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Electrical impedance acquisition system for surgical margin assessment during robot-assisted laparoscopic prostatectomy

机译:电阻抗采集系统,用于机器人辅助腹腔镜前列腺切除术中的手术余量评估

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摘要

Prostate cancer (PCa) recurrences are often predicted by assessing the status of surgical margins (SM)---positive surgical margins (PSM) increase the chances of biochemical recurrence by 2-4 times which may lead to PCa recurrence. At present, there are no available tools that are being readily used in the operating room to detect PSMs. To address this need, a multi-frequency multi-channel electrical impedance acquisition system with a microendoscopic probe was developed and employed in an ex-vivo study of human prostates. This system measures the tissue bioimpedance over a range of frequencies (1 kHz to 1MHz), and computes a number of Composite Impedance Metrics (CIM). A support vector machine (SVM) classifier based method (trained using CIM data) was used to compute the probability of probed tissue site being cancerous.;The system was used to collect the impedance spectra from 14 excised prostates, which were obtained from men undergoing radical prostatectomy, for a total of 23 cancerous and 53 benign measurements. The data revealed statistically significant (p<0.05) differences in the impedance properties of the benign and tumorous tissues, and among the measurements taken on the apical, base, and lateral surface of the prostate. Further, in the leave-one-patient-out cross validation, a maximum predictive accuracy of 90.79% was achieved by combining high frequency CIM phase data to train the SVM classifier. The observations are consistent with the physiology and morphology of benign and malignant prostate tissue. CIMs were found to be an effective tool in distinguishing benign from cancerous tissues in the ex-vivo study.
机译:通常通过评估手术切缘(SM)的状态来预测前列腺癌(PCa)的复发-阳性手术切缘(PSM)使生化复发的机会增加2-4倍,这可能导致PCa复发。当前,在手术室中没有可用的工具可用于检测PSM。为了满足该需求,开发了具有微内窥镜探头的多频多通道电阻抗采集系统,并将其用于人体前列腺的体外研究。该系统在一定频率范围(1 kHz至1MHz)上测量组织的生物阻抗,并计算许多复合阻抗指标(CIM)。使用基于支持向量机(SVM)分类器的方法(使用CIM数据进行训练)来计算探测到的组织部位癌变的可能性。;该系统用于收集来自14例切除的前列腺的阻抗谱前列腺癌根治术,共进行23次癌变和53次良性测量。数据显示,在良性和肿瘤组织的阻抗特性以及在前列腺的根尖,根部和侧面进行的测量之间,统计学上存在显着差异(p <0.05)。此外,在留一人的交叉验证中,通过组合高频CIM相位数据来训练SVM分类器,可以达到90.79%的最大预测准确性。观察结果与良性和恶性前列腺组织的生理学和形态一致。在离体研究中,发现CIM是区分良性和癌性组织的有效工具。

著录项

  • 作者

    Khan, Shadab.;

  • 作者单位

    Dartmouth College.;

  • 授予单位 Dartmouth College.;
  • 学科 Electrical engineering.;Biomedical engineering.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 147 p.
  • 总页数 147
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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