| Autor: |
Candito A; Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK., Good DW; Edinburgh Urological Cancer Group, Division of Pathology Laboratories, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK.; Department of Urology, NHS Lothian, Western General Hospital, Edinburgh, UK., Palacio-Torralba J; Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK., Hammer S; Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK., Johnson O; Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK., McNeill SA; Edinburgh Urological Cancer Group, Division of Pathology Laboratories, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK.; Department of Urology, NHS Lothian, Western General Hospital, Edinburgh, UK., Reuben RL; Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK., Chen Y; Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK. |
| Abstrakt: |
Detection of tumor nodules is key to early cancer diagnosis. This study investigates the potential of using the mechanical data, acquired from probing the prostate for detecting the existence, and, more importantly, characterizing the size and depth, from the posterior surface, of the prostate cancer (PCa) nodules. A computational approach is developed to quantify the uncertainty of nodule detectability and is based on identifying stiffness anomalies in the profiles of point force measurements across transverse sections of the prostate. The capability of the proposed method was assessed firstly using a 'training' dataset of in silico models including PCa nodules with random size, depth and location, followed by a clinical feasibility study, involving experimental data from 13 ex vivo prostates from patients who had undergone radical prostatatectomy. Promising levels of sensitivity and specificity were obtained for detecting the PCa nodules in a total of 44 prostate sections. This study has shown that the proposed methods could be a useful complementary tool to exisiting diagnostic methods of PCa. The future study will involve implementing the proposed measurement and detection strategies in vivo , with the help of a miniturized medical device. |
