Overdiagnosis in breast cancer screening: the importance of length of observation period and lead time

PMCID: PMC3706885This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

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

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited

Estimate of overdiagnosis of breast cancer due to mammography after adjustment for lead time. A service screening study in Italy

INTRODUCTION: Excess of incidence rates is the expected consequence of service screening. The aim of this paper is to estimate the quota attributable to overdiagnosis in the breast cancer screening programmes in Northern and Central Italy. METHODS: All patients with breast cancer diagnosed between 50 and 74 years who were resident in screening areas in the six years before and five years after the start of the screening programme were included. We calculated a corrected-for-lead-time number of observed cases for each calendar year. The number of observed incident cases was reduced by the number of screen-detected cases in that year and incremented by the estimated number of screen-detected cases that would have arisen clinically in that year. RESULTS: In total we included 13,519 and 13,999 breast cancer cases diagnosed in the pre-screening and screening years, respectively. In total, the excess ratio of observed to predicted in situ and invasive cases was 36.2%. After correction for lead time the excess ratio was 4.6% (95% confidence interval 2 to 7%) and for invasive cases only it was 3.2% (95% confidence interval 1 to 6%). CONCLUSION: The remaining excess of cancers after individual correction for lead time was lower than 5%

Integrating Factor Analysis and a Transgenic Mouse Model to Reveal a Peripheral Blood Predictor of Breast Tumors

Abstract Background Transgenic mouse tumor models have the advantage of facilitating controlled in vivo oncogenic perturbations in a common genetic background. This provides an idealized context for generating transcriptome-based diagnostic models while minimizing the inherent noisiness of high-throughput technologies. However, the question remains whether models developed in such a setting are suitable prototypes for useful human diagnostics. We show that latent factor modeling of the peripheral blood transcriptome in a mouse model of breast cancer provides the basis for using computational methods to link a mouse model to a prototype human diagnostic based on a common underlying biological response to the presence of a tumor. Methods We used gene expression data from mouse peripheral blood cell (PBC) samples to identify significantly differentially expressed genes using supervised classification and sparse ANOVA. We employed these transcriptome data as the starting point for developing a breast tumor predictor from human peripheral blood mononuclear cells (PBMCs) by using a factor modeling approach. Results The predictor distinguished breast cancer patients from healthy individuals in a cohort of patients independent from that used to build the factors and train the model with 89% sensitivity, 100% specificity and an area under the curve (AUC) of 0.97 using Youden's J-statistic to objectively select the model's classification threshold. Both permutation testing of the model and evaluating the model strategy by swapping the training and validation sets highlight its stability. Conclusions We describe a human breast tumor predictor based on the gene expression of mouse PBCs. This strategy overcomes many of the limitations of earlier studies by using the model system to reduce noise and identify transcripts associated with the presence of a breast tumor over other potentially confounding factors. Our results serve as a proof-of-concept for using an animal model to develop a blood-based diagnostic, and it establishes an experimental framework for identifying predictors of solid tumors, not only in the context of breast cancer, but also in other types of cancer.</p

Growth of breast cancer recurrences assessed by consecutive MRI

<p>Abstract</p> <p>Background</p> <p>Women with a personal history of breast cancer have a high risk of developing an ipsi- or contralateral recurrence. We aimed to compare the growth rate of primary breast cancer and recurrences in women who had undergone prior breast magnetic resonance imaging (MRI).</p> <p>Methods</p> <p>Three hundred and sixty-two women were diagnosed with breast cancer and had undergone breast MRI at the time of diagnosis in our institution (2005 - 2009). Among them, 37 had at least one prior breast MRI with the lesion being visible but not diagnosed as cancer. A linear regression of tumour volume measured on MRI scans and time data was performed using a generalized logistic model to calculate growth rates. The primary objective was to compare the tumour growth rate of patients with either primary breast cancer (no history of breast cancer) or ipsi- or contralateral recurrences of breast cancer.</p> <p>Results</p> <p>Twenty women had no history of breast cancer and 17 patients were diagnosed as recurrences (7 and 10 were ipsi- and contralateral, respectively). The tumour growth rate was higher in contralateral recurrences than in ipsilateral recurrences (growth rate [10^-3days^-1] 3.56 vs 1.38, p < .001) or primary cancer (3.56 vs 2.09, p = 0.01). Differences in tumour growth were not significant for other patient-, tumour- or treatment-related characteristics.</p> <p>Conclusions</p> <p>These findings suggest that contralateral breast cancer presents accelerated growth compared to ipsilateral recurrences or primary breast events.</p

Effect of population breast screening on breast cancer mortality up to 2005 in England and Wales: an individual-level cohort study.

