Mammography screening plays a crucial role in detecting and diagnosing breast cancer, enabling early treatments and thereby improving survival rates. Evaluating the effectiveness of screening involves comparing the survival of patients diagnosed through screening with those diagnosed after the onset of symptoms. However, this analysis is susceptible to biases that may overestimate the benefits of screening. The lead-time bias arises because screening allows for the early diagnosis of breast cancer, resulting in a longer observed survival without a true improvement in the date of death. Two commonly used approaches to solve this bias were applied to real data in order to compare them. The first approach is based on the sojourn time in the preclinical phase, i.e., the period during which the tumor is detectable through screening without causing symptoms. A distribution of this duration can be derived from a multi-state model. The correction proposed by Duffy et al. (2008) subtracts the expected value of this distribution from the observed survival. We propose enhancing the existing correction by considering breast density. The underlying hypothesis is that denser breast tissues reduce screening sensitivity, thereby shortening the duration of the preclinical phase. The second approach, developed by Abrahamsson et al.(2020), is based on a continuous tumor growth model. This model allows volume growth to be estimated up to the patient's age at diagnosis. The spirit of this second approach is to use estimates from a model to prolong tumor growth in patients diagnosed by screening to estimate the time until a symptomatic diagnosis. Although this latter method is more precise in estimating lead time, it remains underutilized due to its novelty, complexity and lack of available software. Standardizing its use would be relevant, especially considering criticisms of the Duffy correction, deemed excessive.