Department of Urology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY, USA.
Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
Eur Urol Oncol. 2019 Mar;2(2):135-140. doi: 10.1016/j.euo.2018.07.005. Epub 2018 Aug 17.
Magnetic resonance imaging/ultrasound-guided fusion biopsy (FBx) is more accurate at detecting clinically significant prostate cancer than conventional transrectal ultrasound-guided systematic biopsy. However, learning curves for attaining accuracy may limit the generalizability of published outcomes.
To delineate and quantify the learning curve for FBx by assessing the targeted biopsy accuracy and pathological quality of systematic biopsy over time.
DESIGN, SETTING, AND PARTICIPANTS: We carried out a retrospective analysis of 173 consecutive men who underwent Artemis FBx with computer-template systematic sampling between July 2015 and May 2017.
The accuracy of targeted biopsy was determined by calculating the distance between planned and actual core trajectories stored on Artemis. Systematic sampling proficiency was assessed via pathological analysis of fibromuscular tissue in all cores and then comparing pathology elements from individual cores from men in the first and last tertiles. Polynomial linear regression models, change-point analysis, and piecewise linear regression were used to quantify the learning curve.
A significant improvement in targeted biopsy accuracy occurred up to 98 cases (p<0.01). There was a significant decrease in fibromuscular tissue in the systematic biopsy cores up to 84 cases (p<0.01) and an improvement in pathological quality when comparing systematic cores from the first and third tertiles. Use of a different fusion platform may limit the generalizability of our results.
There is a significant learning curve for targeted and systemic biopsy using the Artemis platform. Improvements in accuracy of targeted biopsy and better sampling for systematic biopsy can be achieved with greater experience.
We define the learning curve for magnetic resonance imaging/ultrasound-guided fusion biopsy (FBx) using targeted biopsy accuracy and systematic core sampling quality as measures. Our findings underscore the importance of overcoming learning curves inherent to FBx to minimize patient discomfort and biopsy risk and improve the quality of care for accurate risk stratification, active surveillance, and treatment selection.
磁共振成像/超声引导融合活检（FBx）比传统经直肠超声引导系统活检更能准确检测到有临床意义的前列腺癌。然而，达到准确性的学习曲线可能会限制已发表结果的普遍性。
通过评估 FBx 靶向活检的准确性和系统活检的病理质量随时间的变化，描绘和量化 FBx 的学习曲线。
设计、设置和参与者：我们对 2015 年 7 月至 2017 年 5 月期间接受 Artemis FBx 联合计算机模板系统抽样的 173 例连续男性进行了回顾性分析。
通过计算存储在 Artemis 上的计划和实际核心轨迹之间的距离来确定靶向活检的准确性。通过分析所有核心中的纤维肌肉组织来评估系统抽样的熟练程度，然后比较来自第一和最后三分位数男性的单个核心的病理元素。使用多项式线性回归模型、变化点分析和分段线性回归来量化学习曲线。
靶向活检的准确性在进行到 98 例时显著提高（p<0.01）。系统活检核心中的纤维肌肉组织显著减少（p<0.01），并且比较第一和第三三分位数的系统核心时，病理质量得到改善。使用不同的融合平台可能会限制我们结果的普遍性。
使用 Artemis 平台进行靶向和系统活检存在显著的学习曲线。随着经验的增加，可以提高靶向活检的准确性和系统活检的采样质量。
我们使用靶向活检准确性和系统核心采样质量作为指标来定义磁共振成像/超声引导融合活检（FBx）的学习曲线。我们的发现强调了克服 FBx 固有学习曲线的重要性，以最大程度地减少患者不适和活检风险，并提高准确风险分层、主动监测和治疗选择的护理质量。
Zotero 插件