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 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.

The Effectiveness of Population Mass Screening to Oral Cancer: A Simulation Study

Background: Mass screening of high-risk populations for oral cancer has proven to be effective in reducing oral cancer mortality. However, the magnitude of the effectiveness of the various screening scenarios has rarely been addressed. Methods: We developed a simulation algorithm for a prospective cohort under various oral cancer screening scenarios. A hypothetical cohort of 8 million participants aged ≥30 years with cigaret smoking and/or betel quid chewing habits was constructed based on parameters extracted from studies on oral cancer screening. The results of a population-based screening program in Taiwan and a randomized controlled trial in India were used to validate the fitness; then, the effectiveness of the model was determined by changing the screening parameters. Results: There was a reduction in the risk of advanced oral cancer by 40% (relative risk [RR] = 0.60, 95% confidence interval [CI]:0.59-0.62) and oral cancer mortality by 29% (RR = 0.71, 95% CI: 0.69-0.73) at the 6-year follow-up in a screening scenario similar to the biennial screening in Taiwan, with a 55.1% attendance rate and 92.6% referral rate. The incremental effect in reducing advanced oral cancer was approximately 5% with a short 1-year screening frequency, and the corresponding reduction in mortality was, on average, 6.5%. The incremental reduction in advanced oral cancer per 10% increase in the compliance rate was 3% to 4%, while only 1% to 2% reduction was noted per 10% increase in the referral rate. The effectiveness of screening in reducing advanced oral cancer was 5% to 6% less when both betel quid chewing and alcohol drinking habits were present. Conclusion: Our computer simulation model demonstrated the effect of screening on the reduction in oral cancer mortality under various scenarios. The results provide screening policymakers with the necessary guidance to implement screening programs to save lives.

A pre-symptomatic incubation model for precision strategies of screening, quarantine, and isolation based on imported COVID-19 cases in Taiwan

Facing the emerging COVID viral variants and the uneven distribution of vaccine worldwide, imported pre-symptomatic COVID-19 cases play a pivotal role in border control strategies. A stochastic disease process and computer simulation experiments with Bayesian underpinning was therefore developed to model pre-symptomatic disease progression during incubation period on which we were based to provide precision strategies for containing the resultant epidemic caused by imported COVID-19 cases. We then applied the proposed model to data on 1051 imported COVID-19 cases among inbound passengers to Taiwan between March 2020 and April 2021. The overall daily rate (per 100,000) of pre-symptomatic COVID-19 cases was estimated as 106 (95% credible interval (CrI): 95-117) in March-June 2020, fell to 37 (95% CrI: 28-47) in July-September 2020 (p < 0.0001), resurged to 141 (95% CrI: 118-164) in October-December 2020 (p < 0.0001), and declined to 90 (95% CrI: 73-108) in January-April 2021 (p = 0.0004). Given the median dwelling time, over 82% cases would progress from pre-symptomatic to symptomatic phase in 5-day quarantine. The time required for quarantine given two real-time polymerase chain reaction (RT-PCR) tests depends on the risk of departing countries, testing and quarantine strategies, and whether the passengers have vaccine jabs. Our proposed four-compartment stochastic process and computer simulation experiments design underpinning Bayesian MCMC algorithm facilitated the development of precision strategies for imported COVID-19 cases.

Breast Cancer Screening Recommendations Inclusive of All Women at Average Risk: Update from the ACR and Society of Breast Imaging

Breast cancer remains the most common nonskin cancer, the second leading cause of cancer deaths, and the leading cause of premature death in US women. Mammography screening has been proven effective in reducing breast cancer deaths in women age 40 years and older. A mortality reduction of 40% is possible with regular screening. Treatment advances cannot overcome the disadvantage of being diagnosed with an advanced-stage tumor. The ACR and Society of Breast Imaging recommend annual mammography screening beginning at age 40, which provides the greatest mortality reduction, diagnosis at earlier stage, better surgical options, and more effective chemotherapy. Annual screening results in more screening-detected tumors, tumors of smaller sizes, and fewer interval cancers than longer screening intervals. Screened women in their 40s are more likely to have early-stage disease, negative lymph nodes, and smaller tumors than unscreened women. Delaying screening until age 45 or 50 will result in an unnecessary loss of life to breast cancer and adversely affects minority women in particular. Screening should continue past age 74 years, without an upper age limit unless severe comorbidities limit life expectancy. Benefits of screening should be considered along with the possibilities of recall for additional imaging and benign biopsy and the less tangible risks of anxiety and overdiagnosis. Although recall and biopsy recommendations are higher with more frequent screening, so are life-years gained and breast cancer deaths averted. Women who wish to maximize benefit will choose annual screening starting at age 40 years and will not stop screening prematurely.

