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PubMed2006

The SPECTRUM population model of the impact of screening and treatment on U.S. breast cancer trends from 1975 to 2000: principles and practice of the model methods.

OBJECTIVE: This stochastic simulation model was developed to estimate the impact of screening and treatment diffusion on U.S. breast cancer mortality between 1975 and 2000. MODELING APPROACH: We use an event-driven continuous-time state transition model. Women who are destined to develop breast cancer may be screen detected, present with symptoms, or die of other causes before cancer is diagnosed. At presentation, the cancer has a stage assigned on the basis of mode of detection. Cancers are assumed to be estrogen receptor (ER) positive or negative. Data on screening and treatment diffusion are based on national datasets; other parameters are based on a synthesis of the evidence available in the literature. MODEL METHODS: The model is calibrated to predict incidence and stage distribution (in situ, local, regional, and distant). Other than screening or treatment, background events that affect mortality are not explicitly modeled but are captured in the deviation between model projections of mortality trends and actual trends. We assume that: 1) tumors progress more slowly in older age groups, 2) screen- and clinically detected disease have the same survival conditional on age and stage, 3) women do not die of breast cancer within the "lead time" period, 4) screening benefits are captured by shifts in stage at diagnosis, 4) tamoxifen benefits only ER-positive women, and 5) preclinical sojourn time and dwell times in each of the clinical stages are stochastically independent. MODEL RESULTS: Dissemination of screening and therapeutic advances had a substantial impact on mortality trends. We estimate that, by the year 2000, diffusion of screening lowered mortality by 12.4% and treatment improvements and dissemination lowered mortality by 14.6%. CONCLUSIONS: Models such as this one can be useful to translate clinical trial findings to general populations. This model can also be used inform policy debates about how to best achieve targeted reductions in breast cancer morbidity and mortality.

Adult↗
PubMed1999

Stage distribution at first and repeat examinations in breast cancer screening.

OBJECTIVES: To investigate observed stage distributions at first and repeat screenings. To compare the observed outcomes with expected values based on simulation modelling, varying the assumptions about the natural history of the disease. METHODS: An overview is made of observed data on stage distribution at first and repeat screenings and the difference between those distributions is summarised in a Gini coefficient. Four possible explanations for the observations are considered, two of these are worked out as Miscan simulation models, and the outcomes are compared with observations. RESULTS: Often the reported stage distributions at repeat screenings are not or only slightly more favourable than at first screenings and, in the ones that are more favourable, the difference is relatively small. If, in the Miscan model, it is assumed that there is no correlation between the duration of preclinical breast cancer in consecutive tumour size categories and that there is a strong influence of latent cancers, it is not possible to reproduce the observed outcomes. CONCLUSIONS: The two modelled explanations are not sufficient. Decreasing sensitivity seems an unlikely explanation for the discrepancy in many screening programmes. The possibility that the observations may be explained because false reassurance has been given should be seriously considered and investigated.

Adult↗
PubMed1990

Estimation of radiation risk from screening mammography: recent trends and comparison with expected benefits.

On the basis of recent epidemiologic studies, the National Institutes of Health in 1985 provided a new estimate for radiation risk to the breast that employed a relative risk model and acknowledged greater dependence on age at exposure. Lifetime risks from a single mammogram may be calculated from this estimate and are lower than those based on the previous 1977 National Cancer Institute estimate. Possible years of life expectancy lost from annual mammography beginning at age 40 years may also be calculated and are negligible compared with estimates for years of life expectancy gained from such screening.

Age Factors↗
PubMed1997

Semiparametric regression analysis for recurrent event interval counts.

This paper deals with analysis of data from longitudinal studies where the rate of a recurrent event characterizing morbidity is the primary criterion for treatment evaluation. We consider clinical trials which require patients to visit their clinical center at successive scheduled times as part of follow-up. At each visit, the patient reports the number of events that occurred since the previous visit, or an examination reveals the number of accumulated events, such as skin cancers. The exact occurrence times of the events are unavailable and the actual patient visit times typically vary randomly about the scheduled follow-up times. Each patient's record thus consists of a sequence of clinic visit dates, event counts corresponding to the successive time intervals between clinic visits, and baseline covariates. We propose a semiparametric regression model, extending the fully parametric model of Thall (1988, Biometrics 44, 197-209), to estimate and test for covariate effects on the rate of events over time while also accounting for the possibly time-varying nature of the underlying event rate. Covariate effects enter the model parametrically, while the underlying time-varying event rate is modelled nonparametrically. The method of Severini and Wong (1992, Annals of Statistics 20, 1768-1802) is used to construct asymptotically efficient estimators of the parametric component and to specify their asymptotic distribution. A simulation study and application to a data set are provided.

