 12 months might further reduce the breast cancer mortality rate.

A continuous-time Markov model approach for modeling myelodysplastic syndromes progression from cross-sectional data

The integration of both genomics and clinical data to model disease progression is now possible, thanks to the increasing availability of molecular patients' profiles. This may lead to the definition of novel decision support tools, able to tailor therapeutic interventions on the basis of a "precise" patients' risk stratification, given their health status evolution. However, longitudinal analysis requires long-term data collection and curation, which can be time demanding, expensive and sometimes unfeasible. Here we present a clinical decision support framework that combines the simulation of disease progression from cross-sectional data with a Markov model that exploits continuous-time transition probabilities derived from Cox regression. Trajectories between patients at different disease stages are stochastically built according to a measure of patient similarity, computed with a matrix tri-factorization technique. Such trajectories are seen as realizations drawn from the stochastic process driving the transitions between the disease stages. Eventually, Markov models applied to the resulting longitudinal dataset highlight potentially relevant clinical information. We applied our method to cross-sectional genomic and clinical data from a cohort of Myelodysplastic syndromes (MDS) patients. MDS are heterogeneous clonal hematopoietic disorders whose patients are characterized by different risks of Acute Myeloid Leukemia (AML) development, defined by an international score. We computed patients' trajectories across increasing and subsequent levels of risk of developing AML, and we applied a Cox model to the simulated longitudinal dataset to assess whether genomic characteristics could be associated with a higher or lower probability of disease progression. We then used the learned parameters of such Cox model to calculate the transition probabilities of a continuous-time Markov model that describes the patients' evolution across stages. Our results are in most cases confirmed by previous studies, thus demonstrating that simulated longitudinal data represent a valuable resource to investigate disease progression of MDS patients.

Statistical models of tumour onset and growth for modern breast cancer screening cohorts

Historically, multi-state Markov models have been used to study breast cancer incidence and mammography screening effectiveness. In recent years, more biologically motivated continuous tumour growth models have emerged as alternatives. However, a number of challenges remain for these models to make use of the wealth of information available in large mammography cohort data. In particular, methodology is needed to address random left truncation and individual, asynchronous screening. We present a comprehensive continuous random effects model for the natural history of breast cancer. It models the unobservable processes of tumour onset, tumour growth, screening sensitivity, and symptomatic detection. We show how the unknown model parameter values can be jointly estimated using a prospective cohort with diagnostic data on age and tumour size at diagnosis, and individual screening histories. We also present a microsimulation study calibrated to population breast cancer incidence data, and to data on mode of detection and tumour size. We highlight the importance of adjusting for random left truncation, derive tumour doubling time distributions for screen-detected and interval cancers, and present results concerning the relationship between tumour presence time and age at diagnosis.

The Structure and Parameterization of the Breast Cancer Transition Model Among Chinese Women

OBJECTIVES

Markov model simulation based on the natural history of disease is commonly employed for the comparative research of health interventions. The present study aims to simulate the natural progression of breast cancer and parameterize the initial and transition probabilities of multiple states of breast cancer development among Chinese women.

METHODS

The age-specific incidence, mortality, and clinical stage distribution of breast cancer; and relapse rate of each clinical stage were collected from China's cancer registry yearbooks and clinical epidemiological studies to simulate the process from full health to breast cancer to death among Chinese women aged 30 to 80 through a Markov cohort study. The validity analysis was conducted to evaluate the accuracy of the model estimation.

RESULTS

A Markov transition model with 7 states (no breast cancer, clinical stages 0-IV breast cancer, and death) was constructed for Chinese women. The age-specific incidence, mortality, and clinical stage distribution of breast cancer estimated by the initial and transition probabilities among different Markov states were highly consistent with the registered data and observed studies.

CONCLUSION

A breast cancer transition model for Chinese women has been established with validity. It could be a point of reference for further economic evaluations and breast cancer screening policy formulation.

Towards evidence-based follow-up intervals for breast cancer survivors: Estimates of the preclinical detectable phase of contralateral second breast cancer

PURPOSE

Follow-up schemes in breast cancer survivors are predominantly consensus-based. To determine evidence-based follow-up intervals, estimates of sensitivity of the screening test(s) and duration of the preclinical detectable phase (PCDP) are key. We estimated the sensitivity and the duration of the PCDP of clinical breast examination (CBE) and mammography for the detection of contralateral second breast cancers (CBC) in breast cancer survivors.

METHODS

Women with a CBC (N = 589) diagnosed in Florence between 1980 and 2005 were included. Test sensitivity and the duration of PCDP were estimated using a simple exponential model of PCDP duration. Analyses were stratified by follow-up period (0-5 vs. >5 years after primary diagnosis) and age at CBC diagnosis (<50 vs. ≥50 years).

RESULTS

For CBE, test sensitivity was 55% and the duration of the PCDP 16 months. Mammography sensitivity was 91% and duration of the PCDP 35 months. Stratified analyses showed a higher test sensitivity for CBE for women aged <50 (70% vs. 51%). No difference in the duration of PCDP of CBE was found. For mammography, test sensitivity and the duration of the PCDP were higher for women with longer follow-up and in older women.

CONCLUSIONS

Poor test sensitivity for CBE with a shorter duration of the PCDP compared with mammography were observed. Mammography had high test sensitivity and the potential to detect CBCs early. The estimated duration of the PCDP (35 months) was considerably longer than the recommended follow-up interval (12 months). Future studies are needed to determine whether a longer follow-up interval is appropriate.

Continuous tumour growth models, lead time estimation and length bias in breast cancer screening studies

Comparisons of survival times between screen-detected and symptomatically detected breast cancer cases are subject to lead time and length biases. Whilst the existence of these biases is well known, correction procedures for these are not always clear, as are not the interpretation of these biases. In this paper we derive, based on a recently developed continuous tumour growth model, conditional lead time distributions, using information on each individual's tumour size, screening history and percent mammographic density. We show how these distributions can be used to obtain an individual-based (conditional) procedure for correcting survival comparisons. In stratified analyses, our correction procedure works markedly better than a previously used unconditional lead time correction, based on multi-state Markov modelling. In a study of postmenopausal invasive breast cancer patients, we estimate that, in large (>12 mm) tumours, the multi-state Markov model correction over-corrects five-year survival by 2–3 percentage points. The traditional view of length bias is that tumours being present in a woman's breast for a long time, due to being slow-growing, have a greater chance of being screen-detected. This gives a survival advantage for screening cases which is not due to the earlier detection by screening. We use simulated data to share the new insight that, not only the tumour growth rate but also the symptomatic tumour size will affect the sampling procedure, and thus be a part of the length bias through any link between tumour size and survival. We explain how this has a bearing on how observable breast cancer-specific survival curves should be interpreted. We also propose an approach for correcting survival comparisons for the length bias.

Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up

Background

Overdiagnosis, defined as the detection of a cancer that would not become clinically apparent in a woman’s lifetime without screening, has become a growing concern. Similar underlying risk of breast cancer in the screened and control groups is a prerequisite for unbiased estimates of overdiagnosis, but a contemporary control group is usually not available in organized screening programs.

Methods

We estimated the frequency of overdiagnosis of breast cancer due to screening in women 50–69 years old by using individual screening data from the population-based organized screening program in Stockholm County 1989–2014. A hidden Markov model with four latent states and three observed states was constructed to estimate the natural progression of breast cancer and the test sensitivity. Piecewise transition rates were used to consider the time-varying transition rates. The expected number of detected non-progressive breast cancer cases was calculated.

Results

During the study period, 2,333,153 invitations were sent out; on average, the participation rate in the screening program was 72.7% and the average recall rate was 2.48%. In total, 14,648 invasive breast cancer cases were diagnosed; among the 8305 screen-detected cases, the expected number of non-progressive breast cancer cases was 35.9, which is equivalent to 0.43% (95% confidence interval (CI) 0.10%–2.2%) overdiagnosis. The corresponding estimates for the prevalent and subsequent rounds were 15.6 (0.87%, 95% CI 0.20%–4.3%) and 20.3 (0.31%, 95% CI 0.07%–1.6%), respectively. The likelihood ratio test showed that the non-homogeneous model fitted the data better than an age-homogeneous model (P <0.001).

Conclusions

Our findings suggest that overdiagnosis in the organized biennial mammographic screening for women 50–69 in Stockholm County is a minor phenomenon. The frequency of overdiagnosis in the prevalent screening round was higher than that in subsequent rounds. The non-homogeneous model performed better than the simpler, traditional homogeneous model.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-1082-z) contains supplementary material, which is available to authorized users.

Mathematical modeling of disease dynamics in SDHB- and SDHD-related paraganglioma: Further step in understanding hereditary tumor differences and future therapeutic strategies

Succinate dehydrogenase subunit B and D (SDHB and SDHD) mutations represent the most frequent cause of hereditary pheochromocytoma and paraganglioma (PPGL). Although truncation of the succinate dehydrogenase complex is thought to be the disease causing mechanism in both disorders, SDHB and SDHD patients exihibit different phenotypes. These phenotypic differences are currently unexplained by molecular genetics. The aim of this study is to compare disease dynamics in these two conditions via a Markov chain model based on 4 clinically-defined steady states. Our model corroborates at the population level phenotypic observations in SDHB and SDHD carriers and suggests potential explanations associated with the probabilities of disease maintenance and regression. In SDHB-related syndrome, PPGL maintenance seems to be reduced compared to SDHD (p = 0.04 vs 0.95) due to higher probability of tumor cell regression in SDHB vs SDHD (p = 0.87 vs 0.00). However, when SDHB-tumors give rise to metastases, metastatic cells are able to thrive with decreased probability of regression compared with SDHD counterparts (p = 0.17 vs 0.89). By constrast, almost all SDHD patients develop PGL (mainly head and neck) that persist throughout their lifetime. However, compared to SDHB, maintenance of metastatic lesions seems to be less effective for SDHD (p = 0.83 vs 0.11). These findings align with data suggesting that SDHD-related PPGL require less genetic events for tumor initiation and maintenance compared to those related to SDHB, but fail to initiate biology that promotes metastatic spread and metastatic cell survival in host tissues. By contrast, the higher number of genetic abnormalities required for tumor initiation and maintenance in SDHB PPGL result in a lower penetrance of PGL, but when cells give rise to metastases they are assumed to be better adapted to sustain survival. These proposed differences in disease progression dynamics between SDHB and SDHD diseases provide new cues for future exploration of SDHx PPGL behavior, offering considerations for future specific therapeutic and prevention strategies.

A Two-Step Markov Processes Approach for Parameterization of Cancer State-Transition Models for Low- and Middle-Income Countries

Implementation of organized cancer screening and prevention programs in high-income countries (HICs) has considerably decreased cancer-related incidence and mortality. In low- and middle-income countries (LMICs), screening and early diagnosis programs are generally unavailable, and most cancers are diagnosed in late stages when survival is very low. Analyzing the cost-effectiveness of alternative cancer control programs and estimating resource needs will help prioritize interventions in LMICs. However, mathematical models of natural cancer onset and progression needed to conduct the economic analyses are predominantly based on populations in HICs because the longitudinal data on screening and diagnoses required for parameterization are unavailable in LMICs. Models currently used for LMICs mostly concentrate on directly calculating the shift in distribution of cancer diagnosis as an evaluative measure of screening. We present a mathematical methodology for the parameterization of natural cancer onset and progression, specifically for LMICs that do not have longitudinal data. This full onset and progression model can help conduct comprehensive analyses of cancer control programs, including cancer screening, by considering both the positive impact of screening as well as any adverse consequences, such as over-diagnosis and false-positive results. The methodology has been applied to breast, cervical, and colorectal cancers for 2 regions, under the World Health Organization categorization: Eastern Sub-Saharan Africa (AFRE) and Southeast Asia (SEARB). The cancer models have been incorporated into the Spectrum software and interfaced with country-specific demographic data through the demographic projections (DemProj) module and costing data through the OneHealth tool. These software are open-access and can be used by stakeholders to analyze screening strategies specific to their country of interest.

Breast Cancer Risk From Modifiable and Non-Modifiable Risk Factors among Women in Southeast Asia: A Meta-Analysis

Objective:

The aim of this study was to determine breast cancer risk from modifiable and non-modifiable factors among women in Southeast Asia.

Methods:

This meta-analysis was performed on research articles on breast cancer risk factors in PubMed, ProQuest and EBSCO databases published between 1997 and October 2017. Pooled odds ratios (OR) are calculated using fixed and random-effect models. Data were processed using Review Manager 5.3 (RevMan 5.3).