Precision Science on Incidence and Progression of Early-Detected Small Breast Invasive Cancers by Mammographic Features

The aim was to evaluate how the inter-screening interval affected the performance of screening by mammographic appearances. This was a Swedish retrospective screening cohort study with information on screening history and mammography features in two periods (1977-1985 and 1996-2010). The pre-clinical incidence and the mean sojourn time (MST) for small breast cancer allowing for sensitivity by mammographic appearances were estimated. The percentage of interval cancer against background incidence (I/E ratio) was used to assess the performance of mammography screening by different inter-screening intervals. The sensitivity-adjusted MSTs (in years) were heterogeneous with mammographic features, being longer for powdery and crushed stone-like calcifications (4.26, (95% CI, 3.50-5.26)) and stellate masses (3.76, (95% CI, 3.15-4.53)) but shorter for circular masses (2.65, (95% CI, 2.06-3.55)) in 1996-2010. The similar trends, albeit longer MSTs, were also noted in 1977-1985. The I/E ratios for the stellate type were 23% and 32% for biennial and triennial screening, respectively. The corresponding figures were 32% and 43% for the circular type and 21% and 29% for powdery and crushed stone-like calcifications, respectively. Mammography-featured progressions of small invasive breast cancer provides a new insight into personalized quality assurance, surveillance, treatment and therapy of early-detected breast cancer.

Sampling-based Markov regression model for multistate disease progression: Applications to population-based cancer screening program

To develop personalized screening and surveillance strategies, the information required to superimpose state-specific covariates into the multi-step progression of disease natural history often relies on the entire population-based screening data, which are costly and infeasible particularly when a new biomarker is proposed. Following Prentice's case-cohort concept, a non-standard case-cohort design from a previous study has been adapted for constructing multistate disease natural history with two-stage sampling. Nonetheless, the use of data only from first screens may invoke length-bias and fail to consider the test sensitivity. Therefore, a new sampling-based Markov regression model and its variants are proposed to accommodate additional subsequent follow-up data on various detection modes to construct state-specific covariate-based multistate disease natural history with accuracy and efficiency. Computer simulation algorithms for determining the required sample size and the sampling fraction of each detection mode were developed either through power function or the capacity of screening program. The former is illustrated with breast cancer screening data from which the effect size and the required sample size regarding the effect of BRCA on multistate outcome of breast cancer were estimated. The latter is applied to population-based colorectal cancer screening data to identify the optimal sampling fraction of each detection mode.

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.

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.

Predicting the effectiveness of the Finnish population-based colorectal cancer screening programme

Objective

Because colorectal cancer (CRC) has a long natural history, estimating the effectiveness of CRC screening programmes requires long-term follow-up. As an alternative, we here demonstrate the use of a temporal multi-state natural history model to predict the effectiveness of CRC screening.

Methods

In the Finnish population-based biennial CRC screening programme using faecal occult blood tests (FOBT), which was conducted in a randomised health services study, we estimated the pre-clinical incidence, the mean sojourn time (MST), and the sensitivity of FOBT using a Markov model to analyse data from 2004 to 2007. These estimates were applied to predict, through simulation, the effects of five rounds of screening on the relative rate of reducing advanced CRC with 6 years of follow-up, and on the reduction in mortality with 10 years of follow-up, in a cohort of 500,000 subjects aged 60 to 69.

Results

For localised and non-localised CRC, respectively, the MST was 2.06 and 1.36 years and the sensitivity estimates were 65.12% and 73.70%. The predicted relative risk of non-localised CRC and death from CRC in the screened compared with the control population was 0.86 (95% CI: 0.79–0.98) and 0.91 (95% CI: 0.85–1.02), respectively.

Conclusion

Based on the preliminary results of the Finnish CRC screening programme, our model predicted a 9% reduction in CRC mortality and a 14% reduction in advanced CRC.

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.

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.