Background Population breast screening has been implemented in the UK for over 25 years, but the size of benefit attributable to such programmes remains controversial. We have conducted the first individual-based cohort evaluation of population breast screening in the UK, to estimate the impact of the NHS breast screening programme (NHSBSP) on breast cancer mortality.Methods We followed 988 090 women aged 49-64 years in 1991 resident in England and Wales, who because of the staggered implementation of the NHSBSP, included both invited subjects and an uninvited control group. Individual-level breast screening histories were linked to individual-level mortality and breast cancer incidence data from national registers. Risk of death from breast cancer was investigated by incidence-based mortality analyses in relation to intention to screen and first round attendance. Overdiagnosis of breast cancer following a single screening round was also investigated.Results Invitation to NHSBSP screening was associated with a reduction in breast cancer mortality in 1991-2005 of 21% (RR=0.79, 95% CI: 0.73-0.84, P<0·001) after adjustment for age, socioeconomic status and lead-time. Breast cancer deaths among first invitation attenders were 46% lower than among non-attenders (RR=0.54, 95% CI: 0.51-0·57, P<0.001) and 32% lower following adjustment for age, socioeconomic status and self-selection bias (RR=0.68, 95% CI: 0.63-0·73, P<0.001). There was little evidence of overdiagnosis associated with invitation to first screen.Conclusions The results indicate a substantial, statistically significant reduction in breast cancer mortality between 1991 and 2005 associated with NHSBSP activity. This is important in public health terms

Machine learning-based prediction of breast cancer growth rate in-vivo

BackgroundDetermining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen.MethodsA serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort.ResultsSM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours.ConclusionOur Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications

Interleukin-6 trans-signaling is a candidate mechanism to drive progression of human DCCs during clinical latency

Although thousands of breast cancer cells disseminate and home to bone marrow until primary surgery, usually less than a handful will succeed in establishing manifest metastases months to years later. To identify signals that support survival or outgrowth in patients, we profile rare bone marrow-derived disseminated cancer cells (DCCs) long before manifestation of metastasis and identify IL6/PI3K-signaling as candidate pathway for DCC activation. Surprisingly, and similar to mammary epithelial cells, DCCs lack membranous IL6 receptor expression and mechanistic dissection reveals IL6 trans-signaling to regulate a stem-like state of mammary epithelial cells via gp130. Responsiveness to IL6 trans-signals is found to be niche-dependent as bone marrow stromal and endosteal cells down-regulate gp130 in premalignant mammary epithelial cells as opposed to vascular niche cells. PIK3CA activation renders cells independent from IL6 trans-signaling. Consistent with a bottleneck function of microenvironmental DCC control, we find PIK3CA mutations highly associated with late-stage metastatic cells while being extremely rare in early DCCs. Our data suggest that the initial steps of metastasis formation are often not cancer cell-autonomous, but also depend on microenvironmental signals. Metastatic dissemination in breast cancer patients occurs early in malignant transformation, raising questions about how disseminated cancer cells (DCC) progress at distant sites. Here, the authors show that DCCs in bone marrow are activated via IL6-trans-signaling and thereby acquire stemness traits relevant for metastasis formation

Increased risk for clinical onset of myasthenia gravis during the postpartum period

Neurological Motor Disorder

Cardiovascular risk factors and body composition in adults with achondroplasia

PURPOSE: An increased cardiovascular mortality has been reported in achondroplasia. This population-based, case-control study investigated cardiovascular risk factors and body composition in Norwegian adults with achondroplasia. METHODS: We conducted anthropometric, clinical, and laboratory assessments in 49 participants with achondroplasia, of whom 40 completed magnetic resonance imaging (MRI) for body composition analysis. Controls consisted of 98 UK Biobank participants, matched for body mass index (BMI), sex, and age. RESULTS: Participants were well matched for BMI (33.3 versus 32.5 kg/m2) and sex, but achondroplasia participants were younger than controls (mean age 41.1 versus 54.3 years). Individuals with achondroplasia had lower age-adjusted mean blood pressure, total and low-density lipoprotein (LDL) cholesterol, and triglycerides compared with controls, but similar fasting glucose and HbA1c values. Age-adjusted mean visceral fat store was 1.9 versus 5.3 L (difference -2.7, 95% confidence interval [CI] -3.6 to -1.9; P < 0.001), abdominal subcutaneous fat was 6.0 versus 11.2 L (-4.7, 95% CI -5.9 to -3.4; P < 0.001), and liver fat was 2.2 versus 6.9% (-2.8, 95% CI -5.2 to -0.4; P = 0.02). CONCLUSION: Despite a high BMI, the cardiovascular risks appeared similar or lower in achondroplasia compared with controls, indicating that other factors might contribute to the increased mortality observed in this condition