Results:

From a total of 1,211 articles, 15 studies (1 cohort and 14 case control studies) met the criteria for systematic review. Meta-analysis results showed that of the known modifiable risk factors for breast cancer, parity (nulipara) had the highest odd ratio (OR = 1.85 [95% CI 1.47-2.32]) followed by body mass index (overweight) (OR = 1.61 [95% CI 1.43-1.80]) and use of oral contraceptives (OR = 1.27 [95% CI 1.07-1.51]). Of non-modifiable risk factors, family history of breast cancer had the highest odd ratio (OR = 2.53 [95% CI 1.25-5.09]), followed by age (≥ 40 years) (OR = 1.53 [95% CI 1.34-1.76]) and menopausal status (OR = 1.44 [95% CI 1.26-1.65]).

Conclusion:

This analysis confirmed associations between both modifiable risk factors (parity, body mass index and use of oral contraceptives) and non-modifiable risk factors (family history of breast cancer, age and menopausal status) with breast cancer.

Estimation of overdiagnosis in breast cancer screening using a non-homogeneous multi-state model: A simulation study

Objectives

Overdiagnosis is regarded as a harm of screening. We aimed to develop a non-homogeneous multi-state model to consider the age-specific transition rates for estimation of overdiagnosis, to validate the model by a simulation study where the true frequency of overdiagnosis can be calculated, and to compare our estimate with the cumulative incidence method.

Methods

We constructed a four-state model to describe the natural history of breast cancer. The latent disease progression and the observed states for each individual were simulated in a trial with biennial screening of women aged 51–69 and a control group of the same size without screening. We performed 100 repetitions of the simulation with one million women to evaluate the performance of estimates. A sensitivity analysis with reduced number of controls was performed to imitate the data from the service screening programme.

Results

Based on the 100 repetitions, the mean value of the true frequency of overdiagnosis was 12.5% and the average estimates by the cumulative incidence method and the multi-state model were 12.9% (interquartile range: 2.46%) and 13.4% (interquartile range: 2.16%), respectively. The multi-state model had a greater bias of overdiagnosis than the cumulative incidence method, but the variation in the estimates was smaller. When the number of unscreened group was reduced, the variation of multi-state model estimates increased.

Conclusions

The multi-state model produces a proper estimate of overdiagnosis and the results are comparable with the cumulative incidence method. The multi-state model can be used in the estimation of overdiagnosis, and might be useful for the ongoing service screening programmes.

Effect of Mammography Screening on Mortality by Histological Grade

Background: It has been asserted that mammography screening preferentially benefits those with less aggressive cancers, with lesser or no impact on more rapidly progressing and therefore more life-threatening tumors.Methods: We utilized data from the Swedish Two-County Trial, which randomized 77,080 women ages 40 to 74 to invitation to screening and 55,985 for usual care. We tabulated cancers by histologic grade and then compared mortality from cancers specific to histologic grade between the invited and control group using Poisson regression, with specific interest in the effect on mortality from grade 3 cancers. We used incidence-based mortality from tumors diagnosed within the screening phase of the trial. Finally, we cross-tabulated grade with tumor size and node status, to assess downstaging within tumor grades.Results: There was a major reduction in mortality from grade 3 tumors (RR = 0.65; 95% CI, 0.53-0.80; P < 0.001), and more deaths prevented from grade 3 tumors (n = 95) than grade 1 and 2 tumors combined (n = 48) in the invited group. The proportions of tumors ≥15 mm or larger and node-positive tumors were substantially reduced in the grade 3 tumors in the invited group.Conclusions: The combination of prevention of tumors progressing to grade 3 and detection at smaller sizes and lesser rates of lymph node metastases within grade 3 tumors results in a substantial number of deaths from grade 3 cancers being prevented by invitation to mammographic screening.Impact: Mammography screening prevents deaths from aggressive cancers. Cancer Epidemiol Biomarkers Prev; 27(2); 154-7. ©2017 AACR.

Effect of population breast screening on breast cancer mortality to 2005 in England and Wales: A nested case-control study within a cohort of one million women

Objectives

To evaluate the effectiveness of the NHS breast screening programme (NHSBSP) on breast cancer mortality in England and Wales and to compare findings with a cohort analysis of the same underlying population.

Methods

A nested case-control study within a cohort of 959,738 women in England and Wales aged 49–64 who were eligible for routine NHSBSP screening during 1991–2005. Cases who died from breast cancer in 1991–2005 were matched to controls without breast cancer at the case diagnosis date and alive when the case died. Risk of breast cancer mortality associated with intention to screen (ITS) (7047 cases/28,188 controls) and screening attendance (4707 cases/9413 controls) was examined. Bias was minimised in accordance with currently advocated best practice. Odds ratios (ORs) were calculated using conditional logistic regression. Results were compared with findings from an incidence-based breast cancer mortality cohort analysis.

Results

ITS was associated with a 21% breast cancer mortality reduction (OR = 0.79, 95% confidence interval [CI]: 0.71–0.88, P < 0.001). Attendance ≤5 years before diagnosis was associated with a 47% reduction in breast cancer mortality after self-selection correction (OR = 0.53, 95% CI: 0.46–0.62, P < 0.001). Breast cancer mortality reduction associated with ITS was 21% in both the case-control and cohort analyses, but the impact of attendance was marginally greater in the case-control analysis (36% vs. 32%).

Conclusions

Case-control studies designed and analysed according to current best practice guidelines offer an effective means of evaluating population breast screening.

Bayesian negative-binomial-family-based multistate Markov model for the evaluation of periodic population-based cancer screening considering incomplete information and measurement errors

Population-based cancer screening is often asked but hardly addressed by a question: “How many rounds of screening are required before identifying a cancer of interest staying in the pre-clinical detectable phase (PCDP)?” and also a similar one related to the number of screens required for stopping screening for the low risk group. It can be answered by using longitudinal follow-up data on repeated rounds of screen, namely periodic screen, but such kind of data are rather complicated and fraught with intractable statistical properties including correlated multistate outcomes, unobserved and incomplete (censoring or truncation) information, and imperfect measurements. We therefore developed a negative-binomial-family-based discrete-time stochastic process, taking sensitivity and specificity into account, to accommodate these thorny issues. The estimation of parameters was implemented with Bayesian Markov Chain Monte Carlo method. We demonstrated how to apply this proposed negative-binomial-family-based model to the empirical data similar to the Finnish breast cancer screening program.