Overdiagnosis due to breast cancer screening: updated estimates of the Helsinki service study in Finland

BACKGROUND

Overdiagnosis is the most important adverse event of breast cancer screening with the estimates ranging from 0% to 40-50% depending on invitational age and methods. We updated the estimates of overdiagnosis in Helsinki service screening study in Finland by comparing the observed and expected cumulative incidence of all breast carcinomas and invasive breast carcinomas.

METHODS

Women aged 50-59 years have been invited to Helsinki service screening since 1986. The incidence of breast carcinoma in the first invited birth cohorts born in 1935-1939 was compared with older, non-invited cohorts. The minimum follow-up time of the invitees after the last screening round was 14 years. Expected cumulative incidence rates were estimated with two alternative approaches.

RESULTS

For both any breast carcinoma and invasive breast carcinoma, the estimates of overdiagnosis varied from 5% (95% CI=-1, 11%) to 7% (95% CI=1, 13%) depending on the approach.

CONCLUSIONS

Our estimates of overdiagnosis are of the same magnitude than other plausible estimates in Europe. Both alternative approaches produced similar estimates for the expected cumulative incidence, which increased the confidence in the estimates of overdiagnosis.

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.

Sensitivity and specificity of mammographic screening as practised in Vermont and Norway

OBJECTIVE

The aim of this study was to examine the sensitivity and specificity of screening mammography as performed in Vermont, USA, and Norway.

METHODS

Incident screening data from 1997 to 2003 for female patients aged 50-69 years from the Vermont Breast Cancer Surveillance System (116 996 subsequent screening examinations) and the Norwegian Breast Cancer Screening Program (360 872 subsequent screening examinations) were compared. Sensitivity and specificity estimates for the initial (based on screening mammogram only) and final (screening mammogram plus any further diagnostic imaging) interpretations were directly adjusted for age using 5-year age intervals for the combined Vermont and Norway population, and computed for 1 and 2 years of follow-up, which ended at the time of the next screening mammogram.

RESULTS

For the 1-year follow-up, sensitivities for initial assessments were 82.0%, 88.2% and 92.5% for 1-, 2- and >2-year screening intervals, respectively, in Vermont (p=0.022). For final assessments, the values were 73.6%, 83.3% and 81.2% (p=0.047), respectively. For Norway, sensitivities for initial assessments were 91.0% and 91.3% (p=0.529) for 2- and >2-year intervals, and 90.7% and 91.3%, respectively, for final assessments (p=0.630). Specificity was lower in Vermont than in Norway for each screening interval and for all screening intervals combined, for both initial (90.6% vs 97.8% for all intervals; p<0.001) and final (98.8% vs 99.5% for all intervals; p<0.001) assessments.

CONCLUSION

Our study showed higher sensitivity and specificity in a biennial screening programme with an independent double reading than in a predominantly annual screening program with a single reading.

ADVANCES IN KNOWLEDGE

This study demonstrates that higher recall rates and lower specificity are not always associated with higher sensitivity of screening mammography. Differences in the screening processes in Norway and Vermont suggest potential areas for improvement in the latter.

Time trends in breast cancer incidence and mortality in a mid-sized northeastern Brazilian city

Background

Breast cancer incidence within an area is usually proportional to the area’s income level. High-income areas have shown the highest incidence rates and since 2003, negative trends. As for mortality, rates are often higher in low-income regions. The purpose of this study was to analyze trends in incidence and mortality in a capital city of a northeastern Brazilian state with an intermediate human development index.

Methods

Incidence data from the Population-Based Cancer Registry of Aracaju and mortality data from the Official State Database for the period 1996–2006 were used. Incidence and mortality crude and age-standardized rates were calculated. Time trends were obtained using the Joinpoint Regression Model.

Results

For the period studied, invasive breast cancer age-standardized incidence rates increased annually with an annual percentage change (APC) of 2.9 (95% CI: 1.2-4.6). Significant increasing trends were observed in groups aged 45–54 years (APC: 3.9, 95% CI: 1.4 to 6.6), and 55–64 years (APC: 5.6, 95% CI: 1.8 to 9.6). Age-standardized mortality rates did not show an increasing trend (APC: 3.0, (95% CI: -2.8 to9.1), except for the group aged 55–64 years (APC: 11.3, 95% CI: 1.1 to 22.4).

Conclusions

In the study community, breast cancer showed increasing incidence among women in the peri- and postmenopausal periods. However, mortality did not present increasing overall trends, except for among the group aged 55–64 years. For better outcomes, screening policies should focus on the peri- and postmenopausal periods of women’s lives to diagnose disease.