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The interplay between engineering and medical research plays a major role in advancing the healthcare technologies. Novel medical devices have been developed to improve the diagnosis and treatment plans for patients with pathological conditions such as prostate cancer (PCa). In this context, in silico modelling has been a valuable tool as it is complementary to traditional trial-and-error approaches, particularly in the area of nodule identification in soft tissue. The goal of this thesis is to develop a computational framework of detecting and characterizing the presence of PCa, based on instrumented probing. The proposed methodologies involve Finite-Element simulations, inverse analysis and probability-based methods, using models reconstructed from medical imaging and histological data. The proposed methods are later validated using experimental measurements from instrumented probing on ex-vivo prostates. It is expected that the in-silico framework can serve as a complementary tool to the medical devices and to improve the effectiveness of current methods for early PCa diagnosis.