The challenge of evaluating annual mammography screening for young women with a family history of breast cancer

It has been recommended that women aged 40–49 years with a significant family history of breast cancer should be offered annual mammography screening ( http://www.nice.org.uk ). An observational study known as FH01 ( http://www.screeningservices.org/btw/fh01/index.asp ) is evaluating this policy in a cohort of 6000 women at moderately increased risk of breast cancer due to family history. The main aims are to assess the likely impact on breast cancer mortality and cost-effectiveness. Measuring these outcomes is challenging in an environment where a randomized trial is not feasible and there is no natural comparison group. In this paper, we present some approaches to estimating effectiveness and the planned analyses. These involve comparison of disease stage and likely consequent breast cancer mortality in the cohort offered screening with that estimated in the absence of screening. The estimation uses observed outcomes in external populations and estimated outcomes for the hypothetical situation where screening had not taken place.

Using Simulation to Model and Validate Invasive Breast Cancer Progression in Women in the Study and Control Groups of the Canadian National Breast Screening Studies I and II

BACKGROUND

Modeling breast cancer progression and the effect of various risk is helpful in deciding when a woman should start and end screening, and how often the screening should be undertaken.

METHODS

We modeled the natural progression of breast cancer using a hidden Markov process, and incorporated the effects of covariates. Patients are women aged 50-59 (older) and 40-49 (younger) years from the Canadian National Breast Screening Studies. We included prevalent cancers, estimated the screening sensitivities and rates of over-diagnosis, and validated the models using simulation.

RESULTS

We found that older women have a higher rate of transition from a healthy to preclinical state and other causes of death but a lower rate of transition from preclinical to clinical state. Reciprocally, younger women have a lower rate of transition from a healthy to preclinical state and other causes of death but a higher rate of transition from a preclinical to clinical state. Different risk factors were significant for the age groups. The mean sojourn times for older and younger women were 2.53 and 2.96 years, respectively. In the study group, the sensitivities of the initial physical examination and mammography for older and younger women were 0.87 and 0.81, respectively, and the sensitivity of the subsequent screens were 0.78 and 0.53, respectively. In the control groups, the sensitivities of the initial physical examination for older and younger women were 0.769 and 0.671, respectively, and the sensitivity of the subsequent physical examinations for the control group aged 50-59 years was 0.37. The upper-bounds for over-diagnosis in older and younger women were 25% and 27%, respectively.

CONCLUSIONS

The present work offers a basis for the better modeling of cancer incidence for a population with the inclusion of prevalent cancers.

Optimal scheduling of post-therapeutic follow-up of patients treated for cancer for early detection of relapses

Post-therapeutic surveillance is one important component of cancer care. However, there still is no evidence-based strategies to schedule patients’ follow-up examinations. Our approach is based on the modeling of the probability of the onset of relapse at an early asymptotic or preclinical stage and its transition to a clinical stage. For that we consider a multistate homogeneous Markov model, which includes the natural history of relapse. The model also handles separately the different types of possible relapses. The optimal schedule is provided by the calendar visit that maximizes a utility function. The methodology has been applied to laryngeal cancer. The different follow-up strategies revealed to be more efficient than those proposed by different scientific societies.

Estimating breast cancer mortality reduction and overdiagnosis due to screening for different strategies in the United Kingdom

Background:

The benefits and harms of population-wide mammography screening have been long debated. This study evaluated the impact of screening frequency and age range on breast cancer mortality reduction and overdiagnosis.

Methods:

We developed a Markov simulation model for the evaluation of mammography screening in a cohort of British women born in 1935–40.

Results:

For triennial screening in women aged 47–73, breast cancer mortality reduction and overdiagnosis was 18.1% (95% confidence interval: 17.3%, 19.0%) and 5.6% (5.1%, 6.1%), of all breast cancer deaths and diagnoses, respectively, from age 40 to 85 years. For annual screening in the same age range, estimates for both outcomes increased considerably to 35.0% (34.2%, 35.7%) and 7.6% (7.1%, 8.1%), respectively. For the age extension of triennial screening from 50–70 to 47–73, we estimated 5 (3, 7) incremental breast cancer deaths avoided and 14 (9, 19) incremental cases overdiagnosed per 10 000 women invited for screening.

Conclusions:

Estimates of mortality reduction and overdiagnosis were highly dependent on screening frequency, age range, and uptake, which may explain differences between some previous estimates obtained from randomised trials and from service screening.

Quantifying the natural history of breast cancer

Background:

Natural history models of breast cancer progression provide an opportunity to evaluate and identify optimal screening scenarios. This paper describes a detailed Markov model characterising breast cancer tumour progression.

Methods:

Breast cancer is modelled by a 13-state continuous-time Markov model. The model differentiates between indolent and aggressive ductal carcinomas in situ tumours, and aggressive tumours of different sizes. We compared such aggressive cancers, that is, which are non-indolent, to those which are non-growing and regressing. Model input parameters and structure were informed by the 1978–1984 Ostergotland county breast screening randomised controlled trial. Overlaid on the natural history model is the effect of screening on diagnosis. Parameters were estimated using Bayesian methods. Markov chain Monte Carlo integration was used to sample the resulting posterior distribution.

Results:

The breast cancer incidence rate in the Ostergotland population was 21 (95% CI: 17–25) per 10 000 woman-years. Accounting for length-biased sampling, an estimated 91% (95% CI: 85–97%) of breast cancers were aggressive. Larger tumours, 21–50 mm, had an average sojourn of 6 years (95% CI: 3–16 years), whereas aggressive ductal carcinomas in situ took around half a month (95% CI: 0–1 month) to progress to the invasive ⩽10 mm state.

Conclusion:

These tumour progression rate estimates may facilitate future work analysing cost-effectiveness and quality-adjusted life years for various screening strategies.

Screening for breast cancer with mammography

BACKGROUND

A variety of estimates of the benefits and harms of mammographic screening for breast cancer have been published and national policies vary.

OBJECTIVES

To assess the effect of screening for breast cancer with mammography on mortality and morbidity.

SEARCH METHODS

We searched PubMed (22 November 2012) and the World Health Organization's International Clinical Trials Registry Platform (22 November 2012).

SELECTION CRITERIA

Randomised trials comparing mammographic screening with no mammographic screening.

DATA COLLECTION AND ANALYSIS

Two authors independently extracted data. Study authors were contacted for additional information.

