A stochastic model for evaluating the progression of ductal carcinoma in situ breast cancer using Norwegian breast cancer screening program data

Abstract

Background

Following widespread mammography screening for breast cancer, the incidence of ductal carcinoma in situ (DCIS) has increased sharply. However, the value of detecting DCIS by screening is uncertain as not all DCIS progresses to invasive breast cancer. Knowledge about the sojourn time in the screen-detectable DCIS state and the progression or regression of DCIS to other stages (i.e., the natural history of DCIS) is essential to treat screen-detected DCIS lesions.

Methods

We developed a stochastic model for DCIS natural history, characterized by DCIS states, invasive breast cancer states, and transition probabilities between the states. The model included DCIS lesions in the screen-detectable preclinical state and their progression to clinical DCIS, invasive breast cancer, or regression to a state undetectable by screening. Unlike currently available DCIS Markov models, the proposed model assumed no relationship between the sojourn time and transition probabilities in DCIS states and used age-specific transition probabilities. In the absence of ideal data for DCIS modeling, the Norwegian Breast Cancer Screening Program data, specifically arranged by screening round and mode of detection, was applied to obtain maximum likelihood estimates of DCIS natural history parameters, including transition probabilities and the mean sojourn time in the preclinical screen-detectable DCIS state.

Results

By indirectly specifying a range of the proportion of breast lesions in the preclinical undetectable DCIS state (S_du) that progress through the preclinical screen-detectable DCIS state (S_dp), P_d(t), not going directly to preclinical invasive breast cancer (S_p), plausible sets of DCIS natural history parameters were systematically evaluated. All estimates indicated that the mean sojourn time in S_dp was relatively short (≤3.5 years). For the age group 50–54 years, the best fitting mean sojourn time in S_dp was 3.4–3.5 years, with mammography sensitivity 0.60–0.61 when P_d(t) was 0.31–0.34. When P_d(t) was larger, mean sojourn times in S_dp likely varied by the pathway. In general, assuming higher P_d(t)—that is, a higher proportion of DCIS lesions that progress to from S_dp to S_p—the mean sojourn time became shorter. Regression to no cancer or undetectable state might be possible, but the quantified level of regression was associated with great uncertainties.

Conclusion

While difficult to point to a unique set of DCIS natural history estimates, identifying broader sets of plausible estimates is possible. Estimates reported here provide a comprehensive view of potential progression paths of DCIS while acknowledging the limitations of available data.