Abstract

SummaryComputational modeling has become a successful tool for scientific advances including understanding the behavior of biological and biomedical systems as well as improving clinical practice. In most cases, only general models are used without taking into account patient‐specific features. However, patient specificity has proven to be crucial in guiding clinical practice because of disastrous consequences that can arise should the model be inaccurate. This paper proposes a framework for the computational modeling applied to the example of the male pelvic cavity for the purpose of prostate cancer diagnostics using palpation. The effects of patient specific structural features on palpation response are studied in three selected patients with very different pathophysiological conditions whose pelvic cavities are reconstructed from MRI scans. In particular, the role of intrabladder pressure in the outcome of digital rectal examination is investigated with the objective of providing guidelines to practitioners to enhance the effectiveness of diagnosis. Furthermore, the presence of the pelvic bone in the model is assessed to determine the pathophysiological conditions in which it has to be modeled. The conclusions and suggestions of this work have potential use not only in clinical practice and also for biomechanical modeling where structural patient‐specificity needs to be considered. © 2015 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.

Highlights

  • Cross-disciplinary research between engineering and medicine has resulted in significant advances in clinical diagnostics and treatments for cancer such as magnetic resonance imaging (MRI) and radiotherapy

  • PATIENT SPECIFIC MODELING FOR PROSTATE CANCER DIAGNOSTICS (3 of 13) e02730

  • PATIENT SPECIFIC MODELING FOR PROSTATE CANCER DIAGNOSTICS (5 of 13) e02730 table during examination

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Summary

Introduction

Cross-disciplinary research between engineering and medicine has resulted in significant advances in clinical diagnostics and treatments for cancer such as magnetic resonance imaging (MRI) and radiotherapy. More importantly for this work, computational modeling has become a useful predictive tool, which has been proven to benefit clinical practice in a number of ways, including surgery planning [1], clinician training [2] and preventive medicine [3]. The vast majority of the studies reported in the Literature use general models, often with insufficient patient-specific input, in such scenarios as arterial clamping [7], prostate cancer diagnostics [8] or cornea pinching [9]. It is of great importance to examine the sensitivity of any model to patient-specific parameters, especially when quantitative information is required to make clinical decisions

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