MAIN RESULTS

Eight eligible trials were identified. We excluded a trial because the randomisation had failed to produce comparable groups.The eligible trials included 600,000 women in the analyses in the age range 39 to 74 years. Three trials with adequate randomisation did not show a statistically significant reduction in breast cancer mortality at 13 years (relative risk (RR) 0.90, 95% confidence interval (CI) 0.79 to 1.02); four trials with suboptimal randomisation showed a significant reduction in breast cancer mortality with an RR of 0.75 (95% CI 0.67 to 0.83). The RR for all seven trials combined was 0.81 (95% CI 0.74 to 0.87). We found that breast cancer mortality was an unreliable outcome that was biased in favour of screening, mainly because of differential misclassification of cause of death. The trials with adequate randomisation did not find an effect of screening on total cancer mortality, including breast cancer, after 10 years (RR 1.02, 95% CI 0.95 to 1.10) or on all-cause mortality after 13 years (RR 0.99, 95% CI 0.95 to 1.03).Total numbers of lumpectomies and mastectomies were significantly larger in the screened groups (RR 1.31, 95% CI 1.22 to 1.42), as were number of mastectomies (RR 1.20, 95% CI 1.08 to 1.32). The use of radiotherapy was similarly increased whereas there was no difference in the use of chemotherapy (data available in only two trials).

AUTHORS' CONCLUSIONS

If we assume that screening reduces breast cancer mortality by 15% and that overdiagnosis and overtreatment is at 30%, it means that for every 2000 women invited for screening throughout 10 years, one will avoid dying of breast cancer and 10 healthy women, who would not have been diagnosed if there had not been screening, will be treated unnecessarily. Furthermore, more than 200 women will experience important psychological distress including anxiety and uncertainty for years because of false positive findings. To help ensure that the women are fully informed before they decide whether or not to attend screening, we have written an evidence-based leaflet for lay people that is available in several languages on www.cochrane.dk. Because of substantial advances in treatment and greater breast cancer awareness since the trials were carried out, it is likely that the absolute effect of screening today is smaller than in the trials. Recent observational studies show more overdiagnosis than in the trials and very little or no reduction in the incidence of advanced cancers with screening.

Parameter estimates for invasive breast cancer progression in the Canadian National Breast Screening Study

Background:

The aim of screening is to detect a cancer in the preclinical state. However, a false-positive or a false-negative test result is a real possibility.

Methods:

We describe invasive breast cancer progression in the Canadian National Breast Screening Study and construct progression models with and without covariates. The effect of risk factors on transition intensities and false-negative probability is investigated. We estimate the transition rates, the sojourn time and sensitivity of diagnostic tests for women aged 40–49 and 50–59.

Results:

Although younger women have a slower transition rate from healthy state to preclinical, their screen-detected tumour becomes evident sooner. Women aged 50–59 have a higher mortality rate compared with younger women. The mean sojourn times for women aged 40–49 and 50–59 are 2.5 years (95% CI: 1.7, 3.8) and 3.0 years (95% CI: 2.1, 4.3), respectively. Sensitivity of diagnostic procedures for older women is estimated to be 0.75 (95% CI: 0.55, 0.88), while women aged 40–49 have a lower sensitivity (0.61, 95% CI: 0.42, 0.77). Age is the only factor that affects the false-negative probability. For women aged 40–49, ‘age at entry', ‘history of breast disease' and ‘families with breast cancer' are found to be significant for some of the transition rates. For the age-group 50–59, ‘age at entry', ‘history of breast disease', ‘menstruation length' and ‘number of live births' are found to affect the transition rates.

Conclusion:

Modelling and estimating the parameters of cancer progression are essential steps towards evaluating the effectiveness of screening policies. The parameters include the transition rates, the preclinical sojourn time, the sensitivity, and the effect of different risk factors on cancer progression.

Modelling the overdiagnosis of breast cancer due to mammography screening in women aged 40 to 49 in the United Kingdom

INTRODUCTION

Overdiagnosis of breast cancer due to mammography screening, defined as the diagnosis of screen-detected cancers that would not have presented clinically in a women's lifetime in the absence of screening, has emerged as a highly contentious issue, as harm caused may question the benefit of mammographic screening. Most studies included women over 50 years old and little information is available for younger women.

METHODS

We estimated the overdiagnosis of breast cancer due to screening in women aged 40 to 49 years using data from a randomised trial of annual mammographic screening starting at age 40 conducted in the UK. A six-state Markov model was constructed to estimate the sensitivity of mammography for invasive and in situ breast cancer and the screen-detectable mean sojourn time for non-progressive in situ, progressive in situ, and invasive breast cancer. Then, a 10-state simulation model of cancer progression, screening, and death, was developed to estimate overdiagnosis attributable to screening.

RESULTS

The sensitivity of mammography for invasive and in situ breast cancers was 90% (95% CI, 72 to 99) and 82% (43 to 99), respectively. The screen-detectable mean sojourn time of preclinical non-progressive and progressive in situ cancers was 1.3 (0.4 to 3.4) and 0.11 (0.05 to 0.19) years, respectively, and 0.8 years (0.6 to 1.2) for preclinical invasive breast cancer. The proportion of screen-detected in situ cancers that were non-progressive was 55% (25 to 77) for the first and 40% (22 to 60) for subsequent screens. In our main analysis, overdiagnosis was estimated as 0.7% of screen-detected cancers. A sensitivity analysis, covering a wide range of alternative scenarios, yielded a range of 0.5% to 2.9%.

CONCLUSION

Although a high proportion of screen-detected in situ cancers were non-progressive, a majority of these would have presented clinically in the absence of screening. The extent of overdiagnosis due to screening in women aged 40 to 49 was small. Results also suggest annual screening is most suitable for women aged 40 to 49 in the United Kingdom due to short cancer sojourn times.

Computational modeling and multilevel cancer control interventions

This chapter presents an overview of computational modeling as a tool for multilevel cancer care and intervention research. Model-based analyses have been conducted at various "beneath the skin" or biological scales as well as at various "above the skin" or socioecological levels of cancer care delivery. We review the basic elements of computational modeling and illustrate its applications in four cancer control intervention areas: tobacco use, colorectal cancer screening, cervical cancer screening, and racial disparities in access to breast cancer care. Most of these models have examined cancer processes and outcomes at only one or two levels. We suggest ways these models can be expanded to consider interactions involving three or more levels. Looking forward, a number of methodological, structural, and communication barriers must be overcome to create useful computational models of multilevel cancer interventions and population health.

Overdiagnosis in the population-based service screening programme with mammography for women aged 40 to 49 years in Sweden

OBJECTIVES

To estimate the level of overdiagnosis of all breast cancers and of invasive breast cancers in women aged 40-49 invited to the subsequent screening rounds in the Swedish service-screening programme 1986-2005.