Biometry↗
PubMed2004

Intravoxel distribution of DWI decay rates reveals C6 glioma invasion in rat brain.

The hypothesis was tested that the intravoxel distribution of water diffusion rates, as measured with a stretched-exponential model of diffusion-weighted imaging (DWI), is a marker of brain tumor invasion. Eight rats underwent intracerebral inoculation of C6 glioma cells. In three rats, cells were labeled with a fluorescent dye for microscopy. One rat was inoculated with a saline solution, and five more rats were imaged without inoculation as controls. Five healthy uninoculated rats were also imaged. DWI was performed 14-15 days after inoculation, with diffusion-weighting factor b = 500 to 6500 sec/mm2, and the resulting signal attenuation was fitted with the stretched-exponential model. The heterogeneity index values were significantly lower (P < 0.05) in the peritumor ROI than in normal gray matter and significantly higher than in normal white matter. The distributed diffusion coefficient values were significantly lower than in normal white matter or normal gray matter. Fluorescence microscopy confirmed the presence of tumors in the peritumor region that could be histologically distinguished from the main tumor mass. There was no change in proton density or T2-weighted images in the peritumor region, making vasogenic edema unlikely as a source of contrast. It is therefore thought that the heterogeneity parameter alpha is a marker of brain tumor invasion.

Animals↗
PubMed1993

Using autoregressive and random walk models to detect trends and shifts in unequally spaced tumour biomarker data.

Continuous time autoregressive (CAR(1)) and random walk models of time series data are provided for detecting non-random shifts and trends of tumour markers in breast cancer patients following resection for cure. The continuous time random walk model with observation error is extended to the case of multiple patient time series. These models can be used to monitor large numbers of patients with time series with few sampling events that are serially correlated and unequally spaced. Further, the methodologies can be used to recommend appropriate testing intervals. A Kalman filter recursive algorithm is used to calculate the likelihood functions arising from the CAR(1) and random walk models and to calculate recursive residuals, which are monitored by Shewhart-cusum schemes.

Antigens, Tumor-Associated, Carbohydrate↗
PubMed1998

[Evaluating the risk of axillary lymph node involvement in inferior breast cancer measuring 3 centimeters. Analysis of a predictive model based on 893 cases].

STUDY AIM: The aim of the study was to assess, by clinical and histological predictive factors, the axillary lymph-node involvement (pN+) in early breast cancers. MATERIALS AND METHODS: Eight hundred ninety-three patients with unilateral invasive breast cancer were studied. The evaluated parameters included clinical size (T), pathological size (pT), histological subtype (ductal infiltrating, according to grading 1, 2, 3, lobular infiltrating and others), age (less than 40, 40 to 60 and above 60). Furthermore, a new parameter, the dosimetric breast size, recently described, was included (Eur J Cancer 1997; 33: 2432-4). RESULTS: The global rate of pN+ was 25.3%, with respectively, pN1: 10%, pN2-3: 8.4% and pN > 3: 6.9%. According to T, the pN+ rates were, respectively, 13.8%, 19.8% and 36.2% in the T0, T1 and T2 < or = 3 cm groups. According to pT, the pN+ rates were, respectively, 11.1%, 17.7%, 23.5%, 30.1% and 36% in the following groups: 0-9.9 mm, 10-14.9 mm, 15-19.9 mm, 20-24.9 mm and 25-29.9 mm. For the ductal infiltrating carcinoma, according to the gradings 1, 2 and 3, we found, respectively, 18.3%, 27.2% and 37.8% of pN+. For the lobular infiltrating carcinoma and the other histological subtypes, the rates were 22.7% and 10%, respectively. For the three age categories cited above the pN+ rates were, respectively, 30.3%, 25.8% and 22.4%. According to breast size we found 30.1% and 24.4% of pN+ respectively for small and medium or large dosimetric breast size. After a multivariate analysis, three factors were significant for pN+ risk: clinical tumor size (P = 0.0001), histological subtype (P = 0.0005) and dosimetric breast size (P = 0.004). With a combination of these three factors, the pN+ rates varied from 5% to 50%. CONCLUSIONS: The authors conclude that both clinical and pathological characteristics of the primary tumor (specified by previous core biopsy) can indicate the risk for axillary node metastases, and allow selection of candidates for limited axilla surgery (sentinel node).

Adult↗
PubMed1993

A model of long-term survival following adjuvant therapy for stage 2 breast cancer.

