It depends on what you mean by "using" a continuous variable, but technically I think the answer is actually no. By definition the "manifest variables" in an LCA (the observed variables that you theorize are measures the underlying latent construct) have to be categorical. The whole logic of LCA requires it, because the way it tells you what each class looks like is by giving an item response probability ("rho") for each category of each variable, for each class. So it will tell you (e.g.) that "an individual who answered "A" to Variable 1 has a 76% probability of being in Class 2." That whole approach won't work with a continuous manifest variable.
Another post noted that you can use continuous COVARIATES in an LCA model, but that's something different. The covariates aren't actually part of the model itself, but they are used to help predict the model more accurately. In other words, the covariates don't help define the nature of the latent variable itself, they just predict class membership. So even though you can include a continuous variable as a covariate, you aren't treating it as a measure of the latent variable. So if you want to actually use your continuous variable to actually construct the latent variable then you have to group it into some number of categories (although you can use more than 2, that's the beauty of LCA: a 3 or 4 or 5 category variable is fine).