Vaassen Femke, Hazelaar Colien, Vaniqui Ana, Gooding Mark, van der Heyden Brent, Canters Richard, van Elmpt Wouter
Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.
Mirada Medical Ltd., Oxford, United Kingdom.
Phys Imaging Radiat Oncol. 2019 Dec 17;13:1-6. doi: 10.1016/j.phro.2019.12.001. eCollection 2020 Jan.
In radiotherapy, automatic organ-at-risk segmentation algorithms allow faster delineation times, but clinically relevant contour evaluation remains challenging. Commonly used measures to assess automatic contours, such as volumetric Dice Similarity Coefficient (DSC) or Hausdorff distance, have shown to be good measures for geometric similarity, but do not always correlate with clinical applicability of the contours, or time needed to adjust them. This study aimed to evaluate the correlation of new and commonly used evaluation measures with time-saving during contouring.
Twenty lung cancer patients were used to compare user-adjustments after atlas-based and deep-learning contouring with manual contouring. The absolute time needed (s) of adjusting the auto-contour compared to manual contouring was recorded, from this relative time-saving (%) was calculated. New evaluation measures (surface DSC and added path length, APL) and conventional evaluation measures (volumetric DSC and Hausdorff distance) were correlated with time-recordings and time-savings, quantified with the Pearson correlation coefficient, R.
The highest correlation (R = 0.87) was found between APL and absolute adaption time. Lower correlations were found for APL with relative time-saving (R = -0.38), for surface DSC with absolute adaption time (R = -0.69) and relative time-saving (R = 0.57). Volumetric DSC and Hausdorff distance also showed lower correlation coefficients for absolute adaptation time (R = -0.32 and 0.64, respectively) and relative time-saving (R = 0.44 and -0.64, respectively).
Surface DSC and APL are better indicators for contour adaptation time and time-saving when using auto-segmentation and provide more clinically relevant and better quantitative measures for automatically-generated contour quality, compared to commonly-used geometry-based measures.
在放射治疗中，自动危及器官分割算法可缩短轮廓勾画时间，但临床相关的轮廓评估仍具有挑战性。常用的评估自动轮廓的方法，如体积骰子相似系数（DSC）或豪斯多夫距离，已被证明是衡量几何相似性的良好指标，但并不总是与轮廓的临床适用性或调整轮廓所需的时间相关。本研究旨在评估新的和常用的评估方法与轮廓勾画过程中节省时间之间的相关性。
选取20例肺癌患者，比较基于图谱和深度学习的轮廓勾画后用户调整与手动轮廓勾画的情况。记录与手动轮廓勾画相比调整自动轮廓所需的绝对时间（秒），并据此计算相对节省时间（%）。将新的评估方法（表面DSC和增加路径长度，APL）和传统评估方法（体积DSC和豪斯多夫距离）与时间记录和节省时间进行相关性分析，用Pearson相关系数R进行量化。
APL与绝对适应时间之间的相关性最高（R = 0.87）。APL与相对节省时间的相关性较低（R = -0.38），表面DSC与绝对适应时间的相关性较低（R = -0.69），与相对节省时间的相关性为（R = 0.57）。体积DSC和豪斯多夫距离在绝对适应时间（分别为R = -0.32和0.64）和相对节省时间（分别为R = 0.44和 -0.64）方面的相关系数也较低。
与常用的基于几何的方法相比，表面DSC和APL在使用自动分割时是轮廓适应时间和节省时间的更好指标，并且为自动生成的轮廓质量提供了更具临床相关性和更好的定量测量方法。