METHODS

To estimate the level of overdiagnosis in subsequent screening, the rate ratios (RR) of the breast cancer incidence in the study group (women in areas with screening in ages 40-49) and the control group (women in areas with no screening in ages 40-49) were calculated for all breast cancers and for invasive breast cancers. The RR estimates were adjusted for the prescreening difference in incidence between study and control group and for lead time.

RESULTS

The prescreening incidence rate ratio was estimated at 0.92 (95% confidence interval [CI]: 0.88-0.97). The number of breast cancer cases and person-years were 6047 and 3.8 million, and 7790 and 5.2 million, in the study group and control group respectively during the study period. The RR estimate for all cancers was 1.01 (95% CI: 0.94-1.08) when adjusted for prescreening difference and a lead time of 1.2 years. The corresponding estimate for invasive breast cancers was 0.95 (95% CI: 0.88-1.02).

CONCLUSIONS

We found no significant overdiagnosis for women aged 40-49 in the Swedish service screening programme with mammography.

Effects of annual vs triennial mammography interval on breast cancer incidence and mortality in ages 40-49 in Finland

Background:

The aim of this study was to evaluate the effects of mammography screening invitation interval on breast cancer mortality in women aged 40–49 years.

Methods:

Since 1987 in Turku, Finland, women aged 40–49 years and born in even calendar years were invited for mammography screening annually and those born in odd years triennially. The female cohorts born during 1945–1955 were followed for up to 10 years for incident breast cancers and thereafter for an additional 3 years for mortality.

Results:

Among 14 765 women free of breast cancer at age 40, there were 207 incident primary invasive breast cancers diagnosed before the age of 50. Of these, 36 women died of breast cancer. The mean follow-up time for cancer incidence was 9.8 years and for mortality 12.8 years. The incidence of breast cancer was similar in the annual and triennial invitation groups (RR: 0.98, 95% confidence interval (CI): 0.75–1.29). Further, there were no significant differences in overall mortality (RR: 1.20, 95% CI: 0.99–1.46) or in incidence-based breast cancer mortality (RR: 1.14, 95% CI: 0.59–1.27) between the annual and triennial invitation groups.

Conclusions:

There were no differences in the incidence of breast cancer or incidence-based breast cancer mortality between the women who were invited for screening annually or triennially.

Cancer screening in people who have chronic disease: the example of kidney disease

Cancer screening in people with chronic illness has been the subject of considerable debate recently. Despite the increased incidence of cancer and higher risk of cancer deaths in selected populations, such as those with kidney disease, the benefits-to-harms ratio of cancer screening is uncertain and is likely to be different to people without chronic illnesses because of the expected higher competing risk of death from disease other than cancer, and a higher risk of complications associated with the screening, the diagnostic, and the treatment processes. Using kidney disease as an example, the authors reviewed the current evidence for early cancer detection through screening in people with two or more coexistent chronic diseases, discussed the accepted principles underpinning cancer screening, and the applicability of these concepts to individuals with chronic disease. This review suggests that future research that evaluates the screening test accuracy, quality of life of having cancer, and cancer treatment effectiveness, targeting those with chronic illnesses are necessary for the development of an effective, safe, and acceptable cancer screening program among people with two or more chronic diseases.

Breast screening policy: are we heading in the right direction?

There is a large body of evidence supporting 2-yearly screening of women aged 50-69 years. There is good evidence for a mortality reduction from mammographic screening in women aged 40 to 49 years but a 1-year interval is required. The lack of specificity of screening in young women does remain a problem. There is no evidence to suggest that a single screen between the ages of 47 and 50 years within a programme screening at 3-year intervals will reduce mortality; the trials showing a mortality benefit in women in their 40s included multiple screening episodes and shorter screening intervals. There is no randomized, controlled trial evidence to support screening in women aged above 70 years and screening this age group will cause greater harm than in younger women through higher rates of over-diagnosis and consequent over-treatment. The randomized phase of the screening age extension, which at the moment is planned to last only 6 years, should not be immediately followed by general implementation of the policy. Only if and when additional mortality reductions and an acceptable balance between benefit and harms are shown to be achieved by the extra screens should the 2007 Cancer Reform Strategy policy on age extension be implemented. Resources saved by delaying or abandoning the roll-out of the age extension could potentially be redirected towards reducing the current 3 year screening interval to 2 years in women aged 50-69 years. However, reducing the screening interval to 2 years for women aged 50-69 years would require significantly more screening invitations and resources than the proposed age extension.

Screening for breast cancer with mammography

BACKGROUND

A variety of estimates of the benefits and harms of mammographic screening for breast cancer have been published and national policies vary.

OBJECTIVES

To assess the effect of screening for breast cancer with mammography on mortality and morbidity.

SEARCH STRATEGY

We searched PubMed (November 2008).

SELECTION CRITERIA

Randomised trials comparing mammographic screening with no mammographic screening.

DATA COLLECTION AND ANALYSIS

Both authors independently extracted data. Study authors were contacted for additional information.

MAIN RESULTS

Eight eligible trials were identified. We excluded a biased trial and included 600,000 women in the analyses. Three trials with adequate randomisation did not show a significant reduction in breast cancer mortality at 13 years (relative risk (RR) 0.90, 95% confidence interval (CI) 0.79 to 1.02); four trials with suboptimal randomisation showed a significant reduction in breast cancer mortality with an RR of 0.75 (95% CI 0.67 to 0.83). The RR for all seven trials combined was 0.81 (95% CI 0.74 to 0.87). We found that breast cancer mortality was an unreliable outcome that was biased in favour of screening, mainly because of differential misclassification of cause of death. The trials with adequate randomisation did not find an effect of screening on cancer mortality, including breast cancer, after 10 years (RR 1.02, 95% CI 0.95 to 1.10) or on all-cause mortality after 13 years (RR 0.99, 95% CI 0.95 to 1.03).Numbers of lumpectomies and mastectomies were significantly larger in the screened groups (RR 1.31, 95% CI 1.22 to 1.42) for the two adequately randomised trials that measured this outcome; the use of radiotherapy was similarly increased.

AUTHORS' CONCLUSIONS

Screening is likely to reduce breast cancer mortality. As the effect was lowest in the adequately randomised trials, a reasonable estimate is a 15% reduction corresponding to an absolute risk reduction of 0.05%. Screening led to 30% overdiagnosis and overtreatment, or an absolute risk increase of 0.5%. This means that for every 2000 women invited for screening throughout 10 years, one will have her life prolonged and 10 healthy women, who would not have been diagnosed if there had not been screening, will be treated unnecessarily. Furthermore, more than 200 women will experience important psychological distress for many months because of false positive findings. It is thus not clear whether screening does more good than harm. To help ensure that the women are fully informed of both benefits and harms before they decide whether or not to attend screening, we have written an evidence-based leaflet for lay people that is available in several languages on www.cochrane.dk.

