We introduce in this paper substantial enhancements to a previously proposed hybrid multiscale cancer invasion modelling framework to better reflect the biological reality and dynamics of cancer. These model updates contribute to a more accurate representation of cancer dynamics, they provide deeper insights and enhance our predictive capabilities.  Key updates include the integration of porous medium-like diffusion for the evolution of Epithelial-like Cancer Cells and other essential cellular constituents of the system, more realistic modelling of Epithelial-Mesenchymal Transition and Mesenchymal-Epithelial Transition models with the inclusion of Transforming Growth Factor beta within the tumour microenvironment, and the introduction of Compound Poisson Process in the Stochastic Differential Equations that describe the migration behaviour of the Mesenchymal-like Cancer Cells. Another innovative feature of the model is its extension into a multi-organ metastatic framework. This framework connects various organs through a circulatory network, enabling the study of how cancer cells spread to secondary sites.

Understanding the dynamics of cancer cell dormancy and reactivation is crucial for predicting metastatic potential and improving treatment outcomes in cancer research. This study evaluates a cancer growth model incorporating cell death, dormancy reactivation, and proliferation to capture the duration of dormancy and the frequency of reactivation events. By testing various statistical distributions against experimental data on mice, we identified models that effectively represent the asymmetry and variability observed in dormancy durations. Notably, the estimated cancer cell death rate remained consistent across all tested distributions, supporting its biological relevance as a robust parameter for modelling dormancy survival dynamics. Among the distributions, the most suitable ones feature heavy-tailed behaviour and asymmetric skewness aligned with the prolonged and rare dormancy periods expected of cancer cells. Our findings underscore the importance of selecting appropriate statistical models for dormancy, in aiding predictions of cancer cell reactivation events, and potentially informing therapeutic strategies to manage dormancy-driven metastasis.