A natural history model of stage progression applied to breast cancer

Abstract

Invasive breast cancer is commonly staged as local, regional or distant disease. We present a stochastic model of the natural history of invasive breast cancer that quantifies (1) the relative rate that the disease transitions from the local, regional to distant stages, (2) the tumour volume at the stage transitions and (3) the impact of symptom-prompted detection on the tumour size and stage of invasive breast cancer in a population not screened by mammography. By symptom-prompted detection, we refer to tumour detection that results when symptoms appear that prompt the patient to seek clinical care. The model assumes exponential tumour growth and volume-dependent hazard functions for the times to symptomatic detection and stage transitions. Maximum likelihood parameter estimates are obtained based on SEER data on the tumour size and stage of invasive breast cancer from patients who were symptomatically detected in the absence of screening mammography. Our results indicate that the rate of symptom-prompted detection is similar to the rate of transition from the local to regional stage and an order of magnitude larger than the rate of transition from the regional to distant stage. We demonstrate that, in the even absence of screening mammography, symptom-prompted detection has a large effect on reducing the occurrence of distant staged disease at initial diagnosis.

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

“…The simulations used in this study were based on a previously published tumour growth model which has been shown to fit observed distributions of tumour size at diagnosis reasonably well [28,29]. One of the main assumptions of the model is that tumours grow exponentially until detection.…”

“…al. [28] and by Abrahamsson and Humphreys [29]. To choose values of the parameters, τ 1 , τ 2 and η, we used data on the tumour size distribution for breast cancers diagnosed during the years 1977-1979 from the Stockholm-Gotland regional quality register.…”

“…al. [28]. The maximum likelihood estimates of the parameters were τ 1 = τ 2 = 1.385 and η = 0.0002566, and these values were used in our simulations.…”

Modeling the transition of lung cancer from early to advanced stage

“…From data on patients who were detected in advanced stage, one can infer that the size at transition must be smaller than the size at detection. The majority of models that aim to infer primary tumor size at stage transition have been applied to breast cancer [11,12,[17][18][19][20][21][22][23][24], but we have found that they can not be directly applied to lung cancer. When modeling the natural history of breast cancer, a common assumption made is that disease stage does not impact the detection of the disease due to clinical symptoms.…”

Modeling Disease Progression With Longitudinal Markers

“…For example, studies of colorectal and lung cancer have described natural history as a series of transformations from normal to mutated epithelium, to malignant clone, to adenoma (Moolgavkar and Knudson, 1981;Luebeck and Moolgavkar, 2002). Studies of lung and breast cancer have described natural history in terms of tumor size and metastatic status, with the risk of metastasis increasing as tumor size increases (Kimmel and Flehinger,1991;Plevritis et al, 2004). Other models in cancer and HIV have taken the concept of natural history to a higher level of abstraction, specifying only that disease progresses from a preclinical or latent state to a clinically apparent or symptomatic state (Louis, Albert and Heghinian, 1978;Walter and Day, 1983;De Gruttola and Lagakos, 1989).…”