Complementary approaches to assessing risk factors for interval breast cancer

OBJECTIVE

To examine risk factors for interval breast cancer among women screened in a population-based mammography program.

METHODS

Risk for interval cancer was assessed in terms of both the incidence per 10,000 negative screens and the proportion of all breast cancers diagnosed among screened women. Interval (N = 557) and screen-detected cancers (N = 1,545) were identified among 208,667 women receiving mammography in Colorado (1994-2001). Logistic regression was used to assess independent effects of multiple factors.

RESULTS

Overall risk of interval cancer was 29.5/10,000 women screened. Incidence was higher in women >50 years (OR: 2.28, 1.86-2.80), with family history (OR: 2.23, 1.85-2.70), with dense breasts (OR: 3.84, 2.76-5.35), and using hormones (OR: 1.54, 1.20-1.97). Hispanics had lower incidence than Whites (OR: 0.52, 0.34-0.81). Interval cancers represented 26% of all cancers diagnosed. This proportion was higher in women <50 (OR: 1.41, 1.09-1.82) and in women with dense breasts (OR: 2.95, 1.94-4.48).

CONCLUSIONS

Incidence of interval cancer increases with age, breast density, hormone use, and family history. Attempts to reduce occurrence of these cancers through more sensitive and/or intensive screening should focus on these subgroups. The disproportionate number of interval cancers associated with young age and dense breasts suggests these cancers result from both rapid growth and difficulties in detection.

Effectiveness of population-based service screening with mammography for women ages 40 to 49 years: evaluation of the Swedish Mammography Screening in Young Women (SCRY) cohort

BACKGROUND

The effectiveness of mammography screening for women ages 40 to 49 years still is questioned, and few studies of the effectiveness of service screening for this age group have been conducted.

METHODS

Breast cancer mortality was compared between women who were invited to service screening at ages 40 to 49 years (study group) and women in the same age group who were not invited during 1986 to 2005 (control group). Together, these women comprise the Mammography Screening of Young Women (SCRY) cohort, which includes all Swedish counties. A prescreening period was defined to facilitate a comparison of mortality in the absence of screening. The outcome measure was refined mortality, ie, breast cancer death for women who were diagnosed during follow-up at ages 40 to 49 years. Relative risks (RRs) with 95% confidence intervals (CIs) were estimated.

RESULTS

There was no significant difference in breast cancer mortality during the prescreening period. During the study period, there were 803 breast cancer deaths in the study group (7.3 million person-years) and 1238 breast cancer deaths in the control group (8.8 million person-years). The average follow-up was 16 years. The estimated RR for women who were invited to screening was 0.74 (95% CI, 0.66-0.83), and the RR for women who attended screening was 0.71 (95% CI, 0.62-0.80).

CONCLUSIONS

In this comprehensive study, mammography screening for women ages 40 to 49 years was efficient for reducing breast cancer mortality.

Validation of a modelling approach for estimating the likely effectiveness of cancer screening using cancer data on prevalence screening and incidence

PURPOSE

This study aims to validate a biostatistical approach to predict the likely effectiveness of screening in reducing advanced disease in the absence of data on incident screen and interval cancers.

METHODS

We derived the predicted relative reduction in advanced stage disease following screening from the expected proportion of advanced disease following screening and the observed proportion of advanced disease detected clinically among the controls. We compared the predicted estimates to those observed in a randomised trial.

RESULTS

Using our method, the predicted estimates of relative reduction in node positive breast cancer following screening were comparable to the observed estimates for the age groups 50-59 and 60-69 in the screening study (predicted 32% vs. observed 40% (p=0.274) and predicted 34% vs. observed 45% (p=0.068), respectively). However, for the age groups 40-49 and 70-74 the predicted values were overestimates of the likely effectiveness of screening compared to the observed values (predicted 38% vs. observed 16% (p=0.014) and predicted 34% vs. observed 0% (p=0.001), respectively).

CONCLUSION

When the number of cancer cases is more than hundred, the method of prediction using only prevalence screen data may be accurate. Where cancers are less common, for example in small populations or young age groups, further data from interval cancers or incidence screens may be necessary.

Multi-state Markov models in cancer screening evaluation: a brief review and case study

This work presents a brief overview of Markov models in cancer screening evaluation and focuses on two specific models. A three-state model was first proposed to estimate jointly the sensitivity of the screening procedure and the average duration in the preclinical phase, i.e. the period when the cancer is asymptomatic but detectable by screening. A five-state model, incorporating lymph node involvement as a prognostic factor, was later proposed combined with a survival analysis to predict the mortality reduction associated with screening. The strengths and limitations of these two models are illustrated using data from French breast cancer service screening programmes. The three-state model is a useful frame but parameter estimates should be interpreted with caution. They are highly correlated and depend heavily on the parametric assumptions of the model. Our results pointed out a serious limitation to the five-state model, due to implicit assumptions which are not always verified. Although it may still be useful, there is a need for more flexible models. Over-diagnosis is an important issue for both models and induces bias in parameter estimates. It can be addressed by adding a non-progressive state, but this may provide an uncertain estimation of over-diagnosis. When the primary goal is to avoid bias, rather than to estimate over-diagnosis, it may be more appropriate to correct for over-diagnosis assuming different levels in a sensitivity analysis. This would be particularly relevant in a perspective of mortality reduction estimation.

Estimation of natural history parameters of breast cancer based on non-randomized organized screening data: subsidiary analysis of effects of inter-screening interval, sensitivity, and attendance rate on reduction of advanced cancer

Estimating the natural history parameters of breast cancer not only elucidates the disease progression but also make contributions to assessing the impact of inter-screening interval, sensitivity, and attendance rate on reducing advanced breast cancer. We applied three-state and five-state Markov models to data on a two-yearly routine mammography screening in Finland between 1988 and 2000. The mean sojourn time (MST) was computed from estimated transition parameters. Computer simulation was implemented to examine the effect of inter-screening interval, sensitivity, and attendance rate on reducing advanced breast cancers. In three-state model, the MST was 2.02 years, and the sensitivity for detecting preclinical breast cancer was 84.83%. In five-state model, the MST was 2.21 years for localized tumor and 0.82 year for non-localized tumor. Annual, biennial, and triennial screening programs can reduce 53, 37, and 28% of advanced cancer. The effectiveness of intensive screening with poor attendance is the same as that of infrequent screening with high attendance rate. We demonstrated how to estimate the natural history parameters using a service screening program and applied these parameters to assess the impact of inter-screening interval, sensitivity, and attendance rate on reducing advanced cancer. The proposed method makes contribution to further cost-effectiveness analysis. However, these findings had better be validated by using a further long-term follow-up data.

