Reliability of Residual Tumor Estimation Based on Navigation Log

Abstract

The mass of residual tumors has previously been estimated using time-series records of the position of surgical instruments acquired from neurosurgical navigation systems (navigation log). This method has been shown to be useful for rapid evaluation of residual tumors during resection. However, quantitative analysis of the method's reliability has not been sufficiently reported. The effect of poor log coverage is dominant in previous studies, in that it did not highlight other disturbance factors, such as intraoperative brain shift. We analyzed 25 patients with a high log-acquisition rate that was calculated by dividing the log-available time by the instrument-use time. We estimated the region of resection using the trajectory of surgical instrument that was extracted from the navigation log. We then calculated the residual tumor region and measured its volume as log-estimation residual tumor volume (RTV). We evaluated the correlation between the log-estimation RTV and the RTV in the post-resection magnetic resonance (MR) image. We also evaluated the accuracy of detecting the residual tumor mass using the estimated residual tumor region. The log-estimation RTV and the RTV in the post-resection MR image were significantly correlated (correlation coefficient = 0.960; P <0.001). The presence of patient-wise residual tumor mass was detected with a sensitivity of 81.8% and a specificity of 92.9%. The individual residual tumor mass was detected with a positive predictive value of 72%. Estimation of residual tumor with adequate log coverage appears to be a suitable method with a high reliability. This method can support rapid decision-making during resection.

Mapping Resection Progress by Tool-Tip Tracking during Brain Tumor Surgery for Real-Time Estimation of Residual Tumor

“…Our novel technique functions parallel to standardof-care neuronavigation without disrupting the surgical workflow and utilizes a data stream that is otherwise unused. Building upon prior experiences of our own and others [10,22,23], we have achieved a sub-millimeter spatial resolution and the highest sampling rate reported in the literature [10], offering both spatial and temporal precision. When preconditions such as line-of-sight are maintained, and brain shift is minimal, our results report qualitative and quantitative benefits in estimating residual tumor.…”

“…Interruption of line-of-sight by physical objects and team members can result in False Negative Tracking and, thus, overestimate residual tumor (Figure 1 Panel B). Prior publications [22,23] have reiterated this correlation between line-of-sight and the accuracy of residual tumor estimation.…”

“…Their cohort comprised 25 patients and was limited to WHO Grade II and III gliomas in the frontal lobe. Their study achieved a sensitivity of 81.8%, a specificity of 92.9%, and a positive predictive value of 72% compared to the residual tumor volumes segmented on post-resection MRIs [22]. However, unlike our sub-millimeter resolution models, their resection maps were low-resolution binary images (2 mm 3 voxels), and they excluded regions of estimated residual tumor of smaller than 6 mm 3 .…”

“…Past publications [22,23] restricted their cohorts to low-grade gliomas. Woerdeman et al [25] employed resection progress mapping to study instrument movement rather than estimate residual tumor but were the only other group to expand their cohort to high-grade gliomas, resection of epileptic foci, and skull base meningiomas.…”

“…With respect to temporal performance, Hong et al [23] reported a sampling rate of 1.5 Hz (or 3 points every 2 seconds), and Yamada et al [22] reported a rate of 20 points/second. In our experiments, we achieved a maximum rate of 30 points/second when line-of-sight was maintained.…”