Following adjuvant therapy for breast cancer, some patients will die of this tumour while the remainder will die of other causes. Deaths from breast cancer tend to follow a lognormal distribution, while deaths from other causes can be approximated by national demographic data. By combining these two survival models, we have generated an age-specific method for estimating the impact of treatment on overall long-term survival. Treatment was designed to operate by one of two mechanisms: an increase in cured fraction, or an increase in median tumour-related survival time among uncured patients. This analysis revealed that, for young and middle-aged patients, an increase in cured fraction has substantially greater long-term clinical impact than an increase in median survival time. Unfortunately, the non-parametric tests traditionally used in prospective clinical trials cannot distinguish between these two mechanisms of action.

Age Factors↗
PubMed2003

Simultaneous gene clustering and subset selection for sample classification via MDL.

MOTIVATION: The microarray technology allows for the simultaneous monitoring of thousands of genes for each sample. The high-dimensional gene expression data can be used to study similarities of gene expression profiles across different samples to form a gene clustering. The clusters may be indicative of genetic pathways. Parallel to gene clustering is the important application of sample classification based on all or selected gene expressions. The gene clustering and sample classification are often undertaken separately, or in a directional manner (one as an aid for the other). However, such separation of these two tasks may occlude informative structure in the data. Here we present an algorithm for the simultaneous clustering of genes and subset selection of gene clusters for sample classification. We develop a new model selection criterion based on Rissanen's MDL (minimum description length) principle. For the first time, an MDL code length is given for both explanatory variables (genes) and response variables (sample class labels). The final output of the proposed algorithm is a sparse and interpretable classification rule based on cluster centroids or the closest genes to the centroids. RESULTS: Our algorithm for simultaneous gene clustering and subset selection for classification is applied to three publicly available data sets. For all three data sets, we obtain sparse and interpretable classification models based on centroids of clusters. At the same time, these models give competitive test error rates as the best reported methods. Compared with classification models based on single gene selections, our rules are stable in the sense that the number of clusters has a small variability and the centroids of the clusters are well correlated (or consistent) across different cross validation samples. We also discuss models where the centroids of clusters are replaced with the genes closest to the centroids. These models show comparable test error rates to models based on single gene selection, but are more sparse as well as more stable. Moreover, we comment on how the inclusion of a classification criterion affects the gene clustering, bringing out class informative structure in the data. AVAILABILITY: The methods presented in this paper have been implemented in the R language. The source code is available from the first author.

Algorithms↗
PubMed2004

Modelling the impact of detecting and treating ductal carcinoma in situ in a breast screening programme.

OBJECTIVES: Screening has substantially increased the detection of carcinoma in situ of the breast (CIS). Opinions vary as to whether this constitutes over-diagnosis or an opportunity to interrupt breast cancer's natural history. In England, incidence of invasive cancer and CIS increased in women of screening age (50-64 years), leading to a subsequent deficit in invasive incidence in women aged 65-69 years immediately beyond the invited age range. We aimed to model underlying incidence of invasive cancer and CIS expected in the absence of screening, and to quantify the likely relative contributions of their early detection to the observed reduction in invasive cancer in women of postscreening age. SETTING: UK NHS breast screening programme in England. METHODS: Poisson regression modelling was used to establish the underlying incidence of invasive and in situ cancers in the absence of screening. We then estimated age- and year-specific excess detection rates attributable to screening. Applying these to population figures we estimated conservatively the relative contributions of early diagnosis of CIS and invasive cancer at 50-64 years of age to the subsequent deficit in invasive cancer in women beyond invitation age (65-69 years), for screening early in the programme and at steady state. RESULTS: Our model estimated a 1.6% annual increase in incidence, giving an estimated deficit of 4.22 invasive cancers per 10,000 women aged 65-69 years in 1996. Carcinoma in situ contributed 13-17% to the deficit, assuming a mean six year lead time and 75-100% progression to invasive cancer. At steady state, with current screening performance and with lead times of 3-4 years (invasive cancer) and 6-9 years (CIS), invasive incidence might be reduced by 5-6 cancers per 10,000 women aged 65-69 years in 2010 (15-20% of underlying incidence), CIS contributing 20-40%. DISCUSSION: The longer lead time associated with CIS attenuates the impact its early detection has on subsequent invasive incidence. At steady state screening, its detection contributes significantly to the deficit in invasive incidence. Our results suggest that, cancer for cancer, there may be just as much benefit in detecting and treating a case of CIS as there is in treating a case of invasive cancer.

Aged↗