Screening for breast cancer with mammography

BACKGROUND

A variety of estimates of the benefits and harms of mammographic screening for breast cancer have been published and national policies vary.

OBJECTIVES

To assess the effect of screening for breast cancer with mammography on mortality and morbidity.

SEARCH STRATEGY

We searched PubMed (November 2008).

SELECTION CRITERIA

Randomised trials comparing mammographic screening with no mammographic screening.

DATA COLLECTION AND ANALYSIS

Both authors independently extracted data. Study authors were contacted for additional information.

MAIN RESULTS

Eight eligible trials were identified. We excluded a biased trial and included 600,000 women in the analyses. Three trials with adequate randomisation did not show a significant reduction in breast cancer mortality at 13 years (relative risk (RR) 0.90, 95% confidence interval (CI) 0.79 to 1.02); four trials with suboptimal randomisation showed a significant reduction in breast cancer mortality with an RR of 0.75 (95% CI 0.67 to 0.83). The RR for all seven trials combined was 0.81 (95% CI 0.74 to 0.87). We found that breast cancer mortality was an unreliable outcome that was biased in favour of screening, mainly because of differential misclassification of cause of death. The trials with adequate randomisation did not find an effect of screening on cancer mortality, including breast cancer, after 10 years (RR 1.02, 95% CI 0.95 to 1.10) or on all-cause mortality after 13 years (RR 0.99, 95% CI 0.95 to 1.03).Numbers of lumpectomies and mastectomies were significantly larger in the screened groups (RR 1.31, 95% CI 1.22 to 1.42) for the two adequately randomised trials that measured this outcome; the use of radiotherapy was similarly increased.

AUTHORS' CONCLUSIONS

Screening is likely to reduce breast cancer mortality. As the effect was lowest in the adequately randomised trials, a reasonable estimate is a 15% reduction corresponding to an absolute risk reduction of 0.05%. Screening led to 30% overdiagnosis and overtreatment, or an absolute risk increase of 0.5%. This means that for every 2000 women invited for screening throughout 10 years, one will have her life prolonged and 10 healthy women, who would not have been diagnosed if there had not been screening, will be treated unnecessarily. Furthermore, more than 200 women will experience important psychological distress for many months because of false positive findings. It is thus not clear whether screening does more good than harm. To help ensure that the women are fully informed of both benefits and harms before they decide whether or not to attend screening, we have written an evidence-based leaflet for lay people that is available in several languages on www.cochrane.dk.

Modeling the transition of lung cancer from early to advanced stage

We present a stochastic parametric model of the natural history of lung cancer that predicts the primary tumor volume at the moment the disease transits from early to advanced stage. Our model also produces estimates for the probability of symptomatic detection as a function of tumor volume and clinical stage. We estimate model parameters by likelihood maximization using data from the Mayo Lung Project (MLP), which was a clinical trial that evaluated screening for lung cancer in the 1970s. Mayo Lung Project cancer cases reported in Stage III or greater, according to the 1979 AJCC staging for lung cancer, were considered advanced stage. Our estimator distinguishes between the cases detected because of clinical symptoms and cases detected by screening. For nonsmall cell lung cancer cases detected in MLP, we estimate that the median primary tumor diameter at the onset of advanced stage disease was 4.1 cm. In addition, we estimate that the rate of patients symptomatically detected with their disease increases as their primary tumor increases in size, and for patients with a primary tumor of a given size, the rate of symptomatic detection is 12.8 times greater among patients with advanced stage disease compared to patients with early stage disease.

Mortality modeling of early detection programs

Consider a group of subjects who are offered an opportunity to receive a sequence of periodic special examinations for the purpose of diagnosing a chronic disease earlier relative to usual care. The mortality for the early detection group is to be compared with a group receiving usual care. Benefit is reflected in a potential reduction in mortality. This article develops a general probability model that can be used to predict cumulative mortality for each of these groups. The elements of the model assume (i) a four-state progressive disease model in which a subject may be in a disease-free state (or a disease state that cannot be detected), preclinical disease state (capable of being diagnosed by a special exam), clinical state (diagnosis by usual care), and a death state; (ii) age-dependent transitions into the states; (iii) age-dependent examination sensitivity; (iv) age-dependent sojourn time in each state; and (v) the distribution of disease stages on diagnosis conditional on modality of detection. The model may be used to (i) compare mortality rates for different screening schedules; (ii) explore potential benefit of subpopulations; and (iii) compare relative reductions in disease-specific mortality due to advances and dissemination of both treatment and early detection screening programs.

Protection of mammography screening against death from breast cancer in women aged 40-64 years

OBJECTIVE

This study assessed the efficacy of community-based screening mammography in protecting against breast cancer death, asking whether age differences in efficacy persisted in the 1990s.

METHODS

In a case-control study with follow-up, odds ratios (OR) were used to estimate the relative mortality rates from invasive breast cancer among women with at least one screening mammogram in the two years prior to a baseline reference date compared to non-screened women, adjusting for potential confounding. The multicenter population-based study included 553 black and white women diagnosed during 1994-1998 who died in the following five years, and 4016 controls without breast cancer.

RESULTS

Efficacy for reducing the rate of breast cancer death within five years after diagnosis was greater at ages 50-64 years (OR = 0.47, 95% confidence interval (CI) 0.35-0.63) than at ages 40-49 (OR = 0.89, 95% CI 0.65-1.23), and greater among postmenopausal (OR = 0.45, 95% CI 0.33-0.62) than premenopausal women (OR = 0.74, 95% CI 0.53-1.04). Estimates of efficacy were conservative, as shown by sensitivity analyses addressing whether cancer was discovered by a screening mammogram, age at which screening was received, the length of the screening observation window, and years of follow-up after diagnosis.

CONCLUSIONS

Despite the persistence of age differences in efficacy of mammography screening, with greater observed benefit for women aged 50-64 years, these findings support current screening recommendations for women 40-64 years old.