Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK.
Edinburgh Urological Cancer Group, Division of Pathology Laboratories, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK.
Comput Methods Biomech Biomed Engin. 2023 Mar;26(4):383-398. doi: 10.1080/10255842.2022.2065200. Epub 2022 Apr 21.
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 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 , with the help of a miniturized medical device.
肿瘤结节的检测是癌症早期诊断的关键。本研究旨在探讨利用机械数据探测前列腺是否存在肿瘤，以及更重要的是，从前列腺的后表面探测肿瘤的大小和深度，从而检测前列腺癌（PCa）结节。本研究提出了一种计算方法来量化结节可探测性的不确定性，该方法基于识别点力测量在前列腺横截面上的轮廓中的刚度异常。首先，该方法使用包含随机大小、深度和位置的 PCa 结节的“训练”数据集来评估其性能，然后通过涉及来自接受根治性前列腺切除术的 13 名患者的实验数据的临床可行性研究来评估其性能。该方法在总共 44 个前列腺切片中检测 PCa 结节的灵敏度和特异性都达到了较高的水平。这项研究表明，所提出的方法可能是 PCa 现有诊断方法的有用补充工具。未来的研究将涉及在微型医疗设备的帮助下，实现所提出的测量和检测策略。
